text:
\section{Introduction}
Dialog policy, which decides the next action that the dialog agent should take, plays a vital role in a task-oriented dialog system. More recently, dialog policy learning has been widely formulated as a Reinforcement Learning (RL) problem \cite{su2016line,peng2017composite,he2018decoupling,zhao2019rethinking,zhang2019budgeted,takanobu2019guided}, which models users as the interactive environment.
Since RL requires much interaction for training, it is too time-consuming and costly to interact with real users directly. The most common way is first to develop a dialog agent with a user simulator that mimics human behaviors in an offline scenario.

Designing a reliable user simulator, however, is not trivial and often challenging as it is equivalent to building a good dialog agent. With the growing needs for the dialog system to handle more complex tasks, it will be much challenging and laborious to build a fully rule-based user simulator, which requires heavy domain expertise. Data-driven user simulators have been proposed in recent studies \cite{kreyssig2018neural,shi2019build}, but they require a considerable quantity of manually labeled data, most of which regard the simulator as a stationary environment. Furthermore, there is no standard automatic metric for evaluating these user simulators, as it is unclear to define how closely the simulator resembles real user behaviors.

In this paper, we propose Multi-Agent Dialog Policy Learning (MADPL), where the user is regarded as another dialog agent rather than a user simulator. The conversation between the user and the system is modeled as a cooperative interactive process where the system agent and the user agent are trained simultaneously. Two dialog agents interact with each other and collaborate to achieve the goal so that they require no explicit domain expertise, which helps develop a dialog system without the need of a well-built user simulator. Different from existing methods \cite{georgila2014single,papangelis2019collaborative}, our approach is based on \textit{actor-critic} framework \cite{barto1983neuronlike} in order to facilitate pretraining and bootstrap the RL training. Following the paradigm of \textit{centralized training with decentralized execution} (CTDE) \cite{bernstein2002complexity} in multi-agent RL (MARL), the actor selects its action conditioned only on its local state-action history, while the critic is trained with the actions of all agents.

\begin{figure}[!tb]
    \centering
    \includegraphics[width=\linewidth]{pomdp.png}
    \caption{The user has his/her own goal to be accomplished and the system is provided with an interface to access an external database. Both agents can only obtain information from the other side via communication.}
    \label{fig:pomdp}
\end{figure}

It should be noted that the roles of two agents are different though they interact with each other in a cooperative setting. As shown in Fig. \ref{fig:pomdp}, only the user agent knows the user goal, while only the system agent can access the backend database. The user agent should express the requirements completely in an organized way, and the system should respond with useful information accurately and immediately. So it is inappropriate to apply simple self-play RL \cite{silver2017mastering,lewis2017deal} that views two agents as the same agent in this task. To address this issue, the system and the user are viewed as two \textit{asymmetric} agents in MADPL. We introduce \textit{Hybrid Value Network} (HVN) for role-aware reward decomposition. It decomposes the reward into two parts: one is the role-specific reward that focuses on its local target, and the other is the global reward that represents the shared goal.

To evaluate the proposed approach, we conduct our experiments on a multi-domain, multi-intent task-oriented dialog corpus, MultiWOZ \cite{budzianowski2018multiwoz}. The corpus involves high dimensional state and action spaces, multiple decision making in one turn, which makes it more difficult to get a good system policy as well as a good user policy. The experiments demonstrate that MADPL can successfully build a system policy as well as a user policy with the aid of HVN, and two agents can achieve high task success rate in complex tasks by interacting with each other as well as with benchmark policies.

To summarize, our contributions are in three folds:
\begin{itemize}
    \item We apply actor-critic based multi-agent reinforcement learning to learn the task-oriented dialog policy to facilitate pretraining and avoid explicitly building a user simulator.
    \item We propose Hybrid Value Network for reward decomposition to deal with the asymmetric role issue between the system agent and the user agent in the task-oriented dialog.
    \item We conduct in-depth experiments on the multi-domain, multi-intent task-oriented dialog corpus to show the effectiveness, reasonableness and scalability of our algorithm.
\end{itemize}

\section{Related Work}
\subsection{Multi-Agent Reinforcement Learning}
The goal of RL is to discover the optimal strategy $\pi^*(a|s)$ of the Markov Decision Process, which can be extended into the $N$-agent setting, where each agent has its own set of states $\mathcal{S}_i$ and actions $\mathcal{A}_i$. In MARL, the state transition $s=(s_1, \dots, s_N) \rightarrow s'=(s'_1, \dots, s'_N)$ depends on the actions taken by all agents $(a_1, \dots, a_N)$ according to each agent's policy $\pi_i(a_i|s_i)$ where $s_i \in \mathcal{S}_i, a_i \in \mathcal{A}_i$, and similar to single RL, each agent aims to maximize its local total discounted return $R_i = \sum_{t} \gamma^t r_{i,t}$.

Since two or more agents learn simultaneously, the agents continuously change as the training proceeds, therefore the environment is no longer stationary. Many MARL algorithms \cite{lowe2017multi,foerster2018counterfactual,rashid2018qmix} have been proposed to solve challenging problems. Most of them use the CTDE framework to address the non-stationarity of co-adapting agents. It allows the policies to use extra information to ease training, but the learned policies can only use local information (i.e. their own observations) at execution time.

Several studies have demonstrated that applying MARL delivers promising results in NLP tasks these years. While some methods use identical rewards for all agents \cite{das2017learning,kottur2017natural,feng2018learning}, other studies use completely separate rewards \cite{georgila2014single,papangelis2019collaborative}. MADPL integrates two types of rewards by role-aware reward decomposition to train a better dialog policy in task-oriented dialog.

\subsection{User Modeling in Task-Oriented Dialog}
User modeling is essential for training RL-based dialog models, because a large amount of dialog samples are required for RL policy learning, making it impractical to learn with real users directly from the beginning.

There are three main approaches for user modeling.
The first approach is to build a rule-based user simulator. Among these methods, the most popular one is agenda-based simulator \cite{schatzmann2007agenda,shah2018bootstrapping}, which is built on hand-crafted rules with a stack-like agenda based on the user goal.
The second approach is to build a user simulator from the dialog data \cite{keizer2010parameter,el2016sequence,kreyssig2018neural}. Recently,
\citesub{gur2018user} uses a variational hierarchical seq2seq framework to encode user goal and system turns, and then generate the user response.
\citesub{shi2019build} uses two decoders with a copy and attention mechanism to predict a belief span first and then decode user utterance.
The third approach is to use model-based policy optimization that incorporates a differentiable model of the world dynamics and assumptions about the interactions between users and systems \cite{su2018discriminative,zhang2019budgeted}, but this approach still requires real users or a user simulator for world model learning.

Instead of employing a user simulator, a few methods jointly learn two agents directly from the corpus. \citesub{liu2017iterative} models the system and the user by iteratively training two policies.
\citesub{papangelis2019collaborative} make the first attempt to apply MARL into the task-oriented dialog policy, whose algorithm is based on Q-learning for mixed policies. However, it is not well scalable to complex tasks such as multi-domain dialog. Therefore, MADPL uses the actor-critic framework instead to deal with the large discrete action space in dialog.

\section{Multi-Agent Dialog Policy Learning}

We first formally describe the task, and then present the overview of our proposed model. Specifically, given a user goal \texttt{G}=(\texttt{C},\texttt{R}) composed of the user constraints \texttt{C} (e.g. a Japanese restaurant in the center of the city) and requests \texttt{R} (e.g. inquiry for address, phone number of a hotel), and given an external database \texttt{DB} containing all candidate entities and corresponding information, the user agent and system agent interact with each other in a dialog session to fulfill the user goal. There can be multiple domains in \texttt{G}, and two agents have to accomplish all the subtasks in each domain. Both agents can partially observe the environment, i.e. only the user agent knows \texttt{G}, while only the system agent can access \texttt{DB}, and the only way to know each other's information is through conversational interaction. Different from ordinary multi-agent task setting, two agents in dialog are executed \textit{asynchronously}. In a single dialog turn, the user agent posts an inquiry first, then the system agent returns a response, and the two communicate alternately. Therefore, each dialog session $\tau$ can be seen as a trajectory of state-action pairs $\{(s_0^U, a_0^U, s_0^S, a_0^S); (s_1^U, a_1^U, s_1^S, a_1^S);\dots\}$, where the user agent and the system agent make decisions according to each dialog policy $\mu(a^U|s^U), \pi(a^S|s^S)$ respectively.

\begin{figure}[!tb]
    \centering
    \includegraphics[width=\linewidth]{madpl.png}
    \caption{Architecture of MADPL. HVN consists of three critics. Each critic estimates its return based on role-aware reward decomposition, and each actor uses the estimated value to optimize itself.}
    \label{fig:madpl}
\end{figure}

Here we present a novel algorithm, Multi-Agent Dialog Policy Learning (MADPL), as shown in Fig. \ref{fig:madpl}, which can be naturally formulated as a MARL problem. Two agents interact through dialog acts following \cite{georgila2014single}. We choose the \textit{actor-critic} framework in order to learn an explicitly stochastic dialog policy (actor) for high scalability along with an estimated value function (critic) to bootstrap RL training. Besides, this can facilitate imitation learning to pretrain the dialog policy using human-human dialogs. Since two agents cooperate to reach success, yet their roles are asymmetric in the dialog, we propose Hybrid Value Network (HVN) to decompose the task reward into different parts for better policy learning. Note that our approach is fully data-driven without building a user simulator beforehand, and does not need any other human supervision during training.

