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

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Authors
Zhao, Ying
Nagy, Bruce
Subjects
Advisors
Date of Issue
2020
Date
2020
Publisher
SPIE
Language
en_US
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.
Type
Article
Description
The article of record as published may be found at https://doi.org/10.1117/12.2561855
Series/Report No
Department
Organization
Naval Postgraduate School
Identifiers
NPS Report Number
Sponsors
Funder
Format
12 p.
Citation
Ying Zhao, 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)
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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