Stanford University, KSL
The Web is evolving from a repository for text and images to a provider of services - both information-providing services, and services that have some effect on the world. Today's Web was designed primarily for human use. To enable reliable, large-scale automated interoperation of services by computer programs or agents, the properties, capabilities, interfaces and effects of Web services must be understandable to computers. In this work we propose a vision and a partial realization of precisely this. We propose markup of Web services in the DAML family of semantic Web markup languages. Our markup of Web services enables a wide variety of agent technologies for automated Web service discovery, execution, composition and interoperation. We present one logic-based agent technology for service composition, predicated on the use of reusable, task-specific, high-level generic procedures and user-specific customizing constraints. Joint work with Tran Cao Son and Honglei Zeng.
Dr. Sheila McIlraith has been a research scientist in the Stanford University Computer Science Department's Knowledge Systems Laboratory (KSL) since early 1998. For much of 1997, she was a postdoctoral fellow in KSL and a visiting scholar at the Xerox Palo Alto Research Center (PARC). Dr. McIlraith received her Ph.D. in Computer Science from the University of Toronto with Ray Reiter in 1997. Her dissertation addressed knowledge representation and reasoning issues associated with diagnostic problem solving in the context of a theory of action and change. She has 10 years of industrial R&D experience developing artificial intelligence applications, predominantly in the oil and gas sector. Dr. McIlraith has published over 40 refereed papers in the areas of knowledge representation, diagnosis, and related fields. She leads KSL's research effort on DAML-Enabled Web Services, and on modeling and diagnosing hybrid systems for NASA. Her other main research focus concerns detecting and exploiting structure in logical theories to improve the efficiency of reasoning.