Ontology-based hybrid commonsense reasoning framework for handling context abnormalities in uncertain and partially observable environments

Abstract

Ambient intelligence (AmI) systems aim to provide users with context-aware assistance services intended to improve the quality of their lives in terms of autonomy, safety, and well-being. Taking the uncertainty and partial observability of these environments into account is of major importance for context recognition and, more specifically, to detect and solve context abnormalities such as those related to the user's behavior or those related to context attribute prediction. In this paper, an ontology-based framework integrating machine learning and probabilistic planning within commonsense reasoning is proposed to recognize the user's context and abnormalities associated with it. The reasoning is performed using event calculus in answer set programming (ECASP); ECASP allows for abductive and temporal reasoning, which results in an eXplainable AI (XAI) approach. A context ontology is proposed to axiomatize the reasoning and introduce the notion of probabilistic fluents into the EC formalism in order to perform probabilistic reasoning. The reasoning incorporates probabilistic planning based on a partially observable Markov decision process (POMDP) to solve knowledge incompleteness. To evaluate the proposed framework, real-life scenarios, based on the Orange4Home and SIMADL public datasets are implemented and discussed.