In the subsequent subsections, we will first explain the state and action used in two dialog policies. Then we describe how we decompose the reward and the proposed HVN. At last, we present model optimization.

\subsection{Dialog Policy}

\paragraph{System Policy}
The system policy $\pi$ decides the system action $a^S$ according to the system dialog state $s^S$ to give the appropriate response to user agent. Each system action $a^S$ is a subset of dialog act set $\mathcal{A}$ as there may be multiple intents in one dialog turn. A \textit{dialog act} is an abstract representation of an intention \cite{stolcke2000dialogue}, which can be represented in a quadruple composed of \textit{domain}, \textit{intent}, \textit{slot type} and \textit{slot value} (e.g. [\texttt{restaurant}, \texttt{inform}, \texttt{food}, \texttt{Italian}]). In practice, dialog acts are \textit{delexicalized} in the dialog policy. We replace the slot value with a count placeholder and refill it with the true value according to the entity selected from the external database \texttt{DB}, which allows the system to operate on unseen values. The system dialog state $s^S_t$ at dialog turn $t$ is the concatenation of (\RNum{1}) user action at current turn $a_t^U$; (\RNum{2}) system action at the last turn $a_{t-1}^U$; (\RNum{3}) the belief state $b_t$ \cite{williams2016dialog} that keeps track of constraint slots and request slots supplied by the user agent; and (\RNum{4}) embedding vectors of the number of query results $q_t$ from \texttt{DB}.

\paragraph{User Policy}
The user policy $\mu$ decides the user action $a^U$ according to the user dialog state $s^U$ to express its constraint and request to the system agent. Similar to the system policy, the user policy uses delexicalized dialog acts as actions, and the value is refilled according to the user goal \texttt{G}. User dialog state $s^U_t$ is the concatenation of (\RNum{1}) last system action $a_{t-1}^S$; (\RNum{2}) last user action $a_{t-1}^U$; (\RNum{3}) the goal state $g_t$ that represents the remained constraint and request that need to send; (\RNum{4}) inconsistency vector $c_t$ \cite{kreyssig2018neural} that indicates the inconsistency between the system's response and user constraint \texttt{C}. In addition to predicting dialog acts, the user policy outputs terminal signal $T$ at the same time, i.e. $\mu = \mu(a^U,T|s^U)$.

\subsection{Reward Decomposition}

On the one hand, the roles between the user agent and the system agent are different. The user agent actively initiates a task and may change it during conversation, but the system agent passively responds to the user agent and returns the proper information, so the reward should be considered separately for each agent. On the other hand, two agents communicate and collaborate to accomplish the same task cooperatively, so the reward also involves a global target for both agents. Therefore, we decompose the mixed reward into three parts according to the characteristic of each component. The reward of each part is explained as follows:

\paragraph{System Reward} $r_t^S$ consists of (\RNum{1}) empty dialog act penalty $a_t^S = \varnothing$; (\RNum{2}) late answer penalty if there is a request slot triggered but the system agent does not reply the information immediately; and (\RNum{3}) task success reward based on the user agent's description.

\paragraph{User Reward} $r_t^U$ consists of (\RNum{1}) empty dialog act penalty $a_t^U = \varnothing$; (\RNum{2}) early inform penalty if the user agent requests for information when there is still a constraint slot remained to inform; and (\RNum{3}) user goal reward whether the user agents have expressed all the constraints \texttt{C} and requests \texttt{R}.

\paragraph{Global Reward} $r_t^G$ consists of (\RNum{1}) efficiency penalty that a small negative value will be given at each dialog turn; (\RNum{2}) sub-goal completion reward once the subtask of \texttt{G} in a particular domain is accomplished; and (\RNum{3}) task success reward based on user goal \texttt{G}.

Obviously, each agent should obtain its local reward, and both agents should receive the global reward during the training process.
Note that the task success and the user goal reward are only computed at the end of the dialog, and the task success computed in the system reward differs from the one in the global reward.

\subsection{Hybrid Value Network}
The value function aims to estimate the expected return given the current state $V(s_t) = \mathbb{E}[R_t] = \mathbb{E}[\sum_{t'\geq t}\gamma^{t'-t}r_{t'}]$ so that the policy can directly use the estimated cumulative reward for optimization, without sampling the trajectories to obtain rewards which may cause high variance.
Another advantage by applying actor-critic approaches in MARL is that it can integrate with the CTDE framework: the actor of each agent benefits from a critic that is augmented with additional information about the policies of other agents during training. However, a simple centralized critic conditioned on the global state and joint actions cannot well exploit the domain knowledge mentioned above since each part of the overall rewards only depends on a subset of features, e.g. the system reward only depends on the system agent's behaviors.

Inspired by \textit{Hybrid Reward Architecture} \cite{van2017hybrid} that learns a separate Q function, we propose Hybrid Value Network to improve an estimate of the optimal role-aware value function. It first encodes the dialog state of each agent to learn a state representation
\begin{align*}
    h_s^S &= \tanh(f^S_s(s^S)), \\
    h_s^U &= \tanh(f^U_s(s^U)),
\end{align*}
where $f(\cdot)$ can be any neural network unit. The value network $V$ is separated into three branches $V^S$, $V^U$ and $V^G$ for the value of system rewards, user rewards and global rewards, respectively.
\begin{align*}
    V^S(s^S) &= f_S(h_s^S), \\
    V^U(s^U) &= f_U(h_s^U), \\
    V^G(s) &= f_G([h_s^S;h_s^U]).
\end{align*}

\subsection{Optimization}
The action space for the policies can be very large since we deal with multi-domain, complex dialog tasks, which makes it almost impossible for the RL policies to explore and learn from scratch. So the training process can be split into two stages \cite{fatemi2016policy,takanobu2019guided}: pretraining the dialog policy with the conversational corpus first and then using RL to improve the pretrained policies.
We use $\beta$-weighted logistic regression for policy pretraining here to alleviate data bias because each agent only generates several dialog acts in one dialog turn
\begin{align}\label{eq:pretrain}
    L(X, Y; \beta) =& -[\beta \cdot Y^T \log\sigma(X)\\
    &+ (I-Y)^T\log(I -\sigma(X))], \notag
\end{align}
where $X$ is the state and $Y$ is the action from the corpus in this task.

As for critic optimization, it aims to minimize the squared error between the temporal difference (TD) target $r_t + \gamma V(s_{t+1})$ and the estimated value $V(s_t) = \mathbb{E}[r_t + \gamma V(s_{t+1})]$. Actor-critic algorithms have high variance  since the critic is updated too frequently, which has contributed to severe changes in the estimated value, particularly in multi-agent tasks. So we introduce a target network \cite{mnih2015human} to make the training process more stable. In the context of HVN, it aims to minimize the following loss functions:
\begin{align}\label{eq:value_network}
    L_V^S(\theta)
    &= (r^S + \gamma V^S_{\theta^-}(s'^S) - V^S_\theta(s^S))^2, \notag \\
    L_V^U(\theta)
    &= (r^U + \gamma V^U_{\theta^-}(s'^U) - V^U_\theta(s^U))^2, \notag \\
    L_V^G(\theta)
    &= (r^G + \gamma V^G_{\theta^-}(s') - V^G_\theta(s))^2, \notag \\
    L_V &= L_V^S + L_V^U + L_V^G,
\end{align}
where HVN $V_\theta$ is parameterized by $\theta$, and $\theta^-$ is the weight of target network, and the overall loss $L_V$ is the sum of value estimation loss on each component reward.

\begin{algorithm}[!tb]
\DontPrintSemicolon
\caption{Multi-Agent Dialog Policy Learning}
\label{algorithm}
\SetKwInOut{Input}{Require}
\Input{Dialog corpus $\mathcal{D}$ with annotations of dialog acts \{$a$\}}
Initialize weights $\phi, \omega$ for system policy $\pi$ and user policy $\mu$ respectively \\
Pretrain policies $\pi, \mu$ on human conversational data $\mathcal{D}$ using Eq. \ref{eq:pretrain} \\
Initialize weights $\theta$ for hybrid value network $V=(V^S, V^U, V^G)$ and target network $\theta^- \leftarrow \theta$ \\
\ForEach{training iteration}{
    Initialize user goal and dialog state $s^U, s^S$ \\
    \Repeat{the session ends according to $T$}{
        Sample actions $a^U, a^S$ and terminal signal $T$ using current policy $\pi, \mu$ \\
        Execute actions and observe reward $r^U, r^S, r^G$ and new states $s'^U, s'^S$ \\
        Update hybrid value network (critic) using Eq. \ref{eq:value_network} \\
        Compute the advantage $A^U, A^S, A^G$ using current value network \\
        Update two dialog policies (actor) using Eq. \ref{eq:policy_network} \\
        $s^U \leftarrow s'^U, s^S \leftarrow s'^S$ \\
        Assign target network parameters $\theta^- \leftarrow \theta$ every $C$ steps
    }
}
\end{algorithm}

