A Survey of Opponent Modeling in Adversarial Domains

DOI10.1613/jair.1.12889OpenAlexW4206439154MaRDI QIDQ5026260

Shlomo Zilberstein, Samer Nashed

Publication date: 7 February 2022

Published in: Journal of Artificial Intelligence Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1613/jair.1.12889

zbMATH Keywords

multiagent systemsmachine learningsoftware agentsgame playing

Mathematics Subject Classification ID

Artificial intelligence (68Txx)

Cites Work

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• Perspectives on multiagent learning
• Applying adversarial planning techniques to Go
• Probabilistic opponent-model search
• Autonomous agents modelling other agents: a comprehensive survey and open problems
• Successful Nash equilibrium agent for a three-player imperfect-information game
• Bayesian policy reuse
• A semi-Markov decision model for recognizing the destination of a maneuvering agent in real time strategy games
• Limited lookahead in imperfect-information games
• Expecting the unexpected: goal recognition for rational and irrational agents
• A survey of inverse reinforcement learning: challenges, methods and progress
• Landmark-based approaches for goal recognition as planning
• A survey on semi-supervised feature selection methods
• How much does it help to know what she knows you know? An agent-based simulation study
• The Daikon system for dynamic detection of likely invariants
• Formalizing opponent modeling with the rock, paper, scissors game
• Location-Based Reasoning about Complex Multi-Agent Behavior
• Exploiting Model Equivalences for Solving Interactive Dynamic Influence Diagrams
• A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems
• The minimum consistent DFA problem cannot be approximated within any polynomial
• Complexity of automaton identification from given data
• Conditioning and Intervening
• Recognizing Planned, Multiperson Action
• OMBO: An opponent modeling approach
• On the Synthesis of Finite-State Machines from Samples of Their Behavior
• The Complexity of Decentralized Control of Markov Decision Processes
• A Card Game With Bluffing

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