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    Autonomous agents must select action sets satisfying behavioral constraints. This study introduces Pareto-optimal feasible status sets (POSS) to address multiple valid options, enhancing decision-making for agents.

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    Area of Science:

    • Artificial Intelligence
    • Multi-agent Systems
    • Logic in Computer Science

    Background:

    • Autonomous agents require selecting action sets adhering to behavioral constraints.
    • Feasible Status Sets (FSS) represent action sets satisfying deontic logic constraints.
    • Existing methods face challenges when multiple FSSs exist, hindering agent action selection.

    Purpose of the Study:

    • To introduce the novel concept of Pareto-optimal Feasible Status Sets (POSS).
    • To develop methods for identifying and selecting optimal action sets for autonomous agents.
    • To address the challenge of multiple feasible solutions in agent decision-making.

    Main Methods:

    • Formalization of Pareto-optimal Feasible Status Sets (POSS).
    • Development of an algorithm to find POSS.
    • Design of specialized, more efficient algorithms for monotonic or anti-monotonic objective functions.

    Main Results:

    • Demonstrated that checking for POSS is co-NP-hard.
    • Developed a general algorithm for finding POSS.
    • Proposed efficient algorithms for specific objective function types, validated through experiments.

    Conclusions:

    • The POSS concept provides a robust framework for autonomous agent decision-making.
    • The developed algorithms offer effective solutions for identifying optimal action sets.
    • Future work will explore strategies for managing multiple Pareto-optimal solutions.