Each dialog policy aims to maximize all the related returns, e.g. the system policy $\pi$ aims to maximize the cumulative system rewards and global rewards $\mathbb{E}[\sum_t \gamma^t(r_t^S + r_t^G)]$. The advantage $A(s)=r+\gamma V(s')-V(s)$ estimated by the critic can evaluate the new state $s'$ and current state $s$ to determine whether the dialog has become better or worse than expected. With the aid of HVN, the sum of the related component advantages can be used to update different agents. By using the log-likelihood ratio trick, the gradients for the system policy and the user policy yield:
\begin{align}\label{eq:policy_network}
    \nabla_{\phi} J_{\pi}(\phi) = \nabla_{\phi} \log \pi_\phi(a^S|s^S) [A^S(s^S) + A^G(s)], \\
    \nabla_{\omega} J_{\mu}(\omega) = \nabla_{\omega} \log \mu_\omega(a^U|s^U) [A^U(s^U) + A^G(s)], \notag
\end{align}
where the system policy $\pi_\phi$ is parameterized by $\phi$ and the user policy $\mu_\omega$ by $\omega$.

In summary, a brief script for MADPL is shown in Algorithm \ref{algorithm}.

\section{Experimental Setting}

\subsection{Dataset}
MultiWOZ \cite{budzianowski2018multiwoz} is a multi-domain, multi-intent task-oriented dialog corpus that contains 7 domains, 13 intents, 25 slot types, 10,483 dialog sessions, and 71,544 dialog turns. During the data collection process, a user is asked to follow a pre-specified user goal, and is allowed to change the goal during the session if necessary, so the collected dialogs are much closer to real-world conversations. The corpus also provides the domain knowledge that defines all the entities and attributes as the external database.
\subsection{Metrics}
Evaluation of a task-oriented dialog system mainly consists of the cost and task success. We count the number of \textit{dialog turns} to reflect the dialog cost. A user utterance and a subsequent system utterance are regarded as one dialog turn. We utilize two other metrics: \textit{inform F1} and \textit{match rate} to estimate the task success. Both metrics are calculated at the dialog act level.
Inform F1 evaluates whether all the \textit{requested information} has been informed, and match rate checks whether the booked entities match all the \textit{indicated constraints} given by the user. The overall task success is reached if and only if both inform recall and match rate are 1.

\subsection{Baselines}

We compare MADPL with a series of baselines that involve both system policy learning and user policy learning. Note that we do not consider any other approaches that use a user simulator for policy training because our motivation is to avoid explicitly modeling a simulator.

\paragraph{SL}  \textit{Supervised Imitation Learning} directly uses the dialog act annotations and trains the agents simply by behavior cloning using Eq. \ref{eq:pretrain}, which is the same as the pretraining phase in MADPL.

The following three baselines are all RL algorithms that start from the pretrained policy:

\paragraph{RL} \textit{Independent Reinforcement Learning} learns only one dialog policy by fixing another agent following the single RL setting, and the reward for the agent is the sum of role-specific reward and global reward. For example, the user policy uses the reward $r = r^U + r^G$ at each dialog turn.

\paragraph{CRL} \textit{Centralized Reinforcement Learning} is a MARL approach that uses a single centralized critic on the sum of reward $r = r^U + r^S + r^G$ to train two agents simultaneously, which also serves for the ablation test of MADPL.

\paragraph{IterDPL} \textit{Iterative Dialog Policy Learning} \cite{liu2017iterative} updates two agents iteratively using single RL training to reduce the risk of non-stationarity when jointly training the two agents.

\begin{table}[!tb]
    \centering
    \small
    \begin{tabular}{cccc}
    \toprule
        Class & Attraction & Hospital & Hotel \\
        Count & 320 & 22 & 389 \\
    \cmidrule(lr){0-3}
        Police & Restaurant & Taxi & Train \\
        22 & 457 & 164 & 421 \\
    \midrule
        Num. & Single & Two & Three \\
        Count & 328 & 549 & 123 \\
    \bottomrule
    \end{tabular}
    \caption{Domain distribution of user goals used in the automatic evaluation. A user goal with multiple domains is counted repeatedly for each domain.}
    \label{tab:goal}
\end{table}

\section{Automatic Evaluation}

\subsection{Interaction between Two Agents}

A set of 1,000 user goals are used for automatic evaluation as shown in Table \ref{tab:goal}. When the dialog is launched, two agents interact with each other around a given user goal.
The performance of interaction between the two trained policies are shown in Table \ref{tab:2agent}. MADPL reaches the highest match rate and task success among all the methods. It manages to improve the success rate of the pretrained policies from 49.7\% to 70.1\%. Single RL policies (row 2 to 4) have limited improvement, and even decline in match rate since they assume a stationary environment. The comparison between CRL and IterDPL indicates the effectiveness of CTDE in the multi-agent task. The superiority of MADPL against CRL shows that two agents benefit from the role-aware reward decomposition in HVN. The learning curves in Fig. \ref{fig:2agent} illustrates that the success rate grows rapidly in MADPL, and it always improves the success rate as the training proceeds.

The average reward of each component reward is shown in \ref{fig:reward}. We run 10 different instances of MADPL with different random seeds. The solid curves correspond to the mean and the shaded region to the standard deviation of rewards over the 10 trials. We can observe that all the rewards increase steadily during the training process, which implies that HVN has estimated a proper return for policy training.

\begin{table}[!tb]
    \centering
    \begin{tabular}{c@{~~}c@{~~}|@{~~}c@{~~}c@{~~}c@{~~}c}
    \toprule
        System & User & Turns & Inform & Match & Success \\
    \midrule
        SL & SL & 6.34 & 73.08 & 82.58 & 49.7 \\
        SL & RL & 8.75 & \textbf{76.86} & 76.28 & 60.2 \\
        RL & SL & 6.20 & 72.84 & 79.15 & 51.1 \\
        RL & RL & 7.92 & 75.96 & 70.37 & 58.7\\
        \multicolumn{2}{c|@{~~}}{CRL} & 8.13 & 68.29 & 89.71 & 66.6 \\
        \multicolumn{2}{c|@{~~}}{IterDPL} & 8.79 & 74.01 & 81.04 & 64.6 \\
    \midrule
        \multicolumn{2}{c|@{~~}}{MADPL} & 8.96 & 76.26 & \textbf{90.98} & \textbf{70.1} \\
    \bottomrule
    \end{tabular}
    \caption{Performance of the interaction between the user agent and the system agent.}
    \label{tab:2agent}
\end{table}

\begin{figure}[!tb]
    \centering
    \includegraphics[width=\linewidth]{curve_user_to_system.png}
    \caption{Learning curves of the interaction between the user agent and the system agent.}
    \label{fig:2agent}
\end{figure}

\begin{figure}[!tb]
    \centering
    \includegraphics[width=0.81\linewidth]{curve_system_reward.png}
    \includegraphics[width=0.81\linewidth]{curve_user_reward.png}
    \includegraphics[width=0.81\linewidth]{curve_global_reward.png}
    \caption{Learning curves of MADPL on system reward (top), user reward (middle) and global reward (bottom).}
    \label{fig:reward}
\end{figure}

\subsection{Interaction with Benchmark Policies}

It is essential to evaluate a multi-agent dialog system whether all the agents understand the semantic interaction rather than invent an uninterpretable language \cite{kottur2017natural,lee2019countering}. To this end, we use two benchmark policies in the standardized task-oriented dialog system platform Convlab \cite{lee2019convlab} to examine all the methods. Each benchmark is a strong rule-based system policy or user policy at the dialog act level, which is used as the simulated evaluation in the DSTC-8 Track 1 competition and show a high correlation with real user interaction \cite{li2020results}.
The trained system/user policy in each method is directly deployed to interact with the benchmark user/system policy during the test without any other finetuning, which can be regarded as a weakly zero-shot experiment. The same goal set in Table \ref{tab:goal} is used here.

