Commonsense reasoning and automatic generation of IoT contextual knowledge: An Answer Set Programming approach

Abstract

The increasing interest in searching for intelligent, proactive, and autonomous environments leads to the necessity of accessing contextual knowledge: knowledge about changes that occur in the environment and even about the capabilities of the devices that compose a specific deployment. This knowledge would allow carrying out commonsense reasoning (exhibit human-like comprehension) according to the current situation, enabling decision making and task planning. One of the most used forms of knowledge specification is ontologies, but ontological knowledge modeling is usually a manual process, making it a costly task. In addition, ontologies can be found that have been designed for specifying knowledge about IoT-related concepts, but many are overly complex or do not have an adequate orientation that allows modeling the system’s capabilities. There are many different types of reasoners, but Answer Set Solvers are of particular interest for commonsense reasoning. This paper proposes an ontology to represent contextual knowledge about IoT device capabilities, and an Answer Set Programming-based reasoner for the automatic generation of this knowledge. The main challenge is to demonstrate that contextual knowledge can be generated through commonsense reasoning processes implemented with Answer Set Programming, and that the resulting knowledge can be used for decision making (also through commonsense reasoning). This work shows how the generated knowledge is correct through different use cases, presents an application example that demonstrates the benefits of using the generated knowledge, and analyzes the reasoner performance to demonstrate that the execution time is adequate.