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FOCL: Expert System Shell and Machine Learning Program

areas/learning/systems/focl/
FOCL is an expert system shell and machine learning program written in Common Lisp. The machine learning program extends Quinlan's FOIL program by containing a compatible explanation-based learning component. FOCL learns Horn Clause programs from examples and (optionally) background knowledge. The expert system includes a backward-chaining rule interpreter and a graphical interface to the rule and fact base. The Macintosh version includes a graphical interface that displays the search space explored by FOCL, so it is a useful pedagogical tool. This application also contains a graphical interface for building rule bases, so you can ignore the machine learning aspects, and use it as an expert system shell with the following capabilities: + A backward-chaining rule interpreter. + A graphical rule and fact editor. + Graphical display of the rule base. + (Simple) Natural language explanation of inferences + Menu-based facilities for editing rules and adding natural language translations to rules. + Optional typing of variables and checking the rule base for type conflicts + Tracing of rules + Analysis of the accuracy of rules in a rule base. Sample rule bases are included. The Common Lisp source code is limited to portable source code for the machine learning program only, since the interface depends on the Macintosh. 
Origin:   

   ics.uci.edu:/pub/machine-learning-programs/
   as the files README.FOCL-1-2-3, FOCL-1-2-3-manual.hqx,
   FOCL-1-2-3.tar.Z, and FOCL-1-2-3.cpt.hqx
Version: 2.1 (21-APR-94) Requires: Common Lisp Ports: MCL Copying: If you use a copy of FOCL, please send mail to pazzani@ics.uci.edu so they can inform you of upgrades. CD-ROM: Prime Time Freeware for AI, Issue 1-1 Author(s): Mike Pazzani Cliff Brunk ICS Dept UC Irvine, Irvine, CA 92717 USA Contact: Michael Pazzani Keywords: Authors!Brunk, Authors!Pazzani, Backward Chaining, EBL, Expert System Explanation, Expert System Shells, FOCL, Horn Clauses, Lisp!Code, Machine Learning References: Pazzani, M. and Kibler, D., "The role of prior knowledge in inductive learning", Machine Learning 9:54-97, 1992.
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