Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
Functional transformations in AI discovery systems
Artificial Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Inductive Policy: The Pragmatics of Bias Selection
Machine Learning - Special issue on bias evaluation and selection
Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
Machine Learning
Introduction: Cognitive Autonomy in Machine Discovery
Machine Learning
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Closing the Loop: Heuristics for Autonomous Discovery
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A framework for autonomous knowledge discovery from databases
A framework for autonomous knowledge discovery from databases
An Agenda- and Justification-Based Framework for Discovery Systems
Knowledge and Information Systems
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Knowledge discovery programs in the biological sciences require flexibility in the use of symbolic data and semantic information. Because of the volume of nonnumeric, as well as numeric, data, the programs must be able to explore a large space of possibly interesting relationships to discover those that are novel and interesting. Thus, the framework for the discovery program must facilitate proposing and selecting the next task to perform and performing the selected tasks. The framework we describe, called the agenda- and justification-based framework, has several properties that are desirable in semiautonomous discovery systems: It provides a mechanism for estimating the plausibility of tasks, it uses heuristics to propose and perform tasks, and it facilitates the encoding of general discovery strategies and the use of background knowledge. We have implemented the framework and our heuristics in a prototype program, HAMB, and have evaluated them in the domain of protein crystallization. Our results demonstrate that both reasons given for performing tasks and estimates of the interestingness of the concepts and hypotheses examined by HAMS contribute to its performance and that the program can discover novel, interesting relationships in biological data.