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A Survey of Opponent Modeling in Adversarial Domains
Author(s) -
Samer B. Nashed,
Shlomo Zilberstein
Publication year - 2022
Publication title -
journal of artificial intelligence research/the journal of artificial intelligence research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.79
H-Index - 123
eISSN - 1943-5037
pISSN - 1076-9757
DOI - 10.1613/jair.1.12889
Subject(s) - adversarial system , computer science , adversary , artificial intelligence , selection (genetic algorithm) , machine learning , stochastic game , theoretical computer science , mathematics , computer security , mathematical economics
Opponent modeling is the ability to use prior knowledge and observations in order to predict the behavior of an opponent. This survey presents a comprehensive overview of existing opponent modeling techniques for adversarial domains, many of which must address stochastic, continuous, or concurrent actions, and sparse, partially observable payoff structures. We discuss all the components of opponent modeling systems, including feature extraction, learning algorithms, and strategy abstractions. These discussions lead us to propose a new form of analysis for describing and predicting the evolution of game states over time. We then introduce a new framework that facilitates method comparison, analyze a representative selection of techniques using the proposed framework, and highlight common trends among recently proposed methods. Finally, we list several open problems and discuss future research directions inspired by AI research on opponent modeling and related research in other disciplines.

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