Table \ref{tab:rule_system} and Fig. \ref{fig:rule_system} show the results of the interaction between the benchmark user policy and the system agent of each model. The SOTA performance from GDPL \cite{takanobu2019guided} that directly trains with benchmark user policy is also presented as the soft performance upper bound. Among all the methods, MADPL has achieved the highest task success and the second-highest match rate. All the methods experience a decline in inform F1 after the RL training. Fig. \ref{fig:rule_system} also shows that the success rate is unstable during training. This is because the action space of the system policy is much larger, thus more challenging to learn. In spite of that, the success rate of MADPL shows a rising trend.

\begin{table}[!tb]
    \centering
    \begin{tabular}{c|cccc}
    \toprule
        System & Turns & Inform & Match & Success \\
    \midrule
        SL & 7.76 & \textbf{83.33} & 85.84 & 84.2 \\
        RL & 7.53 & 82.06 & 85.77 & 84.3\\
        CRL & 8.38 & 72.43 & \textbf{89.48} & 86.4 \\
        IterDPL & 7.74 & 79.68 & 82.49 & 82.5 \\
    \midrule
        MADPL & 7.63 & 79.93 & 89.24 & \textbf{87.7} \\
        GDPL & \textit{7.62} & \textit{92.10} & \textit{91.50} & \textit{92.1} \\
    \bottomrule
    \end{tabular}
    \caption{Performance of the interaction between the benchmark user policy and each system agent.}
    \label{tab:rule_system}
\end{table}

\begin{figure}[!tb]
    \centering
    \includegraphics[width=\linewidth]{curve_benchmark_to_system.png}
    \caption{Learning curves of the interaction between the benchmark user policy and each system agent.}
    \label{fig:rule_system}
\end{figure}

\begin{table}[!tb]
    \centering
    \begin{tabular}{c|cccc}
    \toprule
        User & Turns & Inform & Match & Success \\
    \midrule
        SL & 8.64 & 78.64 & 87.84 & 51.7 \\
        RL & 11.18 & 85.69 & 92.13 & 77.2 \\
        CRL & 11.31 & 86.58 & \textbf{92.89} & 74.7 \\
        IterDPL & 12.53 & 84.68 & 92.57 & 75.5\\
    \midrule
        MADPL & 13.25 & \textbf{87.04} & 90.81 & \textbf{83.7} \\
    \bottomrule
    \end{tabular}
    \caption{Performance of the interaction between each user agent and the benchmark system policy.}
    \label{tab:rule_user}
\end{table}

\begin{figure}[!tb]
    \centering
    \includegraphics[width=\linewidth]{curve_user_to_benchmark.png}
    \caption{Learning curves of the interaction between each user agent and the benchmark system policy.}
    \label{fig:rule_user}
\end{figure}

\begin{figure*}[!tb]
    \centering
    \includegraphics[width=0.40\linewidth]{single.png}
    \includegraphics[width=0.40\linewidth]{restaurant.png}
    \includegraphics[width=0.40\linewidth]{double.png}
    \includegraphics[width=0.40\linewidth]{hotel.png}
    \includegraphics[width=0.40\linewidth]{triple.png}
    \includegraphics[width=0.40\linewidth]{train.png}
    \caption{Performance of dialog agents according to the different number (left) or class (right) of domains in the dialog.}
    \label{fig:domain}
\end{figure*}

Table \ref{tab:rule_user} and Fig. \ref{fig:rule_user} show the results of the interaction between the user agent of each method and the benchmark system policy. Among all the methods, MADPL has achieved the highest inform F1 and task success. Though CRL improves the performance at the beginning, the success rate fails to increase further afterwards, while MADPL continues to improve all the time. This also indirectly indicates the advantage of using role-aware reward decomposition in HVN.

In summary, each policy trained from MADPL can interact well with the benchmark policy, which implies that MADPL learns a reasonable dialog strategy.

\subsection{Goal across Multiple Domains}

We also investigate the domains in the user goals to observe the scalability of each method in the complex tasks. 200 goals are randomly sampled under each setting. Fig. \ref{fig:domain} presents the results of the interaction between two agents in different numbers or classes of domains. The success rate decreases substantially as the number of domains increases in the goal. When there are 3 domains in the goal, RL/RL gets a high inform F1 but a low match rate, IterDPL gets a high match rate but a low inform F1, while MADPL can still keep a high inform F1 and match rate, and obtains the highest task success. In terms of the class of domains, there are 7/10/6 informable slots that needs to be tracked in the \textit{Restaurant}/\textit{Hotel}/\textit{Train} domain respectively. Among these, MADPL outperforms other baselines in the \textit{Restaurant} and \textit{Hotel} domains, and performs comparably in the \textit{Train} domain. In brief, all the results indicate that MADPL has good scalability in multi-domain dialog.

\section{Human Evaluation}

\begin{table}[!tb]
    \centering
    \begin{tabular}{l@{~~}c@{~~~}c@{~~~}c@{~~~}c@{~~~}c@{~~~}c@{~~~}c@{~~~}c@{~~~}c}
    \toprule
        \multirow{2}{*}[-0.04in]{VS.} & \multicolumn{3}{@{~}l@{}}{System Q} & \multicolumn{3}{@{~~~}l@{}}{User Q} & \multicolumn{3}{@{~~}l@{}}{Success} \\
        \cmidrule(l{-0.1em}r{0.8em}){2-4} \cmidrule(l{-0.1em}r{0.8em}){5-7} \cmidrule(l{-0.1em}r{0.7em}){8-10}
        & W & D & L & W & D & L & W & D & L\\
    \midrule
        SL/SL & 55 & 22 & 23 & 61 & 25 & 14 & 68 & 26 & 6 \\
        RL/RL & 49 & 23 & 28 & 52 & 28 & 20 & 70 & 19 & 11 \\
        IterDPL & 50 & 27 & 23 & 56 & 30 & 14 & 64 & 24 & 12 \\
    \bottomrule
    \end{tabular}
    \caption{Human preference on dialog session pairs that MADPL wins (W), draws with (D) or loses to (L) baselines with regard to quality (Q) and success by majority voting.}
    \label{tab:human}
\end{table}

For human evaluation, we hire Amazon Mechanical Turkers to conduct pairwise comparison between MADPL and baselines. Since all the policies work at the dialog act level, we generate the texts from dialog acts using hand-crafted templates to make the dialog readable. Each Turker is asked to read a user goal first, then we show 2 dialog sessions around this user goal, one from MADPL and the other from another baseline. We randomly sample 100 goals for each baseline. For each goal, 5 Turkers are asked to judge which dialog is better (win, draw or lose) according to different subjective assessments independently: (\RNum{1}) system quality, (\RNum{2}) user quality, and (\RNum{3}) task success. The system quality metric evaluates whether the system policy provides the user with the required information efficiently, and the user quality metric evaluates whether the user policy expresses the constraints completely in an organized way. Note that we do not evaluate the quality of language generation here.

Table \ref{tab:human} shows the results of human preference by majority voting. We can observe that the high win rate of MADPL on the task success is consistent with the results of automatic evaluation, and MADPL outperforms three baselines significantly in all aspects (sign test, p-value $<$ 0.01) except for the system quality against RL/RL policies.

The proportion of the pairwise annotations in which at least 3 of 5 annotators assign the same label to a task is 78.7\%/77.3\%/83.3\% for system quality/user quality/task success, respectively. This indicates that annotators have moderate agreements. The human judgements align well with the results of automatic evaluation, which also indicates the reliability of the metrics used in task-oriented dialog.

\section{Conclusion}

We present a multi-agent dialog policy algorithm, MADPL, that trains the user policy and the system policy simultaneously. It uses the actor-critic framework to facilitate pretraining and bootstrap RL training in multi-domain task-oriented dialog. We also introduce role-aware reward decomposition to integrate the task knowledge into the algorithm.
MADPL enables the developers to set up a dialog system rapidly from scratch. It only requires the annotation of dialog acts in the corpus for pretraining and does not need to build a user simulator explicitly beforehand.
Extensive experiments\footnote{We provide implementation details and case studies in appendix.} demonstrate the effectiveness, reasonableness and scalability of MADPL.

As future work, we will apply MADPL in the more complex dialogs and verify the role-aware reward decomposition in other dialog scenarios.

\section*{Acknowledgement}
This work was jointly supported by the NSFC projects (Key project with No. 61936010 and regular project with No. 61876096), and the National Key R\&D Program of China (Grant No. 2018YFC0830200). We would like to thank THUNUS NExT Joint-Lab for the support.
The code is available at \url{https://github.com/truthless11/MADPL}.

redpajama_set_name: RedPajamaArXiv


Our trip took nine days. We flew from LAX directly on Thai Airlines to Bangkok. The flight took 17 hours, and is the longest commercial flight in the world. We landed in Bangkok, and spent two days seeing the sites of the city.
Where we took a longboat for a twenty minute ride to Railay Bay and the Railay Bay Beach Resort.
Our room for the week. Nice!
Our room faced one of the pools. Not too tough.


Share some of your photography work in here. Don't worry, if its not a "photographic shot" and was more "point and click", we don't care - let's just see them anyway!
I'll share some of mine when I load my laptop this morning.


