Revising event calculus theories to recover from unexpected observations

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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Annals of mathematics and artificial intelligence Ročník 89; číslo 1-2; s. 209 - 236
Hlavní autoři: Tsampanaki, Nikoleta, Patkos, Theodore, Flouris, Giorgos, Plexousakis, Dimitris
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.02.2021
Springer
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Calculus
Complex Systems
Computer Science
Dialects
Epistemology
Formalism
Knowledge
Knowledge bases (artificial intelligence)
Logic programming
Mathematics
Revisions
Semantics
03B42
Event calculus
03B70
68T27
Action theories
Answer set programming
Belief revision
ISSN:1012-2443, 1573-7470
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1012-2443
1573-7470
DOI:10.1007/s10472-019-09663-5