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.

Loreggia A., Calegari R., Lorini E., Rossi F., Sartor G. (2022). How to model contrary-to-duty with GCP-nets. INTELLIGENZA ARTIFICIALE, 16(2), 185-198 [10.3233/IA-221057].

How to model contrary-to-duty with GCP-nets

Loreggia A.;Calegari R.
;
Sartor G.
2022

Abstract

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.
2022
Loreggia A., Calegari R., Lorini E., Rossi F., Sartor G. (2022). How to model contrary-to-duty with GCP-nets. INTELLIGENZA ARTIFICIALE, 16(2), 185-198 [10.3233/IA-221057].
Loreggia A.; Calegari R.; Lorini E.; Rossi F.; Sartor G.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/912872
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