Decision support systems often require knowledge of users' preferences. However, preferences may vary among individual users or be difficult for users to articulate. This paper describes how user preferences can be acquired in the form of preference predicates by a learning apprentice system and proposes two new instance-based algorithms for preference predicate acquisition: IARC and Compositional Instance-Based Learning (CIBL). An empirical evaluation using simulated preference behavior indicated that the instance-based approaches are preferable to decision-tree induction and perceptrons as the learning component of a learning apprentice system, if representation of the relevant characteristics of problem-solving states, requires a large number of attributes, if attributes interact in a complex fashion, or if there are very few training instances. Conversely, decision-tree induction or perceptron learning is preferable if there are a small number of attributes and the attributes do not interact in a complex fashion unless there are very few training instances. When tested as the learning component of a learning apprentice system used by astronomers for scheduling astronomical observations, both CIBL and decision-tree induction rapidly achieved useful levels of accuracy in predicting the astronomers' preferences.
All Science Journal Classification (ASJC) codes
- Human Factors and Ergonomics
- Human-Computer Interaction
- Hardware and Architecture