Representation results for defeasible logic
ACM Transactions on Computational Logic (TOCL)
IEEE Intelligent Systems
Propositional defeasible logic has linear complexity
Theory and Practice of Logic Programming
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
DR-Prolog: A System for Defeasible Reasoning with Rules and Ontologies on the Semantic Web
IEEE Transactions on Knowledge and Data Engineering
Rule responder: RuleML-based agents for distributed collaboration on the pragmatic web
ICPW '07 Proceedings of the 2nd international conference on Pragmatic web
Proof explanation for a nonmonotonic Semantic Web rules language
Data & Knowledge Engineering
RuleML '09 Proceedings of the 2009 International Symposium on Rule Interchange and Applications
A Guide to the Basic Logic Dialect for Rule Interchange on the Web
IEEE Transactions on Knowledge and Data Engineering
EMERALD: a multi-agent system for knowledge-based reasoning interoperability in the semantic web
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
Prova: rule-based java scripting for distributed web applications
EDBT'06 Proceedings of the 2006 international conference on Current Trends in Database Technology
The OO jDREW reference implementation of RuleML
RuleML'05 Proceedings of the First international conference on Rules and Rule Markup Languages for the Semantic Web
Hi-index | 0.00 |
The ultimate vision of the Semantic Web (SW) is to offer an interoperable and information-rich web environment that will allow users to safely delegate complex actions to intelligent agents. Much work has been done for agents' interoperability; a plethora of proposals and standards for ontologybased metadata and rule-based reasoning are already widely used. Nevertheless, the SW proof layer has been neglected so far, although it is vital for SW agents and human users to understand how a result came about, in order to increase the trust in the interchanged information. This paper focuses on the implementation of third party SW reasoning and proofing services wrapped as agents in a multiagent framework. This way, agents can exchange and justify their arguments without the need to conform to a common rule paradigm. Via external reasoning and proofing services, the receiving agent can grasp the semantics of the received rule set and check the validity of the inferred results.