Multisensorial Generative and Descriptive Self-Awareness Models for Autonomous Systems

Abstract

In a computational context, self-awareness (SA) is a capability of an autonomous system to describe the acquired experience about itself and its surrounding environment with appropriate models and correlate them incrementally with the currently perceived situation to expand its knowledge continuously. This article introduces a bio-inspired framework for generative and descriptive dynamic models that support SA computationally and efficiently. Generative models facilitate predicting future states, while descriptive models enable the selection of the representation that best fits the current observation. Our framework is founded on the analysis and extension of three bio-inspired theories that have studied SA from different viewpoints, and we demonstrate how probabilistic techniques, such as cognitive dynamic Bayesian networks and generalized filtering paradigms, can learn appropriate models from multidimensional proprioceptive and exteroceptive signals acquired by the autonomous system. We discuss essential capabilities for SA and show how our modeling framework supports these capabilities in theory and through a case study where a mobile robot uses multisensorial data to determine its internal and environmental state as well as distinguishing among normal and abnormal behaviors.