Functional Validation of Agents

A Project of the D'Agents Laboratory at Dartmouth College

Guofei Jiang, George Cybenko, Daniel Bilar

In most working and proposed multiagent systems, the problem of identifying and locating agents that can provide specific services is a major problem of concern. A broker or matchmaker service is often proposed as a solution. These systems use keywords drawn from application domain ontologies to specify agent services, usually framed within some sort of knowledge representation language. However, we believe that keywords and ontologies cannot be defined and interpreted precisely enough to make brokering or matchmaking among agents sufficiently robust in a truly distributed, heterogeneous multiagent computing environment. This creates matching conflicts between a client agent's requested functionality and a service agent's actual functionality. We propose a new form of interagent communication, called functional validation, specifically designed to solve such matching conflicts.

Functional validation means that a client agent presents to a prospective service agent a sequence of challenges. The service agent replies to these challenges with corresponding answers. Only after the client agent is satisfied that the service agent's answers are consistent with the client agent's expectations is an actual commitment made to using the service. This is especially important in mission critical applications

In this project we introduce the functional validation concept in multiagent systems, analyze the possible situations that can arise in validation problems and formalize the mathematical framework in PAC- Learning theory. Moreover, we implement a multiagent systems as a testbed and investigate the functional validation protocol . In order to reduce the network traffic, we also create a mobile functional validation agent system.


Poster 1

Poster 2

Relevant Dartmouth papers:

Matching Conflicts: Functional Validation of Agents:George Cybenko and Guofei Jiang. In AAAI Workshop of Agent Conflicts, pages 14-19, Orlando, Florida, July, 1999. AAAI Press.

Machine Learning in Grid Computing: George Cybenko and Guofei Jiang. I37th Annual Allerton Conference on Communication, Control, and Computing, Champaign-Urbana, Illinois, September 1999.

Relevant projects, inside D'Agents:

Performance evaluation of distributed object infrastructure