A Computationally Grounded Framework for Cognitive Attitudes (extended version)

TL;DR

This paper introduces a new formal language with semantics grounded in computation for modeling agents' cognitive attitudes, supporting complex psychological concepts and belief change reasoning, with an efficient model checking algorithm.

Contribution

It presents a novel modal language for cognitive attitudes, an axiomatization, dynamic extensions, and a PSPACE model checking algorithm with experimental validation.

Findings

Operators are mutually non-expressible and combinable.

The language can represent diverse psychological concepts.

An efficient model checking algorithm is developed and tested.

Abstract

We introduce a novel language for reasoning about agents' cognitive attitudes of both epistemic and motivational type. We interpret it by means of a computationally grounded semantics using belief bases. Our language includes five types of modal operators for implicit belief, complete attraction, complete repulsion, realistic attraction and realistic repulsion. We give an axiomatization and show that our operators are not mutually expressible and that they can be combined to represent a large variety of psychological concepts including ambivalence, indifference, being motivated, being demotivated and preference. We present a dynamic extension of the language that supports reasoning about the effects of belief change operations. Finally, we provide a succinct formulation of model checking for our languages and a PSPACE model checking algorithm relying on a reduction into TQBF. We present some experimental results for the implemented algorithm on computation time in a concrete example.