Leveraging domain knowledge to learn normative behavior: A bayesian approach

Hadi Hosseini, Mihaela Ulieru

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Scopus citations

    Abstract

    This paper addresses the problem of norm adaptation using Bayesian reinforcement learning. We are concerned with the effectiveness of adding prior domain knowledge when facing environments with different settings as well as with the speed of adapting to a new environment. Individuals develop their normative framework via interaction with their surrounding environment (including other individuals). An agent acquires the domain-dependent knowledge in a certain environment and later reuses them in different settings. This work is novel in that it represents normative behaviors as probabilities over belief sets. We propose a two-level learning framework to learn the values of normative actions and set them as prior knowledge, when agents are confident about them, to feed them back to their belief sets. Developing a prior belief set about a certain domain can improve an agent's learning process to adjust its norms to the new environment's dynamics. Our evaluation shows that a normative agent, having been trained in an initial environment, is able to adjust its beliefs about the dynamics and behavioral norms in a new environment. Therefore, it converges to the optimal policy more quickly, especially in the early stages of learning.

    Original languageEnglish (US)
    Title of host publicationAdaptive and Learning Agents - International Workshop, ALA 2011, Held at AAMAS 2011, Revised Selected Papers
    Pages70-84
    Number of pages15
    DOIs
    StatePublished - 2012
    Event2011 Adaptive and Learning Agents Workshop, ALA 2011, Held at Autonomous Agents and Multi-Agent Systems Conference, AAMAS 2011 - Taipei, Taiwan, Province of China
    Duration: May 2 2011May 2 2011

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume7113 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference2011 Adaptive and Learning Agents Workshop, ALA 2011, Held at Autonomous Agents and Multi-Agent Systems Conference, AAMAS 2011
    CountryTaiwan, Province of China
    CityTaipei
    Period5/2/115/2/11

    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • Computer Science(all)

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