In US Navy command and control systems there are increasing problems with situations in which human technicians must make quick decisions based on uncertain information. The SABER tool is being developed to use an explanation-based reasoning approach to assist in making such decisions. Explanation-based reasoning is modeled after an explanation-based decision-making process through which people are believed to perform some decision-making tasks. SABER constructs and evaluates alternative explanations to account for input data that may be incomplete or inconsistent. The explanations are evaluated according to three criteria: simplicity, completeness, and significance.>

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Easy learning of a user interface depends in part on users being able to generalize successfully about it. Philosophical doctrine, and some recent work in human-computer interaction, argues that causal analysis of interactions can support generalization. But neither the philosophical literature nor the HCI literature provides a rigorous theory of causal analysis adequate for problems in human-computer interaction. We propose such a rigorous theory here, and show how it accounts for two robust generalizations, using certain general assumptions. We then present evidence that these assumptions are accepted by people. Finally we compare this theory with other treatments of consistency.