When Natural Strategies Meet Fuzziness and Resource-Bounded Actions (Extended Version)

2025-12-23

AI Summary

Existing logical frameworks for strategic reasoning in multi-agent systems (MAS) assume unbounded computational resources, zero action costs, and deterministic environments—diverging sharply from human decision-making under cognitive, perceptual, and resource constraints. Method: We propose HumanATLF, the first temporal logic integrating natural strategies (regularity-guided finite-memory policies), fuzzy temporal semantics (to formalize perceptual uncertainty), and non-renewable resource constraints (to model action costs). The framework jointly captures cognitive succinctness, perceptual fuzziness, and execution economy. Contribution/Results: We design model-checking algorithms for HumanATLF with complexities in P, NP-complete, and Δ₂^P-complete classes, and integrate them into the VITAMIN toolchain. Empirical evaluation in a resource-sensitive adversarial drone rescue scenario demonstrates significant improvements in strategy interpretability and real-world adaptability.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Reasoning under Uncertainty: Sequential Decision Making
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In formal strategic reasoning for Multi-Agent Systems (MAS), agents are typically assumed to (i) employ arbitrarily complex strategies, (ii) execute each move at zero cost, and (iii) operate over fully crisp game structures. These idealized assumptions stand in stark contrast with human decision making in real world environments. The natural strategies framework along with some of its recent variants, partially addresses this gap by restricting strategies to concise rules guarded by regular expressions. Yet, it still overlook both the cost of each action and the uncertainty that often characterizes human perception of facts over the time. In this work, we introduce HumanATLF, a logic that builds upon natural strategies employing both fuzzy semantics and resource bound actions: each action carries a real valued cost drawn from a non refillable budget, and atomic conditions and goals have degrees in [0,1]. We give a formal syntax and semantics, and prove that model checking is in P when both the strategy complexity k and resource budget b are fixed, NP complete if just one strategic operator over Boolean objectives is allowed, and Delta^P_2 complete when k and b vary. Moreover, we show that recall based strategies can be decided in PSPACE. We implement our algorithms in VITAMIN, an open source model checking tool for MAS and validate them on an adversarial resource aware drone rescue scenario.

Problem

• Modeling human decision-making in multi-agent systems with realistic constraints.
• Incorporating action costs and fuzzy semantics into strategic reasoning.
• Analyzing computational complexity of model checking under resource bounds.

Innovation

• Fuzzy semantics for uncertain human perception
• Resource-bounded actions with non-refillable budgets
• Model checking with fixed complexity and budget