异步智能体的策略能力：语义副作用及其抑制方法
Strategic Abilities of Asynchronous Agents: Semantic Side Effects and How to Tame Them
AI总结：
针对异步多智能体系统原有能力验证框架语义存在反直觉解读的问题，提出扩展语义表示、修改执行语义的解决方案，并证明模型约简方案仍适用于新框架。
摘要：
近期，我们提出了一套用于验证异步多智能体系统（asynchronous multi-agent systems，简称异步MAS）中智能体能力的框架，以及一种用于模型自动约简的算法。该语义建立在分布式系统的建模传统之上。我们在本文中表明，在推理策略结果时，这种方式有时会导致对公式的反直觉解读。首先，该语义忽略有限路径，因此会对含死锁的策略产生不自然的评估。其次，语义表示无法捕捉主动智能体与其选择的接收方之间的不对称性。我们提出通过适当扩展表示形式并修改异步MAS的执行语义来规避这些问题，同时证明了模型约简方案在修改后的框架中仍然有效。
英文摘要：
Recently, we have proposed a framework for verification of agents' abilities in asynchronous multi-agent systems, 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 thus 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
