Modeling a multi-segment war game leveraging machine intelligence with EVE structures

Ying Zhao; Bruce Nagy

doi:10.1117/12.2561855

18 May 2020 Modeling a multi-segment war game leveraging machine intelligence with EVE structures

Ying Zhao, Bruce Nagy

Proceedings Volume 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II; 114131V (2020) https://doi.org/10.1117/12.2561855
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

The paper depicts a generic representation of a multi-segment war game leveraging machine intelligence with two opposing asymmetrical players. We show an innovative Event-Verb-Event (EVE) structure that is used to represent small pieces of knowledge, actions, and tactics. We show the war game paradigm and related machine intelligence techniques, including data mining, machine learning, and reasoning AI which have a natural linkage to causal learning, which can be applied for this game. We also show specifically a rule-based reinforcement learning algorithm, i.e., Soar-RL, which can modify, link, and combine a large collection EVE rules, which represent existing and new knowledge, to optimize the likelihood to win or lose a game in the end.

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Citation Download Citation

Ying Zhao and Bruce Nagy "Modeling a multi-segment war game leveraging machine intelligence with EVE structures", Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 114131V (18 May 2020); https://doi.org/10.1117/12.2561855

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