Implementing Discrete Event Calculus with Semantic Web Technologies

Mepham, Will; Gardner, Steve

doi:10.1109/nwesp.2009.15

Cited by 3 publications

(2 citation statements)

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“…Most of these approaches have been developed strictly within the context of the Semantic Web and were not related to any research based on different techniques or paradigms. One exception is the effort to provide an ontologic representation of the Discrete Event Calculus (DEC; Mepham and Gardner, 2009;Mepham, 2010), an alternative to Event Calculus (EC) (see Section 4). The Discrete Event Ontology (DEO) consisted of OWL ontology, several SWRL rules, and a resolver.…”

Section: The Event Section Of the Ontologymentioning

confidence: 99%

“…These modules will, at first, be built manually and will be available to users of DCEO as starting points for developing an ontology-based infrastructure that suits their needs. Taking one of these modules, we plan to transform a set of SWRL rules from DEO that performs sophisticated event-related reasoning (Mepham, 2010). Another set of rules for applications involving legal agents may be based on LegalRuleML, a specific standard for representing content of legal text, that is built on RuleML and is fully compatible with SWRL (Athan et al, 2013(Athan et al, , 2015.…”

Section: Possible Future Extensionsmentioning

confidence: 99%

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Modeling artificial agents’ actions in context – a deontic cognitive event ontology

Vacura

2020

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Although there have been efforts to integrate Semantic Web technologies and artificial agents related AI research approaches, they remain relatively isolated from each other. Herein, we introduce a new ontology framework designed to support the knowledge representation of artificial agents’ actions within the context of the actions of other autonomous agents and inspired by standard cognitive architectures. The framework consists of four parts: 1) an event ontology for information pertaining to actions and events; 2) an epistemic ontology containing facts about knowledge, beliefs, perceptions and communication; 3) an ontology concerning future intentions, desires, and aversions; and, finally, 4) a deontic ontology for modeling obligations and prohibitions which limit agents’ actions. The architecture of the ontology framework is inspired by deontic cognitive event calculus as well as epistemic and deontic logic. We also describe a case study in which the proposed DCEO ontology supports autonomous vehicle navigation.

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Section: The Event Section Of the Ontologymentioning

confidence: 99%