Reasoning about knowledge
Collaborative plans for complex group action
Artificial Intelligence
Opponent Modeling in Multi-Agent Systems
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
Learning and Exploiting Relative Weaknesses of Opponent Agents
Autonomous Agents and Multi-Agent Systems
Introduction to Group Work Practice (with MyHelpingLab), An (5th Edition)
Introduction to Group Work Practice (with MyHelpingLab), An (5th Edition)
Supporting collaborative activity
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Incorporating opponent models into adversary search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
An Empirical Investigation of the Adversarial Activity Model
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
The adversarial activity model for bounded rational agents
Autonomous Agents and Multi-Agent Systems
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Multiagent environments are often not cooperative nor collaborative; in many cases, agents have conflicting interests, leading to adversarial interactions. This paper presents a formal Adversarial Environment model for bounded rational agents operating in a zero-sum environment. In such environments, attempts to use classical utility-based search methods can raise a variety of difficulties (e.g., implicitly modeling the opponent as an omniscient utility maximizer, rather than leveraging a more nuanced, explicit opponent model). We define an Adversarial Environment by describing the mental states of an agent in such an environment. We then present behavioral axioms that are intended to serve as design principles for building such adversarial agents. We explore the application of our approach by analyzing log files of completed Connect-Four games, and present an empirical analysis of the axioms' appropriateness.