Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems 2006
DOI: 10.1145/1160633.1160907
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Asynchronous chess competition

Abstract: Asynchronous Chess (AChess) is a platform for the development and evaluation of real-time adversarial agent technologies. It is a two-player game using the basic rules of chess with the modification that agents may move as many pieces as they want at any time. Modifying chess in this way creates a new robust, asynchronous, real-time game in which agents must carefully balance their time between reasoning and acting in order to out-perform their opponent. As a fast-paced adversarial game, many challenges releva… Show more

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Cited by 3 publications
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“…As discussed in Chapter 3, our implementation of a Sokoban game server is based the Asynchronous Chess server [8], modified for the Sokoban domain to support synchronized actions. The running example (Figure 4.1, repeated from Figure 2.1) demonstrates the 2-agent case.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…As discussed in Chapter 3, our implementation of a Sokoban game server is based the Asynchronous Chess server [8], modified for the Sokoban domain to support synchronized actions. The running example (Figure 4.1, repeated from Figure 2.1) demonstrates the 2-agent case.…”
Section: Resultsmentioning
confidence: 99%
“…Figure 2.1 shows an example of subgoal hierarchies for a simple Sokoban scenario. Our implementation of a Sokoban game server is based the Asynchronous Chess server [8], modified for the Sokoban domain to support synchronized actions. The pawns represent the respective movers which belong to each agent.…”
Section: Coordinating Temporal Consistencymentioning
confidence: 99%
“…RL-SANE and our other work in reinforcement learning came about from the desire to develop an agent capable of learning how to play Asynchronous Chess [12]. Asychronous Chess, or AChess, is an agent technology evaluation platform developed in-house.…”
Section: Reinforcement Learning For Rapid Decision Making In Real-timmentioning
confidence: 99%