Probabilistic BDI in a Cognitive Robot Architecture

The probabilistic approach to cognition has become an established approach in recent decades. Cognition is better viewed as solving probabilistic, rather than logical, inference problems; i.e. cognition is better understood in terms of probability theory, rather than in terms of logic. This article presents a cognitive architecture used to govern a robot probabilistically. The design and implementation of cognitive architectures is a useful tool for understanding cognition in a situated agent. The Cerno research project extended the CAMAL (Computational Architectures for Motivation, Affect, and Learning) model, by incorporating probabilistic reasoning in its BDI model. Subsequent development of CAMAL has integrated all the valanced affective predicates across the architecture. Extensive experiments with synthetic and real robots demonstrate an improvement in the overall performance, success rate, task effectiveness, and goal achievement of the cognitive architecture.