Bayesian emergent self awareness

Multisensor signal Data Fusion and Perception, including processing of signals are important cognitive functionalities that can be included in artificial systems to increase their level of autonomy. However, the techniques they rely on have been developed incrementally along time with the underlying assumption that they should have been used mainly to provide a support to decision tasks driving the actions of those systems. Cognitive functionalities like self-awareness have been so far considered as not primary part of embodied knowledge of an autonomous or semi autonomous systems. One of the reason for this choice was the lack of understanding the principles that could allow an agent, even a human one, to organize successive sensorial experiences into a coherent framework of emergent knowledge, by means of integrating signal processing, machine learning and data fusion aspects. However, the developments of this last decade in many fields carried to the possibility to provide integrated solutions capable to sketch how emergent self awareness can be obtained by capturing experiences of autonomous agents like for example vehicles and intelligent radios. In this presentation, a hierarchical Bayesian representation is proposed based on generalized random states and including in a coherent inference framework anomaly detection and incremental learning. Described models are provided of generative (temporally and hierarchically) predictive as well as of discriminative capabilities and can be used as bricks of emergent self awareness in intelligent agents. Discussion of the advantages of including emergent self awareness in intelligent agents will be also provided with respect to different aspects, e.g. explainability of agent’s actions and capability of imitation learning.