Abstract
We present a novel approach to reducing adversarial search space by employing background knowledge represented in the form of higher-level goals that players tend to pursue in the game. The algorithm is derived from a simultaneous-move modification of the max n algorithm by limiting the search to those branches of the game tree that are consistent with pursuing player’s goals. The algorithm has been tested on a real-world-based scenario modelled as a large-scale asymmetric game. The experimental results obtained indicate the ability of the goal-based heuristic to reduce the search space to a manageable level even in complex domains while maintaining the high quality of resulting strategies.
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Lisý, V., Bošanský, B., Jakob, M., Pěchouček, M. (2010). Goal-Based Game Tree Search for Complex Domains. In: Filipe, J., Fred, A., Sharp, B. (eds) Agents and Artificial Intelligence. ICAART 2009. Communications in Computer and Information Science, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11819-7_8
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DOI: https://doi.org/10.1007/978-3-642-11819-7_8
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