Xiaocheng Luan

An agent-based infrastructure for enterprise integration

Some of the features that make Jackal (Java-based Applications Communication using KQML Agent communication Language) extremely valuable to agent development are its conversation management facilities, its flexible, blackboard-style interface and its ease of integration. Jackal has been developed in support of an investigation of the use of agents in enterprise-wide integration of planning and execution for manufacturing. This paper describes Jackal at a surface level and at the design level, and demonstrates its use in a multi-agent system that supports intelligent integration of enterprise planning and execution.

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.

Quantitative Agent Service Matching

The ultimate goal of service matching is to find the service provider(s) that would perform tasks of given description with the best overall degree of satisfaction. However, service description matching solves only part of the problem. Agents that match a given request may vary greatly in their actual capabilities to perform the tasks, and an agent may have strong and weak areas. In this work, we take a quantitative approach in which performance rating is considered an integral part of an agents capability model and service distribution is taken into account in determining the degree of match. With the dynamic refinement of the agent capability model, the broker captures an agents performance levels as well as its strong and weak areas. An experimental system has been designed and implemented within the OWL/OWL-S framework and the results statistics show significant advantage over other major levels of brokers.

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 development with Jackal

Jackal is a Java-based tool for communicating with the KQML agent communication language. Some features that make it extremely valuable to agent development are its conversation management facilities, flexible, blackboard style interface and ease of integration. Jackal has been developed in support of an investigation of the use of agents in enterprisewide integration of planning and execution for manufacturing.

AGENT CONSUMER REPORTS: OF THE AGENTS, BY THE AGENTS, AND FOR THE AGENTS

Service matching is critical in large, dynamic agent systems. While finding exact matches is always desirable as long as an agent knows what it wants, it is not always possible to find exact matches. Moreover, the selected agents (with exact match) may or may not provide quality services. Some agents may be unwilling or unable to advertise their capability information at the sufficient level of details, some might unknowingly advertise inaccurate information, while others might even purposefully provide misleading information. Our proposed solution to this problem is the agent “consumer reports”. The broker agent will not only collect the information advertised by the service provider agents, but also learn about the experiences the consumer agents have about their service providers. It might also hire some agents to test certain service providers to see how well they can do what they claim they are capable of doing. Then agent consumer reports will be built based on the information collected. The advanced level of agent consumer reports will also dynamically capture the probabilistic distribution of the services and use it to assess the probability of a match. We plan to extend LARKS and use it as our agent capability description language.