Includes bibliographical references (p. ) and index.
A system of defeasible inference based on probabilities
high probabilities and preferential structures
irrelevance and prioritized preferential structures
the causal dimension - evidence vs. explanation
(source: Nielsen Book Data)
Defaults in commonsense reasoning permit the generation of useful predictions in the absence of complete information. However, attempts to represent and reason with defaults in AI run into the problem of spurious arguments, arguments that rely on acceptable defaults but which support unacceptable conclusions. Geffner addresses this problem by analyzing the causal and conditional aspects of default, establishing theoretical limits on the capabilities of probabilistic approaches. He provides new insights into the nature of defaults, and new methods of processing databases containing default expressions. The result is a default-handling system that yields an intuitive behaviour in several domains of interest in AI, including inheritance hierarchies, reasoning about change, general logic programs, and abductive reasoning. (source: Nielsen Book Data)