Clifford Brunk

Adaptive interfaces for ubiquitous web access

Allowing mobile users to access any information at any time from any location.

Detecting and correcting errors in rule-based expert systems: an integration of empirical and explanation-based learning

Abstract In this paper, we argue that techniques proposed for combining empirical and explanation-based learning methods can also be used to detect errors in rule-based expert systems, to isolate the blame for these errors to a small number of rules and suggest revisions to the rules to eliminate these errors. We demonstrate that FOCL, an extension to Quinlans FOIL program, can learn relational concepts in spite of an incorrect domain theory (e.g. a knowledge base of an expert system that contains some erroneous rules). A prototype knowledge acquisition tool, KR-FOCL, has been constructed that utilizes a trace of FOCL to suggest revisions to a rule base.

Avoiding Non-Termination when Learning Logical Programs: A Case Study with FOIL and FOCL

Many systems that learn logic programs from examples adopt θ-subsumption as model of generalization and refer to Plotkins framework in order to define their search space. However, they seldom take into account the fact that the lattice defined by Plotkin is a set of equivalence classes rather than simple clauses. This may lead to non-terminating learning processes, since the search gets stuck within an equivalence class, which contains an infinite number of clauses.