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What Makes Atl Decidable? A Decidable Fragment of Strategy Logic

机译:是什么使Atl更具决定性?战略逻辑的决定性片段

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Strategy Logic (Sl, for short) has been recently introduced by Mogavero, Murano, and Vardi as a formalism for reasoning explicitly about strategies, as first-order objects, in multi-agent concurrent games. This logic turns out to be very powerful, strictly subsuming all major previously studied modal logics for strategic reasoning, including Atl, Atl, and the like. The price that one has to pay for the expressiveness of S L is the lack of important model-theoretic properties and an increased complexity of decision problems. In particular, Sl does not have the bounded-tree model property and the related satisfiability problem is highly un-decidable while for Atl it is 2ExpTime-COMPLETE. An obvious question that arises is then what makes Atl decidable. Understanding this should enable us to identify decidable fragments of Sl. We focus, in this work, on the limitation of ATL to allow only one temporal goal for each strategic assertion and study the fragment of Sl with the same restriction. Specifically, we introduce and study the syntactic fragment One-Goal Strategy Logic (SL[ 1G], for short), which consists of formulas in prenex normal form having a single temporal goal at a time for every strategy quantification of agents. We show that Sl[ 1G] is strictly more expressive than Atl. Our main result is that Sl[1g] has the bounded tree-model property and its satisfiability problem is 2ExpTime-complete, as it is for Atl.
机译:Mogavero,Murano和Vardi最近引入了Strategy Logic(简称为Sl),作为一种形式主义,用于明确地推理多Agent并发游戏中作为一阶对象的策略。事实证明,此逻辑非常强大,严格地将先前研究的所有主要模态逻辑都包含在战略推理中,包括Atl,Atl等。为了表达S L而必须付出的代价是缺乏重要的模型理论特性以及决策问题的复杂性增加。特别地,S1不具有边界树模型属性,并且相关的可满足性问题是高度不确定的,而对于Atl而言,其是2ExpTime-COMPLETE。那么,一个显而易见的问题便是使Atl更具决定性的原因。理解这一点将使我们能够确定S1的决定性片段。在这项工作中,我们将重点放在ATL的限制上,以便为每个策略声明只允许一个时间目标,并研究具有相同限制的S1片段。具体来说,我们介绍并研究句法片段“一目标策略逻辑”(简称“ SL”,简称1G),该策略逻辑由前向范式形式的公式组成,一次针对每个策略量化代理都具有单个时间目标。我们显示,Sl [1G]严格比Atl具有更高的表达力。我们的主要结果是,Sl [1g]具有有界树模型属性,其可满足性问题是2ExpTime-complete,与Atl一样。

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