Michael J. Pazzani
Department of Information and Computer Science University of California, Irvine
Research Overview: The common theme behind my research is the investigation and analysis of learning methods that make use of prior knowledge to guide the learning process. Typically, these learning methods combine empirical (i.e., correlational or data-driven) and explanation-based (i.e., analytical or knowledge-intensive) learning techniques. The goal is to create learning systems that accept as input background knowledge, although incomplete and incorrect, along with training examples, and learn to make classifications that are more accurate than that made by either the background knowledge alone, or by the results of applying an induction algorithm on the training data. My early work on OCCAM  describes a learning system that has the capability of acquiring knowledge empirically and later using this knowledge to facilitate knowledge-intensive learning. This research was inspired by psychological findings on the types of information that people use during learning and how this information affects the rate of learning. Part of this research also focused on the acquisition of causal relationships . In this paper, it is argued that in addition to specific knowledge of actions and effect, the process of learning causal relationships is also facilitated by general knowledge of causality. That is, causal relationships that conform to one of a number of common patterns of causal relationships are easier for human subjects to learn. This paper also provides experimental evidence collected from human subjects. An experiment shows that human subjects learning a causal relationship that conforms to one particular causal pattern require fewer trials than subjects learning a causal relationship that violates this pattern. In more recent research in this framework , I have addressed issues of learning when the background knowledge is overly general. In addition, in  I have addressed the issue of the acquisition of the common patterns of causal relationships used by OCCAM and show that they can be formed by looking for commonalities among rules found by an empirical learner.