Learning from Demonstration and Case-Based Planning for Real-Time Strategy Games

Ontañón, Santiago; Mishra, Kinshuk; Sugandh, Neha; Ram, Ashwin

doi:10.1007/978-3-540-77465-5_15

Cited by 26 publications

(22 citation statements)

References 6 publications

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“…In the area of robotics, CBR has been used typically for high level planning [22] such as behavior and action selection [23], [24]. In this paper, however, CBR is used for learning low level behaviors.…”

Section: B Case-based Reasoningmentioning

confidence: 99%

Implicit robot coordination using Case-Based Reasoning behaviors

Peula

García

Herrero

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Multi-robot systems (MRS) are a very active and important research topic nowadays. One of the main problems of these systems is the large number of variables to take into account. Due to this, robot behaviors are sometimes learnt instead of calculated via analytical expressions. A typical learning mechanism, specially for biomimetic robots, is Learning from demonstration (LfD). This paper proposes a LfD approach for implicit coordinated navigation using combination of CaseBased Reasoning (CBR) behaviors. During a training stage, CBR is used to learn simple behaviors that associate positions of other robots and/or objects to motion commands for each robot. Thus, human operators only need to concentrate on achieving their robot's goal as efficiently as possible in the operating conditions. Then, in running stage, each robot will achieve a different coordinate navigation strategy depending on the triggered behaviors. This system has been successfully tested with three Aibo-ERS7 robots in a RobCup-like environment.

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Section: B Case-based Reasoningmentioning

confidence: 99%

Implicit robot coordination using Case-Based Reasoning behaviors

Peula

García

Herrero

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…For example Ontañón et al [9] explored the use of real-time case-based planning (CBP) in the domain of Wargus (a Warcraft II clone). In their work, they used human demonstration to learn plans, which are then composed at run-time in order to form full-fledges strategies to play the game.…”

Section: A Strategymentioning

confidence: 99%

A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft

Ontañón

Synnaeve

Uriarte

et al. 2013

IEEE Trans. Comput. Intell. AI Games

330

269

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Abstract-This paper presents an overview of the existing work on AI for real-time strategy (RTS) games. Specifically, we focus on the work around the game StarCraft, which has emerged in the past few years as the unified test-bed for this research. We describe the specific AI challenges posed by RTS games, and overview the solutions that have been explored to address them. Additionally, we also present a summary of the results of the recent StarCraft AI competitions, describing the architectures used by the participants. Finally, we conclude with a discussion emphasizing which problems in the context of RTS game AI have been solved, and which remain open.

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“…The main methods used to these ends are case-based reasoning (CBR) and planning or plan recognition [5], [6], [7], [8], [9]. There are precedent works of Bayesian plan recognition [10], even in games with Albrecht et al [11] using dynamic Bayesian networks to recognize a user's plan in a multi-player dungeon adventure.…”

Section: A Related Workmentioning

confidence: 99%

“…Ontañón et al [6] base their real-time case-based planning (CBP) system on a plan dependency graph which is learned from human demonstration. In [7], [12], they use CBR and expert demonstrations on Wargus.…”

Section: A Related Workmentioning

confidence: 99%

A Bayesian model for opening prediction in RTS games with application to StarCraft

Synnaeve

Bessìère

2011

2011 IEEE Conference on Computational Intelligence and Games (CIG'11)

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Abstract-This paper presents a Bayesian model to predict the opening (first strategy) of opponents in real-time strategy (RTS) games. Our model is general enough to be applied to any RTS game with the canonical gameplay of gathering resources to extend a technology tree and produce military units and we applied it to StarCraft 1 . This model can also predict the possible technology trees of the opponent, but we will focus on openings here. The parameters of this model are learned from replays (game logs), labeled with openings. We present a semisupervised method of labeling replays with the expectationmaximization algorithm and key features, then we use these labels to learn our parameters and benchmark our method with cross-validation. Uses of such a model range from a commentary assistant (for competitive games) to a core component of a dynamic RTS bot/AI, as it will be part of our StarCraft AI competition entry bot.

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Learning from Demonstration and Case-Based Planning for Real-Time Strategy Games

Cited by 26 publications

References 6 publications

Implicit robot coordination using Case-Based Reasoning behaviors

Implicit robot coordination using Case-Based Reasoning behaviors

A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft

A Bayesian model for opening prediction in RTS games with application to StarCraft

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