A Computationally Grounded Framework for Cognitive Attitudes (extended version)
Tiago de Lima, Emiliano Lorini, Elise Perrotin, François Schwarzentruber
TL;DR
This work develops a belief-base, computationally grounded framework for cognitive attitudes that jointly handles epistemic and motivational states. It introduces a modal language with five operators—$\Box_i$, $\smiley_i$, $\frownie_i$, $[\smiley]_i$, and $[\frownie]_i$—and provides a sound and complete axiomatization, along with a dynamic extension for belief change. By grounding states in explicit belief bases and defining three accessibility relations, the authors obtain succinct models and a PSPACE model-checking procedure via a reduction to $\mathrm{TQBF}$, supported by an implemented prototype and experiments. The framework supports derived notions such as motivation, demotivation, indifference, ambivalence, and diverse preferences, and paves the way for belief revision and graded notions of motivation in future work.
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.