A model of visual organization for the game of GO

Zobrist, A. L.

doi:10.1145/1476793.1476819

Cited by 36 publications

(20 citation statements)

References 2 publications

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“…Once calculated, each influence map relates some spatial feature or concept determined by the IMFunction. Influence maps evolved out of work done on spatial reasoning within the game of Go and have been used sporadically since then in various games such as Age of Empires [11], [16]. In an RTS game the IMFunction might be a summation of the natural resources present in that square, the distance to the closest enemy, or the number of friendly units in the vicinity.…”

Section: A Influence Mapsmentioning

confidence: 99%

Co-Evolving Influence Map Tree Based Strategy Game Players

Miles

Quiroz

Leigh

et al. 2007

2007 IEEE Symposium on Computational Intelligence and Games

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Abstract-We investigate the use of genetic algorithms to evolve AI players for real-time strategy games. To overcome the knowledge acquisition bottleneck found in using traditional expert systems, scripts, or decision trees we evolve players through co-evolution. Our game players are implemented as resource allocation systems. Influence map trees are used to analyze the game-state and determine promising places to attack, defend, etc. These spatial objectives are chained to non-spatial objectives (train units, build buildings, gather resources) in a dependency graph. Players are encoded within the individuals of a genetic algorithm and co-evolved against each other, with results showing the production of strategies that are innovative, robust, and capable of defeating a suite of hand-coded opponents.

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Section: A Influence Mapsmentioning

confidence: 99%

Co-Evolving Influence Map Tree Based Strategy Game Players

Miles

Quiroz

Leigh

et al. 2007

2007 IEEE Symposium on Computational Intelligence and Games

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show abstract

“…Influence maps evolved out of work done on spatial reasoning within the game of Go and have been used sporadically since then in games such as Age of Empires [16], [11]. Influence maps combine together to form spatial decision making strategies.…”

Section: A Influence Mapsmentioning

confidence: 99%

“…We combine IM's within a tree structure instead of the traditional list [16]. Each tree represents a complete decision making strategy, and is encoded within an individual in a genetic algorithm.…”

Section: B Influence Map Combinationsmentioning

confidence: 99%

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Towards the Co-Evolution of Influence Map Tree Based Strategy Game Players

Miles

Louis

2006

2006 IEEE Symposium on Computational Intelligence and Games

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Abstract-We investigate the use of genetic algorithms to play real-time computer strategy games. To overcome the knowledge acquisition bottleneck found in using traditional expert systems, scripts, or decision trees we use genetic algorithms to evolve game players. The spatial decision makers in our game players use influence maps as a basic building block from which they construct and evolve trees containing complex game playing strategies. Information from influence map trees is combined with that from an A* pathfinder, and used by another genetic algorithm to solve the allocation problems present within many game decisions. As a first step towards evolving strategic players we develop this system in the context of a tactical game. Results show the co-evolution of coordinated attacking and defending strategies superior to their hand-coded counterparts.

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“…This technique is based roughly upon a map weighting technique (Zobrist 1969). The algorithm starts by allocating a grid array (with the same dimensions as the map) and initializing this array to zero at all locations.…”

Section: Strategic Influencementioning

confidence: 99%

Evolving command, control, communications, computers, and Intelligence, Surveillance and Reconnaissance (C4ISR) Decisions

Porto¹,

Fogel²

2002

PsycEXTRA Dataset

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, end completing and reviewing the collection of information. 12b. DISTRIBUTION CODE ABSTRACT (Maximum 200 wordslThis report details the investigation, development, and prototype demonstration of evolving C4ISR capabilities of aircraft in an arbitrary environment. Natural Selection, Inc. (NSI) was tasked to show how the application of the Valuated State Space Approach and evolutionary programming (EP) could be used to produce optimal behavioral plans, and develop a set of requirements for implementing this capability within an existing (or near-future) simulation engine. As a step toward the creation of an actual prototype system, a small prototype optimal planning mechanism was created using computationally intelligent techniques. A long-term goal is to develop a generalized, extensible evolutionary software system which can efficiently and effectively encompass all desired hierarchical levels of planning for air combat entities, and can be used as a tactical decision aid or training mechanism.

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A model of visual organization for the game of GO

Cited by 36 publications

References 2 publications

Co-Evolving Influence Map Tree Based Strategy Game Players

Co-Evolving Influence Map Tree Based Strategy Game Players

Towards the Co-Evolution of Influence Map Tree Based Strategy Game Players

Evolving command, control, communications, computers, and Intelligence, Surveillance and Reconnaissance (C4ISR) Decisions

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