In this short and sweet post I am going to share my thoughts about first impression of Lo'real Paris Skin Perfect Anti-Imperfections+Whitening 20+ cream.
I bought this cream from nykaa.com during a massive sale.
The moment I opened up the lid, my entire room got filled with a strong scent which faded away after around 30 minutes when I applied it on my face. The cream looks thick but is soft to touch and feels light during application. It feels so hydrating and refreshing to my skin and disappears on my face within seconds. I took a very little amount and it got fully absorbed by my skin. It mattified and brightened up my oily face and I actually liked how soothing it felt on my skin. I also felt a cooling effect during application of this cream on my skin which might be due to the presence of alcohol in it.
It looks like a better version of Ponds' white beauty which is sold like a fairness cream. Unlike ponds white beauty, it does not get patchy and leaves no white cast.
I had high hopes from this cream when I bought it but after using it, I felt a little disappointed. As I said earlier also, it looks like a fairness skin and I doubt it's capability to tackle some serious skin issues that we begin to notice in our twenties. It can be a good daily wear cream for oily and combination skin but I don't think it has got anything to help with the skin problems like spots, blemishes, uneven skintone that one has in her twenties. I need to use it for 2-3 weeks at least before giving my final verdict about it's spot lightening action. Let's see.


It is time for the Commission to let antidumping duties and its 'price undertaking' on Chinese solar panels expire. The measures do not help the EU achieve it climate goals, argues Christofer Fjellner.
The EU is extending its duties on solar panels from China. These antidumping tariffs are not only an example of bad environmental policy. Imposed in 2013 with the intention to solve a problem of trade distortion, they have created a new set of distortions that shift profits to Chinese producers and also risk making European solar panels uncompetitive.
It is time for the Commission to let these measures expire. Any other decision will only undermine our climate efforts, the coherence of our trade policy and our efforts to support Europe's green industry.
Antidumping duties should only be applied using strict criteria and sensibly take into account the Union interest. The anti-dumping duties on solar panels are a clear case where there is a strong Union interest on the importing side against such measures, and where the duties actually create more market distortions than they correct.
The price undertaking agreed in parallel with Chinese producers is essentially a form of sanctioned cartel: Chinese producers have agreed to sell above a secret minimum price negotiated with the Commission. The increased cost for European consumers inflates the profits made by solar panel producers in China.
The solar panel duties imposed by the EU are the largest protectionist measure in Europe today. Solar panels represent roughly half of the total value of all EU imports affected by trade defence instruments. This is not just a highly questionable use of the EU's trade defence instruments. I believe the duties are harmful as such.
There is an inherent conflict in every antidumping case between consumers and downstream companies importing on one side and upstream producers being protected by a duty on the other. A majority of jobs in the European solar sector is found downstream, in the business segment of the sector with over 80 percent of the value added. It is likely that the duties have led to a net loss in jobs in Europe.
It has been said that when two producers meet, they will conspire against the consumer. The undertaking has resulted exactly in that. Chinese producers increase their profits and the few remaining European producers are sheltered from competition – all to the detriment of consumers and downstream producers.
Still, there is hardly a consensus among upstream producers that the duties serve their interest. The initial complainants of the alleged Chinese dumping account for a minority of European producers. This is not surprising as the minimum import price distorts all incentives to develop better and more efficient solar panels in Europe. Chinese producers now compete in developing high quality solar panels as the price for the European market is de facto fixed by a minimum price. The long-term effect is that European producers are losing any competitive edge that they might have had before the introduction of duties. Furthermore, all incentives for European solar panel producers to be cost-efficient is gone.
These measures are also harmful environmental policy. EU ambitions to tackle climate change after the COP 21 agreement in Paris requires to further reduce emissions of greenhouse gases: this must be done in a cost-efficient manner. Making the transition to renewable energy more costly will damage the legitimacy and popular support for our environmental ambitions. The deployment of solar panels is growing exponentially, and prices on world markets are decreasing. We should welcome cheaper green technology that can help us meet our ambitious climate and energy objectives. That is nothing we should fear but rather make use of and speed up our transition to less carbon-intensive energy production.
For the sake of our jobs, our consumers and our environment, the duties on solar panels should be removed. The duties were introduced to create a level playing field and fair international competition between solar panel producers, but they have not solved the problem. In fact, they have created larger distortions than the ones they were intended to solve.
The Commission has made up its mind but I now expect member states to take the Union interest into account and oppose the renewal of the duties. Only that way can we have credibility in our ambitions and policies for openness to trade and fighting climate change in an effective and cost-efficient manner.


Suitable for home and professional use. Nail polish storage, Very good condition,only selling because of our change of house.
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Condition: made of high quality acrylic material, waterproof and moisture proof, suitable for professional use. brand new chanel make up lipstick/ nail polish organiser box.
Nail polish case and nail polish roll nail polish case …. durable and practical, acrylic material.. I'm selling a little nail polish storage for 89 or give me a offer; Don't miss out guys, great stuff!
Clarins french white nail polish.. "If an item you have received is faulty, please contact us to arrange an exchange or refund"


text:
Q: How to change Gradle\SDK settings when opening Android Studio project from a different PC I Copied my Android Studio project from my work PC and copied it to my home PC.
When I opened the project I got the following error:

Failed to import Gradle project: Could not fetch model of type
  'IdeaProject' using Gradle distribution
  'http://services.gradle.org/distributions/gradle-1.6-bin.zip'. Build
  file
  'C:\Users\Tom\AndroidStudioProjects\CongressProject\Congress\build.gradle'
  line: 9 A problem occurred evaluating project ':Congress'. A problem
  occurred evaluating project ':Congress'. The SDK directory 'D:\Program
  Files\Android\android-studio\sdk' does not exist.

I guess that's because all my SDK\JDK and all other settings are in different locations in my work PC and home PC.
I get this error all the time I try to rebuild my project.
How do I fix this so I can work on the same project from home and work?
I want to make it easy to copy the project from my 2 different PC's.
thanks!

A: I found a similar question in:
Gradle build errors after updating Android Studio
in the local.properties file in the root of the project, change the sdk.dir value.

A: For anyone else running into Gradle issues after updating Android Studio:
Go to File > Project Structure > Project.
My Gradle version and Android plug in version did not match, which was causing my issue. If you make a change here, make sure you also update your top-level Gradle file with the same version like so: classpath 'com.android.tools.build:gradle:some version number'

redpajama_set_name: RedPajamaStackExchange


text:
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www.yoursitename.com provides YOURSITENAME who have access with a multitude of online information, services, programmes and data (hereinafter, the content), which belongs to the Complutense University of Madrid. The USER assumes the liability of using the portal.
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We currently have an opening for a Kitchen Coordinator/Church Hostess. This is a part-time position with a start date of September 1. Basic responsibilities include planning, organizing, and preparing meals for the upcoming church year including the Wonderful Wednesday meals, ordering supplies for the kitchen and dining room, and overseeing the general cleanliness of the kitchen. A complete job description is available in the church office.
They may also be sent by fax to 336-889-0262.


Brad Ezell is a Vice President of Haskell and as a licensed Professional Engineer, he has devoted his entire 24-year career to the Architectural and Engineering (A&E) design of real-property facilities, buildings and infrastructure.
He has experience with the design of military command and control facilities, defense operations and maintenance facilities, state and municipal capital projects, hospitals, university research and teaching facilities, corporate office buildings, historic renovations and many additional types of buildings and facilities at both domestic and international locations.
In his current role, Brad leads the Federal Business Development and has oversight on design projects ranging in construction value from $50,000 to $300 million, financial performance and overall project quality and execution. He takes a personal and leadership role when working with clients in helping them evaluate design alternatives for their planned facilities and make well informed decisions to arrive with solutions that achieve their goals and needs for their new facility within the established budget.
Brad graduated from Kansas State University with a Bachelor of Science in Architectural Engineering and from Southern Nazarene University with a Master's of Science in Management. He is active in the community and is currently on the Board of Directors for the United Way of Central Oklahoma and serves as the Chair of the Community Investment Committee.


Each year, we take time to reflect on what we have accomplished as a company, and how we can continue to grow and improve. As we looked back on 2017, a key theme continued to emerge as a driving force: The Power of Science.
We are happy to announce the release of Pfizer's 2017 Annual Review, which highlights our focus on harnessing the full power of science to discover, develop and bring to market life-saving medicines and vaccines that help the lives of patients around the world. This year's report uses the lens of our colleagues to illustrate how The Power of Science inspires the work we do every day.
A firsthand perspective on the multifaceted approach we are taking to help prevent and tackle antimicrobial resistance (AMR). AMR is raising concerns of life-threatening infections that are impossible to treat.
A spotlight on the exciting and potentially transformative field of gene therapy, which addresses the root cause of disease caused by genetic mutations.
Advances in our oncology pipeline, including immuno-oncology, which harnesses the natural ability of the body's immune system to recognize and fight cancer.
Innovative partnerships, which allow us to find new solutions to the barriers to accessing health care in developing countries.
Visit www.Pfizer.com/Annual to hear firsthand from the individuals making an impact, click through interactive infographics, and learn more about how The Power of Science drives us to help improve lives around the world.
Learn how The Power of Science drives every aspect of our business.
The Power of Science drives every aspect of our business in our mission to deliver innovative medicines and vaccines that help improve health and well-being at every stage of life.


