Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger
This paper we present a defogger, a model that learns to predict future hidden information from partial observations. We formulate this model in the context of forward modeling and leverage spatial and sequential constraints and correlations via convolutional neural networks and long short-term memory networks, respectively. We evaluate our approach on a large dataset of human games of StarCraft: Brood War, a real-time strategy video game. Our models consistently beat strong rule-based baselines and qualitatively produce sensible future game states.
 The paper considers a problem of predicting hidden information in a poMDP with an application to Starcraft. Authors propose a number of baseline models as well as metrics to assess the quality of “defogging”. I find the problem of defogging quite interesting, even though it is a bit too Starcraft-specific some findings could perhaps be translated to other partially observed environments. Authors use the dataset provided for Starcraft: Brood war by Lin et al, 2017. My impression about the paper is that even though it touches a very interesting problem, it neither is written well nor it contains much of a novelty in terms of algorithms, methods or network architectures. Detailed comments: * Authors should at very least cite (Vinyals et al, 2017) and explain why the environment and the dataset released for Starcraft 2 is less suited than the one provided by Lin et al. * Problem statement in section 3.1 should certainly be improved. Authors introduce rather heavy notation which is then used in a confusing way. For example, what is the top index in supposed to mean? The notation is not much used after sec. 3.1, for example, figure 1 does not use it. * A related issue, is that the definition of metrics is very informal and, again, does not use the already defined notation. Including explicit formulas would be very helpful, because, for example, it looks like when reported in table 1 the metrics are spatially averaged, yet I could not find an explicit notion of that. * Authors seem to only consider deterministic defogging models. However, to me it seems that even in 15 game steps the uncertainty over the hidden state is quite high and thus any deterministic model has a very limited potential in prediction it. At least the concept of stochastic predictions should be discussed * The rule-based baselines are not described in detail. What does “using game rules to infer the existence of unit types” mean? * Another detail which I found missing is whether authors use just a screen, a mini-map or both. In the game of Starcraft, only screen contains information about unit-types, but it’s field of view is limited. Hence, it’s unclear to me whether a model should infer hidden information based on just a single screen + minimap observation (or a history of them) or due to how the dataset is constructed, all units are observed without spatial limitations of the screen.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger
This paper we present a defogger, a model that learns to predict future hidden information from partial observations. We formulate this model in the context of forward modeling and leverage spatial and sequential constraints and correlations via convolutional neural networks and long short-term memory networks, respectively. We evaluate our approach on a large dataset of human games of StarCraft: Brood War, a real-time strategy video game. Our models consistently beat strong rule-based baselines and qualitatively produce sensible future game states.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 The paper considers a problem of predicting hidden information in a poMDP with an application to Starcraft. Authors propose a number of baseline models as well as metrics to assess the quality of “defogging”. I find the problem of defogging quite interesting, even though it is a bit too Starcraft-specific some findings could perhaps be translated to other partially observed environments. Authors use the dataset provided for Starcraft: Brood war by Lin et al, 2017. My impression about the paper is that even though it touches a very interesting problem, it neither is written well nor it contains much of a novelty in terms of algorithms, methods or network architectures. Detailed comments: * Authors should at very least cite (Vinyals et al, 2017) and explain why the environment and the dataset released for Starcraft 2 is less suited than the one provided by Lin et al. * Problem statement in section 3.1 should certainly be improved. Authors introduce rather heavy notation which is then used in a confusing way. For example, what is the top index in supposed to mean? The notation is not much used after sec. 3.1, for example, figure 1 does not use it. * A related issue, is that the definition of metrics is very informal and, again, does not use the already defined notation. Including explicit formulas would be very helpful, because, for example, it looks like when reported in table 1 the metrics are spatially averaged, yet I could not find an explicit notion of that. * Authors seem to only consider deterministic defogging models. However, to me it seems that even in 15 game steps the uncertainty over the hidden state is quite high and thus any deterministic model has a very limited potential in prediction it. At least the concept of stochastic predictions should be discussed * The rule-based baselines are not described in detail. What does “using game rules to infer the existence of unit types” mean? * Another detail which I found missing is whether authors use just a screen, a mini-map or both. In the game of Starcraft, only screen contains information about unit-types, but it’s field of view is limited. Hence, it’s unclear to me whether a model should infer hidden information based on just a single screen + minimap observation (or a history of them) or due to how the dataset is constructed, all units are observed without spatial limitations of the screen.
assistant
 The paper considers a problem of predicting hidden information in a poMDP with an application to Starcraft. Authors propose a number of baseline models as well as metrics to assess the quality of “defogging”. I find the problem of defogging quite interesting, even though it is a bit too Starcraft-specific some findings could perhaps be translated to other partially observed environments. Authors use the dataset provided for Starcraft: Brood war by Lin et al, 2017. My impression about the paper is that even though it touches a very interesting problem, it neither is written well nor it contains much of a novelty in terms of algorithms, methods or network architectures. Detailed comments: * Authors should at very least cite (Vinyals et al, 2017) and explain why the environment and the dataset released for Starcraft 2 is less suited than the one provided by Lin et al. * Problem statement in section 3.1 should certainly be improved. Authors introduce rather heavy notation which is then used in a confusing way. For example, what is the top index in supposed to mean? The notation is not much used after sec. 3.1, for example, figure 1 does not use it. * A related issue, is that the definition of metrics is very informal and, again, does not use the already defined notation. Including explicit formulas would be very helpful, because, for example, it looks like when reported in table 1 the metrics are spatially averaged, yet I could not find an explicit notion of that. * Authors seem to only consider deterministic defogging models. However, to me it seems that even in 15 game steps the uncertainty over the hidden state is quite high and thus any deterministic model has a very limited potential in prediction it. At least the concept of stochastic predictions should be discussed * The rule-based baselines are not described in detail. What does “using game rules to infer the existence of unit types” mean? * Another detail which I found missing is whether authors use just a screen, a mini-map or both. In the game of Starcraft, only screen contains information about unit-types, but it’s field of view is limited. Hence, it’s unclear to me whether a model should infer hidden information based on just a single screen + minimap observation (or a history of them) or due to how the dataset is constructed, all units are observed without spatial limitations of the screen.