Multi-Agent Intention Progression with Black-Box Agents

Michael Dann, Yuan Yao, Brian Logan, John Thangarajah

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    Abstract

    We propose a new approach to intention progression in multi-agent settings where other agents are effectively black boxes. That is, while their goals are known, the precise programs used to achieve these goals are not known. In our approach, agents use an abstraction of their own program called a partially-ordered goal-plan tree (pGPT) to schedule their intentions and predict the actions of other agents. We show how a pGPT can be derived from the program of a BDI agent, and present an approach based on Monte Carlo Tree Search (MCTS) for scheduling an agent's intentions using pGPTs. We evaluate our pGPT-based approach in cooperative, selfish and adversarial multi-agent settings, and show that it out-performs MCTS-based scheduling where agents assume that other agents have the same program as themselves
    Original languageEnglish
    Title of host publicationProceedings of the Thirtieth International Joint Conference on Artificial Intelligence
    EditorsZhi-Hua Zhou
    Publisherijcai.org
    Pages132-138
    Number of pages7
    DOIs
    Publication statusPublished - Aug 2021

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    Keywords

    • Agent-based and Multi-agent Systems: Agent Theories and Models
    • Agent-based and Multi-agent Systems: Engineering Methods, Platforms, Languages and Tools

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