Bootstrapping the Learning Process for the Semi-automated Design of a Challenging Game AI

Charles Madeira 1 Vincent Corruble 2 Geber Ramalho Bohdana Ratitch
1 ACASA - Agents Cognitifs et Apprentissage Symbolique Automatique
LIP6 - Laboratoire d'Informatique de Paris 6
2 SMA - Systèmes Multi-Agents
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : This paper proposes a methodology for the semi-automated design of a game AI for simulation and strategy games which require the player to control a potentially high number of characters or units in complex environments. After defending the idea of using Machine Learning, and especially Reinforcement Learning, as a basic technique, some of the key issues, mostly dealing with complexity, representation, and coordination, are outlined, and some ways forward are proposed. These revolve mainly around the idea of problem decomposition using the game structure, and of bootstrapping the learning process by letting the learning game AI play against another AI, and only progressively take more and more control of the decision-making. The ongoing application of this methodology to the design of a new game AI for an existing wargame system is described in some detail.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01520576
Contributor : Lip6 Publications <>
Submitted on : Wednesday, May 10, 2017 - 4:04:09 PM
Last modification on : Thursday, March 21, 2019 - 2:32:31 PM

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  • HAL Id : hal-01520576, version 1

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Charles Madeira, Vincent Corruble, Geber Ramalho, Bohdana Ratitch. Bootstrapping the Learning Process for the Semi-automated Design of a Challenging Game AI. AAAI-04 Workshop on Challenges in Game AI, Jul 2004, San Jose, CA, United States. pp.72-76. ⟨hal-01520576⟩

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