DR-BROKERING - A Defeasible Logic-Based System for Semantic Brokering

2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology

2009

The Semantic Web aims at making Web content understandable both for people and machines. Although intelligent agents can assist towards this vision, they do not have to conform to a common rule or logic paradigm. This paper reports on the first steps towards a framework for interoperating knowledge-based intelligent agents. A multiagent system was extended with defeasible reasoning and a reusable agent model is proposed for customizable agents, equipped with a knowledge base and a Jess rule engine. Two use case scenarios display the integration of these technologies.

Section: B Use Case: a Negotiation Scenariomentioning

confidence: 99%

Towards a Knowledge-Based Framework for Agents Interacting in the Semantic Web

Kravari

2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology

2009

“…And priority information is often available to resolve conflicts among rules. Potential applications include security policies ( [19]), business rules [1], e-contracting [15], personalization, brokering [5], bargaining and agent negotiations ( [14]). …”

Section: Introductionmentioning

confidence: 99%

Visualizing Defeasible Logic Rules for the Semantic Web

The Semantic Web – ASWC 2006

Antoniou

2006

Abstract. Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and conflicting information. Such reasoning is useful in many Semantic Web applications, like policies, business rules, brokering, bargaining and agent negotiations. Nevertheless, defeasible logic is based on solid mathematical formulations and is, thus, not fully comprehensible by end users, who often need graphical trace and explanation mechanisms for the derived conclusions. Directed graphs can assist in confronting this drawback. They are a powerful and flexible tool of information visualization, offering a convenient and comprehensible way of representing relationships between entities. Their applicability, however, is balanced by the fact that it is difficult to associate data of a variety of types with the nodes and the arcs in the graph. In this paper we try to utilize digraphs in the graphical representation of defeasible rules, by exploiting the expressiveness and comprehensibility they offer, but also trying to leverage their major disadvantage, by defining two distinct node types, for rules and atomic formulas, and four distinct connection types for each rule type in defeasible logic and for superiority relationships. The paper also briefly presents a tool that implements this representation methodology.

“…And priority information is often implicitly or explicitly available to resolve conflicts among rules. Potential applications include security policies ( [6], [16]), business rules [1], personalization, brokering [5], bargaining and agent negotiations ( [13], [20]). …”

Section: Introductionmentioning

confidence: 99%

A Visual Environment for Developing Defeasible Rule Bases for the Semantic Web

Rules and Rule Markup Languages for the Semantic Web

Antoniou

2005

Abstract. Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and inconsistent information. Such reasoning is useful for many applications in the Semantic Web, such as policies and business rules, agent brokering and negotiation, ontology and knowledge merging, etc., mainly due to interesting features, such as conflicting rules and priorities of rules. However, the RuleML syntax of defeasible logic may appear too complex for many users. Furthermore, the interplay between various technologies and languages, such as defeasible reasoning, RuleML, and RDF impose a demand for using multiple, diverse tools for building rule-based applications for the Semantic Web. In this paper we present VDR-Device, a visual integrated development environment for developing and using defeasible logic rule bases on top of RDF ontologies. VDR-Device integrates in a user-friendly graphical shell, a visual RuleML-compliant rule editor that constrains the allowed vocabulary through analysis of the input RDF ontologies and a defeasible reasoning system that processes RDF data and RDF Schema ontologies.