6 Costumes in 2 hours!
Tonight is going to be CRAZY fun!!
I'm going to have a friend in the studio tonight that is a true Costume Artist who specializes in authentic Star Wars costumes. He has a collection of over 20 that we will be doing fine art images of all of them over the next few months, so LOTS of fun for me!!
Tonight we are starting with a classic stormtrooper, sand trooper, tie pilot, rebel pilot, a rebel fleet pilot, and even a Jawa!
Watch out for photos on Facebook over the next few days!
Next Stormtroopers, horses, and families – oh my!


Bitters are to cocktails what proper seasoning is to food. You can make a drink without them but once added the difference is remarkable.
Angostura bitters are one of the most well-known cocktail bitters. They are an extremely concentrated botanically infused alcoholic mixture made of water, alcohol, herbs and spices by House of Angostura in the country of Trinidad and Tobago, legend even has it that only five people actually know the exact recipe.
You'll find Angostura bitters as a key ingredient in classic cocktails like the Old Fashioned and the Manhattan, as a garnish in the form of a few aromatic drops atop a Pisco Sour and in the case of the two cocktails featured below it adds a botanically spicy note.
Don's Little Bitter Cocktail: rum, fernet, lotsa bitters!
Combine in an ice-filled glass and shake for 15-20 seconds.
Strain into a cocktail glass or serve down in a chilled, empty rocks glass.
Angostura Bitters are also known to have "medicinal" properties or so say the elders in my family. So consider this a bonus.
Drink down in one drink but do not chug.


text:
Cambodia's Khmer Rouge quartet face trial
Standing before a UN-backed tribunal in Phnom Penh are four aging senior members of Cambodia's Khmer Rouge that ruled the country from 1975 to 1979.
In more than 30 years, the quartet—former President Khieu Samphan, "Brother Number Two" Nuon Chea, former Foreign Minister Ieng Sary and former Social Affairs Minister Ieng Thirith—are facing justice for the first time, The Guardian stated.
They are accused of crimes against humanity, mass execution, exhaustion, religious persecution, starvation and torture that killed 1.7 million people, one-fourth of Cambodia's population.
Khmer Rouge was the name of the Communist Party of Kampuchea after its "year zero" revolution brought the darkest pages in the country's history.
The party was led by Pol Pot—who died in 1998 not facing trial—Samphan, Chea, Sary and Son Sen.
In 2010, the first Khmer Rouge member, Kaing Guek Eav, alias Duch, was sentenced to 35-year jail term but was later reduced to 19 years, Al Jazeera said.
Watch the attached YouTube video.
Details of this report here.
Categories: Current Affairs, People, Travel


text:
Idaho Potato Harvest Tour Part 2: All About Potatoes
Have you ever seen so many potatoes? Imagine 13 billion pounds of potatoes…that's how many are harvested each year in Idaho on 300,000 acres. 94 per cent of those potatoes are russets like these…and there are different varieties of russets too: Burbank, Norkotah, Ranger, and Western, to name a few. Idaho potato farmers also grow gold, pink, red, and fingerling potatoes. Only about 29% of those potatoes are for eating fresh, and 9% are for seeds. More than 60 per cent of all the potatoes grown are processed: frozen (french fries, hash browns, tater tots. etc.) or dehydrated as potato flakes and slices for instant mashed potatoes and casseroles.
Unwashed potatoes and potato vines come into the plant from the farm.
Last week I was lucky enough to visit Idaho to celebrate the harvest with Idaho Potato Commission. I joined a small group of food bloggers and registered dietitians on tours of farms and potato processing facilities. On our first full day in Idaho we all piled into a bus and drove through the beautiful Idaho countryside. Acres and acres of potato fields had been readied for the harvest…plants cut and removed…potatoes in the ground, waiting for the harvest. Rainy weather was delaying the farmers, who needed the soil to be dry and dusty in order to get the machinery into the fields.
Our first stop was in American Falls at the Lamb Weston processing plant, the first one built in the 60's by Lamb Weston to make frozen potato products, specifically french fries! We removed all our jewelry, donned hair nets and hard hats, signed into the visitors log book, and were ushered through the labyrinth of hallways to a conference room. There we saw a movie about Lamb Weston with an introduction to what we would see in the plant. It's a good thing we saw that movie too, because it was so noisy inside the plant we could hardly hear our guides' narrations. We learned how the potatoes are cleaned and how the skins are removed. We saw the special knives that cut the potatoes into fries, and innovation developed by Lamb Weston years ago that has come an industry standard.
Washed and ready for the next step.
We split up into small groups, climbing metal stairs, walking the cat walks, and observing all the intricate machinery that produces the cut and fried potato products we all love to eat. Later we were treated to samples of the foods they make, including McDonald's french fries, sweet potato puffs, and jalapeño poppers. Each of the products they make are done to exacting standards dictated by their customers. Quality control and food safety are huge priorities in the plant. I was impressed that the people we met who work there have been doing their jobs for decades, taking enormous pride in their jobs and the company as a whole. Later I looked up Lamb Weston, and learned that they deserve to be proud of a legacy that includes some of the greatest innovations made over the last 60 years in the frozen produce industry.
View of Wada Farms' storage and headquarters.
Our next stop was Wada Farms, a 4th generation family operation that began in 1943 when Frank and Agnes Wada moved to Idaho to avoid internment during World War II, having moved from Japan to farm in San Clemente, Ca. in 1922. They started with 160 rented acres, increasing to 400 acres by 1972 when Frank retired, to over 30,000 acres in production today. The company motto is "Pride in Excellence", and it is evident that the family's adherence to that philosophy has contributed to their success.
Here we were welcomed in the conference room by Chris Wada, the grandson of Frank and Agnes, who has joined his brother in the family business. He told us how his grandfather had corresponded with Albert Einstein as a pen-pal, and how his father Albert was named for the renowned physicist. One of the dietitians in our group works with the Pittsburg Steelers, so we had some light moments with a Wada manager who is a big Steelers fan. Again we removed our jewelry, put on nets, ear-plugs, hard hats, and walked through the plant to see potatoes cleaned, sorted, packaged, and wrapped for specific consumer uses. This family has mastered the art of "vertical integration": growing, storing, processing, packaging, and marketing their potatoes themselves.
All those potatoes moving through the machinery reminded me of a huge Mousetrap Game…or a choreographed dance!
One innovative marketing technique Wada Farms has done is to wrap potatoes in foil for grilling or plastic for microwaving. If you see "Easy Grillers" or "Easy Bakers" in your store, you'll know they came from Wada Farms.
I wanted to spend more time looking at all the photos and displays of the fascinating history of Wada Farms displayed in the hallways. I snapped a photo of a picture of the harvesting, since the weather was preventing the farmers from showing us that part of the operation. After we left the building, we visited the potato storage nearby to see how the potatoes are held after the harvest in anticipation of packaging or processing. Millions of pounds of potatoes are put into storage during the autumn harvest and used over the following 6-7 months.
The potatoes are unloaded from the trucks to this long conveyer, which carries the them one by one into the long concrete building. As the potatoes are loaded in, large metal culverts that have holes drilled along their length are laid cross-wise on the floor underneath the potatoes. Cool mist and air is blown with fans into the metal tubes to keep the potatoes at a consistent humidity and temperature so they will remain fresh during the months they may be stored. Each storage can hold up to 80,000 sacks of potatoes.
Here you can see the metal pipes lined up on the sides of the building, with pipes laid in front of the growing pile of fresh potatoes. I must admit that seeing the huge pile of potatoes was exciting and impressive!
Are you enjoying the trip so far? It's fun to remember all that we saw and especially the warm welcome we received each place that we visited. We were disappointed that the weather wasn't cooperating for the farmers' harvest, and didn't expect to be able to see any potatoes being harvested at all. Our hosts from the Idaho Potato Commission made some phone calls and found a farmer who was willing to let us visit his fields later in the afternoon. As we drove back toward Idaho Falls, we took in the huge open farm fields and the beautiful cloud formations in that enormous sky. It had been a long day, but we were giddy with anticipation! We were going to dig up potatoes ourselves on an Idaho potato farm!
Disclaimer: I was invited on an expense-paid trip by the Idaho Potato Commission to celebrate the Idaho potato harvest. I was under no obligation to write this post, was not paid to write this post, and all opinions and experiences are mine.
Categories: Farm Tours, Farming and Agriculture, Stories, Travel | Permalink
Grace Raymond
Mimi–I'm really enjoying the tour so far…you almost make me feel as if I had tagged along with you! Looking forward to next installment!
Mimi Holtz
Thank you so much for reading, Grace! I want to hear more about your latest trip too!
Kim - Liv Life
What an amazing trip!! I always love discovering where our food comes from and the families who are behind it. Thanks for giving us a behind the scenes look!
Kim – Liv Life recently posted..Pike Place Market, Fishmongers and Flying Salmon, Dungeness Crab and Lunch at Matt's on the Market… Day Two with Girard's Culinary Collective
Thanks for following the trip, Kim! I'm loving your adventures in Seattle too!
End of March...Finding Flowers in the Avocado Grove


