Strategy Generation and Evaluation for Meta-Game Playing

Pell, Barney

doi:10.1007/s13218-010-0082-7

Cited by 25 publications

(22 citation statements)

References 37 publications

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“…10). The range of AI-assisted game design tools rule from those designed to assist with generation on complete game rulesets such as MetaGame [124] or RuLearn [125] to those focused on more specific domains such as strategy game levels [37], platform game levels [126] or physics-based puzzles [127].…”

Section: H Ai-assisted Game Designmentioning

confidence: 99%

A Panorama of Artificial and Computational Intelligence in Games

Yannakakis

Togelius

2015

IEEE Trans. Comput. Intell. AI Games

134

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Abstract-This paper attempts to give a high-level overview of the field of artificial and computational intelligence (AI/CI) in games, with particular reference to how the different core research areas within this field inform and interact with each other, both actually and potentially. We identify ten main research areas within this field: NPC behavior learning, search and planning, player modeling, games as AI benchmarks, procedural content generation, computational narrative, believable agents, AI-assisted game design, general game artificial intelligence and AI in commercial games. We view and analyze the areas from three key perspectives: (1) the dominant AI method(s) used under each area; (2) the relation of each area with respect to the end (human) user; and (3) the placement of each area within a human-computer (player-game) interaction perspective. In addition, for each of these areas we consider how it could inform or interact with each of the other areas; in those cases where we find that meaningful interaction either exists or is possible, we describe the character of that interaction and provide references to published studies, if any. We believe that this paper improves understanding of the current nature of the game AI/CI research field and the interdependences between its core areas by providing a unifying overview. We also believe that the discussion of potential interactions between research areas provides a pointer to many interesting future research projects and unexplored subfields.

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Section: H Ai-assisted Game Designmentioning

confidence: 99%

A Panorama of Artificial and Computational Intelligence in Games

Yannakakis

Togelius

2015

IEEE Trans. Comput. Intell. AI Games

134

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“…The idea of general game playing as a test-bed for AI is presented in Barney Pell's thesis (Pell 1993). Pell submits general game playing as a way to preserve the elements that have made game-playing research attractive to the AI community, while maintaining generality.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Evaluation Functions for General Game Playing

Clune

2011

Künstl Intell

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A general game playing program plays games that it has not previously encountered. A game manager program sends the game playing programs a description of a game's rules and objectives in a well-defined game description language. A central challenge in creating effective general game playing programs is that of constructing heuristic evaluation functions from game descriptions. This paper describes a method for constructing evaluation functions that represent exact values of simplified games. The simplified games are abstract models that incorporate the most essential aspects of the original game, namely payoff, control, and termination. Results of applying this method to a sampling of games suggest that heuristic evaluation functions based on our method are both comprehensible and effective.

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“…Identified as a Grand Challenge for AI, this endeavour requires to combine methods from a variety of sub-disciplines, including automated reasoning, search, game playing, and learning [9,5,3,11,2].…”

Section: Introductionmentioning

confidence: 99%

Pushing the Envelope: General Game Players Prove Theorems

Haufe

Thielscher

2010

AI 2010: Advances in Artificial Intelligence

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Abstract.A general game player is a system that can play previously unknown games given nothing but their rules. A key to success in this endeavour is the ability to automatically gain knowledge about new games that follows from the rules without being explicitly given. In this paper, we show how a recently developed, theoretical method for automated theorem proving in general game playing can be put into practice. To this end, we extend the method so as to allow a general game player to systematically search and verify multiple temporal game properties at once. We formally prove this extension to be correct, and we report on extensive experiments that show how this improvement helps to significantly enhance the ability of a successful general game player to infer new properties about a previously unknown game.

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Strategy Generation and Evaluation for Meta-Game Playing

Cited by 25 publications

References 37 publications

A Panorama of Artificial and Computational Intelligence in Games

A Panorama of Artificial and Computational Intelligence in Games

Heuristic Evaluation Functions for General Game Playing

Pushing the Envelope: General Game Players Prove Theorems

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