Youyong Zou

F-OWL: an inference engine for semantic web

Understanding and using the data and knowledge encoded in semantic web documents requires an inference engine. F-OWL is an inference engine for the semantic web language OWL language based on F-logic, an approach to defining frame-based systems in logic. F-OWL is implemented using XSB and Flora-2 and takes full advantage of their features. We describe how F-OWL computes ontology entailment and compare it with other description logic based approaches. We also describe TAGA, a trading agent environment that we have used as a test bed for F-OWL and to explore how multiagent systems can use semantic web concepts and technology.

Using semantic web technology in multi-agent systems: a case study in the TAGA trading agent environment

Travel Agent Game in Agentcities (TAGA) is the framework that extends and enhances the Trading Agent Competition (TAC) scenario to work in Agentcities, an open multi agent environment based on FIPA compliant pla tforms. TAGA uses the semantic web languages and tools (RDF and OWL) to specify and publish the underlying common ontologies; as a content language within the FIPA ACL messages; as the basis for agent knowledge bases via XSB-based reasoning tools; to describe and reason about services. TAGA extends the FIPA protocols to support open market auctions and enriches the Agentcities with auction services. The introducing of the semantic web languages improves the interoperability among agents. TAGA is intended as a platform for research in multi-agent systems, the semantic web and automated trading in dynamic markets as well as a self-contained application for teaching and experimentation with these technologies.

Agents Making Sense of the Semantic Web

Effective use of the vast quantity of available information and services on the Internet will require multi-agent systems to be tightly integrated with existing web infrastructure. This however will be impossible unless the information on the web is presented in a semantic language, such as the DARPA Agent Markup Language (DAML), which is one aim of the “Semantic Web”. As part of our exploration of Semantic Web technology, and DAML in particular, we have constructed ITTALKS, a web-based system for automatic and intelligent notification of information technology talks. In this paper, we describe the ITTALKS system, and discuss the numerous ways in which the use of Semantic Web concepts and DAML extend its ability to provide an intelligent online service to both the human community and, more interestingly, the agents assisting them.

Semantic resolution for e-commerce

We describe a research project on resolving semantic differences for multi-agent systems (MAS) in electronic commerce. The approach can be characterized as follows: (1) agents in a MAS may have their own specific ontologies defined on top of a shared base ontology; (2) concepts in these ontologies are represented as frame-like structures based on DAML+OIL language; (3) the semantic differences between agents are resolved at runtime through inter-agent communication; and (4) the resolution is viewed as an abductive inference process, and thus necessarily involves approximate reasoning.

Agent Communication in DAML World

Darpa Agent Markup Language (DAML) [7] is the newest effort for Semantic Web [5]. It can be used to create ontologies and markup information resource like web pages. The information resource can be read by human and understood by agent programs. We believed DAML could be used to markup agent communication content and promote knowledge sharing and exchanging between agents. This paper also suggested an alternative model to connect web and agent together. We defined the necessary ontologies for agent communication in DAML language and described the agent communication scenario occurred in the ITTalks Project.

Semantic resolution for E-Commerce

We describe a research project on resolving semantic differences for multi-agent systems (MAS) in electronic commerce. The approach can be characterized as follows: (1) agents in a MAS may have their own specific ontologies defined on top of a shared base ontology; (2) concepts in these ontologies are represented as frame-like structures based on DAML+OIL language; (3) the semantic differences between agents are resolved at runtime through inter-agent communication; and (4) the resolution is viewed as an abductive inference process, and thus necessarily involves approximate reasoning.

Semantic Resolution for E-commerce

We describe a research project on resolving semantic differences for multi-agent systems (MAS) in electronic commerce. The approach can be characterized as follows: (1) agents in a MAS may have their own specific ontologies defined on top of a shared base ontology; (2) concepts in these ontologies are represented as frame-like structures based on DAML+OIL language; (3) the semantic differences between agents are resolved at runtime through inter-agent communication; and (4) the resolution is viewed as an abductive inference process, and thus necessarily involves approximate reasoning.