text:
YOUTH MASTER MIND
THINK AND GROW RICH LIVE
GROUP MASTERMIND NETWORK
The eve a new year was ushered in, a new era began in the life of Ann McNeill. It was December 31 1980. Instead of enjoying New Year's Eve festivities with her husband, she decided to stay home and read a book. What book could be that interesting? That book was Napoleon Hill's Think and Grow Rich and it changed her life. The book soon became her blueprint in creating her strategic life plan, she took the book with her every where she went, preaching its principles to anyone that would listen. She gave the book to a small circle of friends at her local church in West Palm Beach, Florida and from there began to give the book as a gift for birthdays.
In Think and Grow Rich, McNeill was introduced to the Mastermind concept of goal setting. Once she started to apply the principles in the book to her own life, a shift began to take place. She began to move from mediocrity to excellence. She also realized that it would be selfish to keep the Mastermind concept to herself, and a major part of Mastermind is accountability. She needed to share Mastermind with others. She needed a group of like minded people to whom she would be accountable She started to meet with those she had given the book to for discussion "sessions". From this the original Mastermind Women's Group was formed.
As McNeill's life transitioned so did the group. Her move to Miami in the early 1990s meant a formation of a Mastermind group. The original Miami Mastermind Women included Alice Fincher, Bert Johnson, Isabella Rivers, Shaundra Scott, Nifretta Thomas and, of course, Ann McNeill. The group met every Saturday morning at 7:00AM to discuss their goals and over the next 5 years saw tremendous growth in its members. McNeill left her corporate job to start a construction company, which she has parlayed into a small dynasty. She is the President of MCO Construction and Services Inc., MCO Consulting, Inc., Constructively Speaking, Inc., Franchise Owner of Colbert/Ball Tax, Founder of Speak 2 Share, Incorporated and of course Founder and President of the International Mastermind Association. The group has produced several authors, such as Dr. Mia Y. Merritt, and a host of other successful entrepreneurs.
Mastermind Women's Group has members that have come and gone, but the nucleus and precepts on which it was founded have remained. In 2005, the group restructured and Mastermind Empowerment Association was born and as the organization was officially incorporated for the first time, it saw an influx in membership, growing to nearly one hundred members. Further, for the first time the membership included men and children. It was also at this time that the organization's annual meeting began to be restructured into the conference that it is today.
Again, in 2007 the organization began to remake itself, changing its name from Mastermind Empowerment Association, Incorporated to International Mastermind Association to encompass the membership that has now spread from the United States to other countries.
As the organization continues to grow and expand the mission still remains the same… To teach people how to create a strategic life plan, in order for them to achieve success.
© 2023 IMA | Theme by Theme Ansar


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Report: Graduation Gap Grows Between Rich, Poor
Jamaal Abdul-Alimcmaadmin (EDU)
A report, co-authored by Margaret Cahalan, also found a drastic decline in the purchasing power of the Pell Grant for low-income students.
The percentage of students from low-income families who go on to earn a bachelor's degrees is almost the same today as it was in 1965 — 6 percent
then versus 9 percent now — while the percentage of students from
high-income families who go on to earn a bachelor's degree has skyrocketed between then and now — 40 percent then versus 77 percent now.
That is one of the most disquieting findings of "Indicators of Higher Education Equity in the United States," a report released jointly Tuesday by the Pell Institute for the Study of Opportunity in Higher Education and PennAHEAD,
or Alliance for Higher Education and Democracy.
"It's pretty discouraging," said Laura Perna, co-author of the report and education professor at the Penn Graduate School of Education, where she is executive director of PennAHEAD, or Alliance for Higher Education and Democracy.
"I think there is some progress, but the magnitude of the gaps that persist are
so large," Perna said of the report's findings during an interview Tuesday
at the National Press Club.
Asked if the widening of the gap between rich and poor students who earn a bachelor's degree suggests that higher education policies and initiatives such as the Higher Education Act of 1965 and Pell Grants have failed, Perna said the gap shows that there is a perpetual need to examine higher education policy and practice in light of ongoing shifts in the demographics of the United States.
"I think the inferences are we need to continually be revising the policies and practices we have in place," Perna said. "To conclude from this that the policies haven't had any effect ignores other types of societal changes, changes in demographics and expansions of other types of opportunities.
"There's more choice now in our system," Perna continued.
The report also found a drastic decline in the purchasing power of the Pell Grant for low-income students and large disparities in the percentage of poor students who go to two-year and for-profit colleges versus public and private non-profit four-year universities, where the completion rates are higher.
"Choice adds the promise of benefit but it also creates challenges," Perna said.
Perna said the report suggests two main priorities — making sure college is affordable and academic readiness.
The affordability issue should be tackled through boosting the purchasing power of Pell Grants controlling tuition and increasing state appropriations for higher education.
"College affordability is one key," Perna said. "The second is around academic readiness. We have to make sure all students are not only academically ready to enter but to persist to degree completion.
"A third type of policy is that it's important to make sure those that start out at two-year institutions are able transfer without loss of credits," Perna said.
The "Indicators" report contains charts and tables on six "equity indicators" that deal with issues of higher education access and affordability between different income groups in the United States.
In short, the indicators show:
• A modest narrowing of the gap between students from the bottom and top family income quartiles who go on to college. In 1970, the gap was 46 percentage points with 28 percent from the bottom quartile going to college versus 74 percent from the top quartile. In 2012, the gap was 37 percentage points, with 45 percent from the bottom quartile going to college versus 82 percent from the top quartile.
• A widening of the gap between Pell and non-Pell students who enroll at four-year versus two-year institutions. In 2001, the gap was 14 percentage points but grew to 20 percentage points in 2012. Specifically, in 2001, 71 percent of non-Pell students went to four-year institutions versus 57 percent of Pell students. In 2012, 75 percent of non-Pell students went to four-year institutions versus 55 percent of Pell students.
• A decline in the maximum Pell Grant in "constant 2012 dollars" from 1974 through 2012 while college costs rose. Specifically, in 1974, a Pell Grant was worth $4,690 in 2012 dollars versus $5,550 in 2012. But college costs rose from an average $8,858 in 2012 dollars in 1974 to $20,234 in 2012.
• Students and families are paying for a greater share of the cost of higher education — 33 percent in 1977 versus 49 percent in 2012.
• In 2013, students from high-income families were eight times more likely to obtain a bachelor's degree by age 24 than those from low-income families — 77 versus 9 percent. The advantage is larger than it was in 1970, when those from high-income families were just five times more likely to obtain a bachelor's degree than students from low-income families.
The report — coauthored with Margaret Cahalan, vice president for research at the Council for Opportunity in Education and director of the Pell Institute for the Study of Opportunity in Higher Education — contains a series of recommendations to reduce disparities by income in higher education.
They range from increasing the reach, funding and capacity of college access programs to restoring Pell Grants to their former buying power.


Artificiality is not a set of developments away from the nature that it created. It must come back to its origin, the human mind, to relate itself for its most powerful use.
Move the mouse over grid to create ripples.
Shunsuke Nakano received a Bachelor of Architecture Degree from Illinois Institute of Technology and a Masters in Advanced Architectural Design from Columbia University.


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redpajama_set_name: RedPajamaGithub


