如何使用GCP网络进行违背职责的建模

IF 1.9 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Intelligenza Artificiale Pub Date : 2022-12-27 DOI:10.3233/ia-221057
Andrea Loreggia, Roberta Calegari, E. Lorini, Francesca Rossi, G. Sartor
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引用次数: 0

摘要

偏好在我们的日常生活中无处不在。它们在个人决策过程中至关重要。最近,它们也被用来代表伦理原则、规范体系或指导方针。在这项工作中,我们专注于道义逻辑的一个等价语义:一种更大的受尊重的处方集比一些被违反的情况更可取的情况。条件偏好网络(CP nets)是一种表达和分析同一偏好的紧凑形式,具有一些理想的计算性质。在本文中,我们展示了如何用广义CP网(GCP网)对义务概念(如违背义务)进行建模,以及如何在这种形式中捕捉强许可和弱许可之间的区别。为了做到这一点,我们利用了现有的限制道义逻辑,该逻辑将被映射到条件偏好网络中。
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How to model contrary-to-duty with GCP-nets
Preferences are ubiquitous in our everyday life. They are essential in the decision making process of individuals. Recently, they have also been employed to represent ethical principles, normative systems or guidelines. In this work we focus on a ceteris paribus semantics for deontic logic: a state of affairs where a larger set of respected prescriptions is preferable to a state of affairs where some are violated. Conditional preference networks (CP-nets) are a compact formalism to express and analyse ceteris paribus preferences, with some desirable computational properties. In this paper, we show how deontic concepts (such as contrary-to-duty obligations) can be modeled with generalized CP-nets (GCP-nets) and how to capture the distinction between strong and weak permission in this formalism. To do that, we leverage on an existing restricted deontic logic that will be mapped into conditional preference nets.
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来源期刊
Intelligenza Artificiale
Intelligenza Artificiale COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
3.50
自引率
6.70%
发文量
13
期刊最新文献
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