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Details of Grant 

EPSRC Reference: GR/K57985/02
Title: EXPERIMENTS WITH DISTRIBUTED - BASED MACHINE LEARNING
Principal Investigator: Muggleton, Professor S
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Imperial Cancer Research Fund Trading Lt
Department: Computer Science
Organisation: University of York
Scheme: Standard Research (Pre-FEC)
Starts: 01 October 1997 Ends: 28 February 1999 Value (£): 83,243
EPSRC Research Topic Classifications:
Artificial Intelligence
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:  
Summary on Grant Application Form
Inductive logic programming (ILP) is the study of automated inductive learning using a first-order definite clause representation. Although ILP already has been applied successfully to problems in several complex domains, including structure-activity prediction of potential pharmaceutical chemicals and protein secondary-structure prediction, these and other such domains often force ILP systems into intractable searches for target concepts or their approximations. This project aims at avoiding such intractable searches through the use of explicit domain-specific information, obtained from domain experts, about the probability distribution of target concepts within the domain. specifically, the project will test the following hypothesis.Hypothesis: An ILP system can be modified to take, as additional input, explicit domain-specific distributional information, and to use that information in a way that renders previously intractable searches tractable.This hypothesis will be tested using the ILP system PROGOL and the following domains: (1) structure-activity prediction, (2) protein secondary-structure prediction, (3) natural language grammar learning, and (4) networking.Success in this project will lead to substantial improvements in existing systems for automated inductive learning.
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Organisation Website: http://www.york.ac.uk