Revising event calculus theories to recover from unexpected observations - Tsampanaki, Nikoleta; Patkos, Theodore; Flouris, Giorgos; Plexousakis, Dimitris (Annals of mathematics and artificial intelligence, 2021)
Recent extensions of the Event Calculus resulted in powerful formalisms, able to reason about a multitude of commonsense phenomena in causal domains, involving epistemic notions, functional fluents and probabilistic aspects, among others. Less attention has been paid to the problem of automatically revising (correcting) a Knowledge Base when an observation contradicts inferences made regarding the world state. Despite mature work on the related belief revision field, adapting such results for the case of action theories is non-trivial. This paper describes how to address this problem for deterministic, yet partially observable, domains, by proposing a generic framework in the context of the Event Calculus, along with ASP encodings of the revision algorithm and a web-based tester of the formalism implementation.