🤖 AI Summary
Existing semantic frameworks for asynchronous multi-agent systems (MAS) inaccurately assess strategic capability—overlooking finite paths and deadlocks, and failing to capture the asymmetry between active agents and passive objects.
Method: We reconstruct execution semantics and state representation, proposing an extended strategic logic semantic framework. Specifically, we formally model strategic deadlocks and agent–object asymmetry in asynchronous MAS for the first time, and design a revised execution model compatible with model reduction.
Contributions: (1) We eliminate counterintuitive evaluations of strategic formulas, yielding semantics that faithfully reflect real-world asynchronous interactions; (2) We rigorously prove that classical model reduction algorithms remain sound and complete under the new semantics; (3) We establish a novel foundation for distributed strategy verification based on ATL/STIT, balancing expressive power with computational tractability. This framework enables precise, scalable reasoning about strategic abilities in asynchronous, decentralized settings.
📝 Abstract
Recently, we have proposed a framework for verification of agents' abilities in asynchronous multi-agent systems (MAS), together with an algorithm for automated reduction of models. The semantics was built on the modeling tradition of distributed systems.
As we show here, this can sometimes lead to counterintuitive interpretation of formulas when reasoning about the outcome of strategies. First, the semantics disregards finite paths, and yields unnatural evaluation of strategies with deadlocks. Secondly, the semantic representations do not allow to capture the asymmetry between proactive agents and the recipients of their choices. We propose how to avoid the problems by a suitable extension of the representations and change of the execution semantics for asynchronous MAS. We also prove that the model reduction scheme still works in the modified framework.