Revising event calculus theories to recover from unexpected observations
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Revising event calculus theories to recover from unexpected observations Nikoleta Tsampanaki1
· Theodore Patkos1 · Giorgos Flouris1 · Dimitris Plexousakis1
© Springer Nature Switzerland AG 2019
Abstract 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 nontrivial. 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. Keywords Event calculus · Belief revision · Action theories · Answer set programming Mathematics Subject Classification (2010) 03B42 · 03B70 · 68T27
1 Introduction Action languages are well-established logical theories for reasoning about the dynamics of changing worlds, aiming at “formally characterizing the relationship between the knowledge, the perception and the action of autonomous agents” [1]. One of the most prominent action languages is the Event Calculus [2, 3], which incorporates certain useful features Nikoleta Tsampanaki
[email protected] Theodore Patkos [email protected] Giorgos Flouris [email protected] Dimitris Plexousakis [email protected] 1
Institute of Computer Science, FORTH, Heraklion, Greece
N. Tsampanaki et al.
for representing causal and narrative information that differentiate it from other similar formalisms. The Event Calculus explicitly represents temporal knowledge, enabling reasoning about the effects of a narrative of events along a time line. It also relies on a non-monotonic treatment of events, in the sense that by default there are no unexpected effects or event occurrences. Powerful extensions of the main formalism have been developed to accommodate, for instance, epistemic extensions [4–6], probabilistic uncertainty [7, 8] or knowledge derivations with non-binary-valued fluents [4]. Moreover, progress in generalizing the stable model semantics used in Answer Set Programming (ASP) has opened the way for the reformulation of Event Calculus axiomatizations into logic programs that can be executed with efficient ASP solvers [9]. This allowed for exploiting state-of-the-art tools that outperform previous SAT- or logic programming-based solvers in almost all classes of problems related to practical applications [10]. However, to the best of our knowledge, little work has been done on supporting belief change in the Event Calculus, in cases when the new information contradicts the already inferred conclusions. Specifically, the existing non-epistemic extensions accommoda
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