Logic programming with social features

TL;DR

This paper introduces a logic programming framework that models social features, enabling reasoning about mental states within communities, with semantics translatable into stable model semantics for effective computation.

Contribution

It presents a novel knowledge representation language supporting mental state reasoning in social contexts, bridging logic programming with social reasoning capabilities.

Findings

Supports reasoning about others' mental states

Semantic translation into stable model semantics

Enables social behavior modeling in logic programming

Abstract

In everyday life it happens that a person has to reason about what other people think and how they behave, in order to achieve his goals. In other words, an individual may be required to adapt his behaviour by reasoning about the others' mental state. In this paper we focus on a knowledge representation language derived from logic programming which both supports the representation of mental states of individual communities and provides each with the capability of reasoning about others' mental states and acting accordingly. The proposed semantics is shown to be translatable into stable model semantics of logic programs with aggregates.