The Zelkova serrata is a non-native, but it is one that you are seeing more and more across the country. It is hardy zones 5- 8, quite adaptable to many urban type settings. In Michael Dirr's Hardy Trees and Shrubs the Zelkova is listed in the back for drought tolerant trees, handling compacted soils, heat tolerant, and ideal for street / urban planting. Many landscapers are using it more in place of the American Elm as it has more resistance to the Dutch Elm disease.
As a mature tree the Zelkova will be a large tree, height ranging from 60- 90 feet tall with a spread of up to 40 feet. The growth habit has the limbs forming a tight vase. In order for this tree to maintain its nice shape and be a healthy tree proper pruning techniques need to be followed. The limb crotch angles are tight and one should prune to keep the center of the tree open.
This sample of the Zelkova is in the Learning Garden and has been pruned for the last number of years with an eye on keeping the vase shape open and room for the limbs to mature.
The 2- 5 inch alternate leaves are dark green and serrated. In the fall this tree really shines. The fall colors range from yellow to red to purple.
The bark is grey and smooth with numerous lentils and some exfoliation.
Some further reading on this tree include North Carolina State, Virginia Tech, UCONN, University of Florida, and MOBOT.
I have mentioned this plant a few times earlier this year but wanted to share it in multiple stages. One can truly see the blackberry in the Blackberry Lily. Belamcanda chinensis - the seedpod certainly looks like a blackberry!
The recent rainfall lends itself to some nice photo shots. I love the raindrops hanging off the ever-maturing bud.
And the droplets beading up on the Edgeworthia...the large leaves are almost like velvet.
Two of the shrubs in my garden that shine this time of year are the two different Callicarpas. I bought a Callicarpa at a native plant sale and it turned out to have smaller leaves and smaller berries. Further investigation proved it to be the Callicarpa japonica (that they were also selling, this was mislabeled).
As you can see the americana variety has much larger leaves and the berries are larger and a little different shade of purple. These shrubs are edge of forest/ dappled sunshine loving plants. Mine have been in ground since 1999. About 4 years ago my neighbor had her pines taken out...about 5 or 6 large Loblolly pines. Since then, this section of my garden is in full sun and the Callicarpa seem to be doing just fine. Every other winter I cut them almost to the ground. As mature shrubs they are about 6-8 wide and about 5- 6 feet tall.
The Miscanthus sinensis 'Dixieland' is in bloom now... thought the dark skies through the seed heads was a good backdrop.
And now for the mystery. My sister suggested I do this in the form of a contest, though I am not sure how to prove one right or wrong... here is a 'volunteer' plant in my backyard. I never planted this plant, no one in the area grows this plant, soooo........HOW DO YOU THINK IT GOT HERE???
Know what this is??? A peanut! Arachis hypogea. But how...???
Sweetbay has IDed this as a Cassia obtusifolia which looks amazingly like a peanut. Still a mystery as to how it got here.
A little humor for the evening post viewers..... If I throw the ball for too long of a time Skyler gets overheated. He will take matters into his own 'hands'.
For all who are members of Blotanical.com, the voting is now open to choose from the top 5 in all sorts of categories. Thanks to all who put me into the top 5!
ps- I found I had a photo of the Koelreuteria bipinnata with the fall pink pods...so scroll down to see another photo in the posting.
*Note- update on the Littleleaf Linden post—Les I still believe you have a Littleleaf.
The Koelreuteria bipinnata (kole-roo-TEER-ee-uh bye-pih-NAY-tuh) is one of three Koelreuteria from Asia and is a small to medium sized tree. The leaves are bipinnate which means that it is a pinnate leave where each leaflet is then pinnate as well. See my very poorly drawn diagram. The leaflets are alternate, a dark green serrated ovate.
Common names for this tree are Bougainvillea Golden Rain Tree or Chinese Flame-tree. It has a spreading shaped growth habit, reaching heights 40- 60 feet. It can also be seen as a multi-stemmed specimen. It is throughout the South, hardy to zones 7- 10 and is a good neighborhood tree.
The yellow flowers appear mid to late summer on terminal panicles and are fragrant. Later two inch long 'Chinese lantern' shaped paper husked seed pods appear. These pods are pink and retain their color after drying. (Some use them in flower arranging) The leaves are yellow in the fall and with the pink lanterns make a very nice addition to the fall landscape.
This tree is self pollinating and can produce fertile seeds that will germinate. Some areas are looking at this as a possible invasive tree. We have this tree and the Koelreuteria paniculata in the Learning Garden and there are small seedlings in the area of these trees. They are easily pulled. Not sure why, but the paniculata didn't bloom this summer (or I missed it, which is unlikely). The bright yellow flowers are striking against the dark green leaves and this makes it a very nice specimen tree in the landscape.
Here are three places with more information about this pretty tree. Henderson State University , University of Florida and North Carolina State .
Sorry about the lateness in posting this ---an old senile dog last night wanted in and out and in and out and in and out….hard to concentrate.
Next week's tree is the Zelkova.
Yesterday Racquel and I went drove over the water (rather under the water, Hampton Roads Bridge Tunnel) to Norfolk to visit Les in his garden. What a great day! Les has a small garden but it is filled to the brim with interesting plant material. At his front walkway you are greeted by the Red Banana paired with Coleus.
On the other side of the front steps is another Coleus 'Alabama Sunset' paired with Jasminum officinalis, a hardy jasmine with wonderful golden foliage.
His backyard has some of the most interesting plants. One of these treasures is Tetrapanax papyrifera 'Steriodal Giant'. This plant looks prehistoric.
In his backyard is a Linden tree…which I was sure was a Littleleaf Linden, but after seeing a tree with small leaves at the Botanical Garden later in the day labeled Tilia Americana, now I am not so sure. More studying is in order!
A short drive from Les' house is the Norfolk Botanical Garden. Both Les and I had blogged about this garden in the spring when the Camellias were blooming. We strolled around the garden and had so many great plants to take in. I was especially happy with this photo of the Bird of Paradise in the Tropical Display House. There are so many unusual specimens.
From the Tropical Display House we ventured out in to the Sarah Lee Baker Perennial Garden. It was as if we were walking into a dream…..a gardeners dream!
There are blooms galore and some interesting insects to boot!
These berries are from a wonderful old crabapple tree. The variegated Ginger lily is very sculptural.
Look across the waterway, see the Tetrapanax like Les has in his yard?
Here is a dilemma for you…sun or shade? I was intrigued with this pairing –the Ginger lily needs sun and the Caladium are shade lovers. Both seem to be very happy under some very mature trees. Perhaps there is enough morning sun.
Too bad I already offered my ornamental grass photo for the contest…here are two nice combinations.
Jan at Always Growing shared Cat's Whiskers a little while ago…I was happy to see them in person. I love the tips with a touch of purple on the ends. It is a member of the mint family. It is listed by Jan as Orthosiphon aristatus.
The rose garden is in full splendor – each rose is prettier than the next. I really like this one with so many variations of pink on one bush. Of course we couldn't find a name for this one though we think it is one of the Fairy Rose cultivars.
What a great time walking through the gardens with a couple other plant lovers. It was a nice time getting to know both Les and Racquel a little bit better. We will have another mini-blogger get together again soon. As we left one of the Norfolk mermaids bid us farewell……………. I will be back soon!


text:
Последнее испытание
 Последнее испытание (мюзикл) — российский мюзикл, впервые презентованный в 1998 году.
 Последнее испытание (фильм) — российский фильм в жанре драмы.

redpajama_set_name: RedPajamaWikipedia


text:
There were several primary elections in the city and towns of the county that will be of interest to
Columbia County Democrats
There was only one Democratic Primary in all of Columbia County, that was in Hudson. There were two seats open for Alderman in Hudson's Common Council representing the 1st Ward. In a three way race for the two seats, Sarah Sterling has won the Democratic Primary. She also has the Independence Party line in November. Sarah served previously as 1st Ward Alderman in 2006 and 2007.
The second 1st Ward alderman seat is too close to call. Geeta Cheddie received 44 votes and Carole Osterink 42. There are a number of absentee votes. We will post results as soon as they become available.
Update: Geeta Cheddie has won the race for the second Alderman seat by one vote. The final vote total were Sterling 55, Cheddie 47 and Osterink 46. Congratulations to Ms. Sterling and Ms. Cheddie.
In the 4th Ward Supervisor race it appears that Supervisor William Hughes Jr has won the Independence Party line. Mr Hughes has previously been endorsed by the Hudson City Democratic Comittee and the Working Families Party
More at Hudson City Democrats
Town of Copake
Current Copake Town Justice Brian J. Herman won the Independence line with 19 votes to William Kane's 17 votes. Herman was also endorsed by the Democratic Party.
In the race for Assessor, Loredana Delventhal won the Independence Party line with 16 votes to current assessor David Gordineer's 14. Delventhal was also endorsed by the Democrats in her bid for town assessor.
More Info at Copake Democrats
Town of Taghkanic:
There were a number of races in Taghkanic where the Democratic candidate picked up additional lines on the ballot this November. Most of these were against long entrenched candidates.
In the 2009 Independence Party Primary election, Loretta Hoffman scored a decisive victory with 13 votes over incumbent town Supervisor Elizabeth Young's two. Hoffman also has the backing of the Taghkanic Democrats and the Working Families Party.
In the Independence Party race for Town Clerk/Tax Collector the vote was 10 votes for Audrey Koran with 5 for her challenger Cheryl E. Rogers
In the races for the 2 open town council seats. One position was won by Larry Kadish, with 13 votes. Joyce Thompson with 9 votes appears to have taken the 2nd seat on the Independence Party line. Mr. Kadish and Ms. Thompson have previously been nominated to run by the Taghkanic Democrats and will also appear on the Democratic line. The other candidates were Tom Keiley with 3 votes with 1 vote for Joe Romaine.
For Highway Superintendent Tom Youhas has won a slot on the Independence Party line. with 10 votes to Republican Edward Waldron's 3 votes. Mr. Youhas already also has the backing of the Taghkanic Democrats.
In the race for Taghkanic Democratic Committee, the winners scored a decisive victory. They were Gary L. Stoller, with 100 votes, and Diane M. Rodriguez, with 96 votes over Cheryl E. Rogers, the previous Chair of the Committee wiho received 29 votes.
More at Taghkanic Democrats
Labels: Carole Osterink, Copake, Diane Rodriguez, Election, Election Returns, Gary Stoller, Hudson, Primary, Sarah Sterling, Taghkanic, William Hughes
Labels: Columbia County, Columbia County Democrats, Healthcare, Hudson, NY, Public Opttion, Rally, Vigil