A Combined Analytic and Inductive Approach to Learning in Knowledge-Based
Combining expert knowledge and inductive learning for knowledge-base
We are investigating the use of novel machine learning techniques to facilitate the acquisition of knowledge for knowledge-based systems and the management of large knowledge bases. We have developed a machine learning system that maintains a close tie between a knowledge base of expert rules and a database of examples. For example, if the knowledge base is changed, the examples in the database affected by the change will be identified, and more importantly, if the database changes (e.g., by the addition of examples), the system will can make changes to the knowledge base to make it consistent with the examples. This system is called the First-Order Combined Learner FOCL.
Use machine learning methods to find patterns in data that are not correctly classified by expert knowledge
Examples belonging to a set of categories
Rules that classify examples into categories
Rules that classify examples into categories
At least as accurate as running inductive program on examples
At least as accurate as original rules
Calendar Management Example
Collaboration with Tom Mitchell , CMU. The Calendar Manager
schedules the time, duration, location of meetings.
It Learns to suggest defaults of users given information on
the purpose of meeting,
the people attending, and the
person scheduling meeting.
It is regularly used by several people at CMU.
Unique Aspects of UCI work: FOCL
Learns first-order rules from examples
More expressive than propositional logic used in decision trees, neural nets, etc.
Ability to learn from relational & object oriented databases
Some Calendar Rules Without First Order Rules:
location-WeH5309 :- attendees = office-hours &
sponsor = mitchell
location-WeH5307 :- attendees = office-hours &
sponsor = mason
Some Calendar Rules With First Order Rules
location(Meeting, Room) :- attendees(Meeting, office-hours) &
sponsor(Meeting, S) &
This first-order rule captures the same information as the two propositional
rules: Faculty typically have office hours in their offices.
Use of existing rules to bias learning
The theory graphed below is a simple theory of how faculty schedule their meeting
locations. A person who schedules the meeting schedule it in his office, classes are held
in the classroom in which they are assigned, and seminars are held in seminar rooms.
The theory isn't perfect, but it helps a FOCL learn to make more accurate predictions.
For example, one rule is changed by training on two faculty schedules to indicate that
faculty schedule meetings in their office if no more than two others attend.
(event-location_learned_rule ?0 ?1) :-
(event-type ?0 ?2), (eql ?2 meeting), (scheduled-by ?0 ?3),
(office ?3 ?1), (number-of-attendees ?0 ?4), (<= ?4 2)
Comparison of Learning Methods
This graph shows the average accuracy of FOCL trying to predicting the location
at which a meeting will be held under three conditions:
- Empirical (No Relations). FOCL learns rules like a propositional learner. It doesn't
use additional relations containing information on classroom or office locations
- Empirical (Relations) FOCL uses relations containing information on classroom or office locations to learn first-order rules.
- Empirical+ Analytic (Relations) FOCL uses relations containing information on classroom or office locations to learn first-order rules. In addition, FOCL is given
an approximate theory that is about 85% accurate.
FOCL is more accurate when given useful background relations, and even more accurate when
also given an approximate domain theory.
Existing Applications of FOCL
Application Rules Data Source
Chess end games 10 1000 investigator
Payment of student loans 20 1000 honors project
Foreign trade negotiations 30 100 company (EBR)
Aircraft Identification 35 100 company (Hughes)
Telephone network 80 1000 company (NYNEX)
Calendar management 7 500 CMU
Translation (3000) 7200 company (NTT)
Work In Progress
I assume you are familiar with a Hot List that contains pointers to your favorite
Web pages. We are working on an an extension to Mosaic that contains a Cold List
with pointers to Web pages you've visited but didn't like. Here's why:
- Links on your cold list will be displayed in gray to warn you not to go there.
- We are building a learning program to analyze your Hot List and Cold List and to suggest new Web Pages that you'd be interested in.
Machine learning papers from UCI
Project Leader: Michael Pazzani
Department of Information and Computer Science,
University of California, Irvine
Irvine, CA 92717-3425