We propose and evaluate an agenda- and justification-based architecture for discovery systems that selects the next tasks to perform. This framework has many desirable properties: (1) it facilitates the encoding of general discovery strategies using a variety of background knowledge, (2) it reasons about the appropriateness of the tasks being considered, and (3) it tailors its behavior toward a user's interests. A prototype discovery program called HAMB demonstrates that both reasons and estimates of interestingness contribute to performance in the domains of protein crystallization and patient rehabilitation.

Autonomous discovery systems will be able to peruse very large databases more thoroughly than people can. In a companion paper by G.R. Livingston et al. (see ibid., p.385-92, 2001), we describe a general framework for autonomous systems. We present and evaluate heuristics for use in this framework. Although these heuristics were designed for a prototype system, we believe they provide good initial solutions to problems encountered when implementing fully autonomous discovery systems. As such, these heuristics may be used as the starting point for future research into fully autonomous discovery systems.

One of the challenges of knowledge discovery is identifying patterns that are interesting, with novelty an important component of interestingness. Another important aspect of knowledge discovery is making efficient use of background knowledge. This paper develops a definition of novelty relative to a prior model of the domain. The definition of novelty is tested using pneumonia outcome data and a prior model of pneumonia severity.