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Natural Strategic Ability in Stochastic Multi-Agent Systems

AAAI 2024 · Multi-agent systems and games › Games and equilibria · approach: Learning with strategic agents · 1 citations (OpenAlex)

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Extends probabilistic ATL variants (PATL and PATL*) to natural strategies (condition-action lists with bounded memory) for stochastic multi-agent systems and gives model-checking complexity results for memoryless and recall settings.

Problem

Model checking strategic abilities of coalitions in stochastic multi-agent systems when strategies must be natural (bounded-memory condition-action lists) rather than combinatorial, since existing logics require infinite memory or have high complexity.

Approach

Introduces probabilistic temporal logics Nat-PATL and Nat-PATL* whose coalition operators quantify over behavioral natural strategies of bounded complexity, and derives complexity bounds for deterministic and probabilistic natural strategies with memoryless and recall settings, plus expressivity incomparability with PATL.

Key results

Surprising findings

Limitations

Experimental setup

Metrics: model checking complexity class expressive power distinguishing power

Baselines: PATL PATL*

Builds on: PATL PATL* ATL with natural strategies PSL Nat-SL

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