SW-CASPAR: Reactive-Cognitive Architecture based on Natural Language Processing for the task of Decision-Making in the Open World Assumption

This paper addresses the issue of nowadays vocal assistants cognitive lacks, which are able to execute from vocal commands only simple plans without higher capabilities of decision-making. In this work we propose an ''open-world assumption'' transposition of the cognitive architecture CASPAR, whose heuristic takes into account of meta-reasoning in the ''closed-world assumption'', namely SW-CASPAR. Such a cognitive architecture is also provided with a module for semi-automatic ''ontology learning'' from sentences in natural language, reflecting the domain with an instance of a novel foundational ontology called Linguistic Oriented Davidsonian Ontology (LODO), with the aim of increasing the deepness of reasoning without compromising linguistic-related features. LODO is inspired by the ''First-Order Logic Davidsonian'' notation and serialized in OWL 2. A case-study applied to automation on health scenarios is also provided.