Reasoning about group responsibility for exceeding risk threshold in one-shot games

IF 0.8 4区 计算机科学 Q3 COMPUTER SCIENCE, THEORY & METHODS
Maksim Gladyshev , Natasha Alechina , Mehdi Dastani , Dragan Doder
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引用次数: 0

Abstract

Tracing and analysing the responsibility for unsafe outcomes of actors' decisions in multi-agent settings have been studied in recent years. These studies often focus on deterministic scenarios and assume that the unsafe outcomes for which actors can be held responsible are actually realized. This paper considers a broader notion of responsibility where unsafe outcomes are not necessarily realized, but their probabilities are unacceptably high. We present a logic combining strategic, probabilistic and temporal primitives designed to express concepts such as the risk of an undesirable outcome and being responsible for exceeding a risk threshold in one-shot games. We demonstrate that the proposed logic is (weakly) complete, decidable and has an efficient model-checking procedure. Finally, we define a probabilistic notion of responsibility and study its formal properties in the proposed logic setting.
单次博弈中超过风险阈值的群体责任推理
近年来,人们对多智能体环境中行为者决策不安全结果的责任跟踪和分析进行了研究。这些研究通常侧重于确定性情景,并假设行为者可能负责的不安全结果实际上已经实现。本文考虑了一个更广泛的责任概念,其中不安全的结果不一定要实现,但它们的概率高得令人无法接受。我们提出了一种结合战略、概率和时间原语的逻辑,旨在表达诸如出现不良结果的风险以及在单次游戏中负责超越风险阈值等概念。我们证明了所提出的逻辑是(弱)完备的,可决定的,并且具有有效的模型检查过程。最后,我们定义了责任的概率概念,并研究了其在所提出的逻辑设置中的形式性质。
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来源期刊
Information and Computation
Information and Computation 工程技术-计算机:理论方法
CiteScore
2.30
自引率
0.00%
发文量
119
审稿时长
140 days
期刊介绍: Information and Computation welcomes original papers in all areas of theoretical computer science and computational applications of information theory. Survey articles of exceptional quality will also be considered. Particularly welcome are papers contributing new results in active theoretical areas such as -Biological computation and computational biology- Computational complexity- Computer theorem-proving- Concurrency and distributed process theory- Cryptographic theory- Data base theory- Decision problems in logic- Design and analysis of algorithms- Discrete optimization and mathematical programming- Inductive inference and learning theory- Logic & constraint programming- Program verification & model checking- Probabilistic & Quantum computation- Semantics of programming languages- Symbolic computation, lambda calculus, and rewriting systems- Types and typechecking
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