1. Field of the Invention
The present invention relates to an interaction device which permits more close interaction between robot and user.
2. Description of Related Art
The concept of the idea of Infomax in perception is retroactive to the study by Linsker, etc. al (e.g., see non-Patent Literatures 2, 12).
This approach has been generalized as non-Gaussian circuit network by Bell and Sejnowski so that Infomax ICA is provided. The Infomax constitutes the important theoretical tool in the computational neuro-science. However, the greater part of this study is viewed from passive point of view of Infomax. The goal of the Infomax processor is to easily send information as many as possible relating to input to the next processing stage. On the other hand, the Infomax control acts together with active processor which can schedule action or behavior in order to discover hypothesis of interest as quickly as possible. For example, neuron can be also rapidly increased in order to discover the relation state with the world as quickly as possible through feedback connection instead of simply transmitting information. Similar inductive concept relating to active role of neuron has been formularized in the form of epicurean neuron hypothesis by Kupuroph.
The problem to schedule behavior in order to discover contingency is related to two-armed bandit problem from a formal point of view. In the classical two-armed problem, in order to determine which one of two levers maximizes the return speed, it is necessary to pull or draw either of two levers. We have amended the problem so that a supplemental hidden variable H for determining whether two conditions thereof are equal or not is included. Accordingly, in the two-armed problem, the goal is to determine which of two arms is superior, whereas in the contingency search problem, the goal is to determine whether or not there is a difference between two-arms. Such a delicate difference has important result. For example, in the standard two-armed bandit problem, only one arm is pulled or drawn plural times to thereby have an ability to make determination or judgment. This is because in the case where one of two arms brings about large return, this fact is the evidence that corresponding arm is already superior. However, in the contingency determination problem, information is unable to be obtained until two arms are pulled at least once.
Hitherto, various proposals are made in connection with the Infomax approaches with respect to perceive process or neuron process (e.g., see non-Patent Literatures 2, 11, 12, 20).
However, these models are passive in that design is made such that information is transmitted to the next processing stage. Instead, model which selects, with elapse of time, behavior for maximizing long-term gathering or collection of information is emphasized here.
The Infomax control can be also found in the tradition of the model of human motion control which explains the behavior of optimization problem (e.g., see non-Patent Literatures 5, 7, 13, 27)
However, the approach proposed here has inherent characteristic in that it can be applied to social action in which levels of scale in terms of time and uncertainty are far greater than the case of the traditional motion control problem.
Moreover, the ideas of information maximization to explain how people choose questions in concept learning tasks were used (see non-Patent Literatures 16, 17, 18).
Infomax-style control has also been proposed to understand how people move their eyes, or how an active camera could be moved to maximize data gathering about events of interest (e.g., see non-Patent Literatures 4, 8, 9, 10, 21).
However, these models do not address the following crucial issues:
(1) Previous models focused on explaining the ordering of behavior did not address the timing of behavior. For example, in [the non-Patent Literatures 16, 17, 18], concept learning is considered as an strict turn-taking activity in which subjects ask questions and given answers with no time constrains to have ability to give answer.
(2) Previous models did not solve the information maximization problem being raised. The models are “greedy” at best, and non causal at worst. For example, in [the non-Patent Literature 17], questions are asked that maximize the immediate information return, rather than the information returned in the long run.
In [the non-Patent Literature 10], the observer is allowed to first make all possible eye movements and then choose the eye movement that happened to provide the most information. This approach while useful for modeling purposes, is of non causal, i.e., generating the current saccade requires actually seeing the future.
In the field of robot technology, it has become popular to differentiate between behavioral and cognitive robot architectures (e.g., see non-Patent Literature 1).
Behavioral architectures are based on direct mappings between sensors and actuators. They emphasize the role of timing, and rapid reaction to changes in the environment. Cognitive architectures typically rely on planning and deliberative process, and the construction of world representations. Without proper mathematical grounding, concepts such as representation, deliberation and knowledge become almost meaningless. For example, the Infomax control framework presented here is reactive in that the controller is simply a causal mapping between sensor information and behaviors.
The idea of Infomax control is directly related to Bayesian approaches to sequential decision processes, and in particular to Bayesian solution or approaches to the n-armed bandit problem. The contribution in this paper is to show how this well known family of problems can be adapted to understand real time social interactions, and that mutual information can be used as a valid reinforcement signal.
Game theory, which can be regarded as a special case of the control theory, has a long history of applications to human social behavior, particularly in economics, and in the study of conflict. However, the importance of control to understand real time social behavior has been only recently appeared in the literature. The inventor, et al pointed out the potential value of stochastic optimal control, in particular of n-bandit problems, to understand the optimality of real time social interaction in [the non-Patent Literature 15]. Wolpert, Doya and Kawato proposed a unified framework for motion control and social interaction. Miyashita and Ishiguro pointed out that simple PID controllers can be used to produce communicative behaviors.
The simple social interactions will now be described.