A Theory of Conditional Information for Probabilistic Inference in Intelligent Systems: 1. Interval of Events Approach
NAVAL COMMAND CONTROL AND OCEAN SURVEILLANCE CENTER RDT AND E DIV SAN DIEGO CA
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This paper emphasizes the need to develop further probability theory at the service of probabilistic intelligent systems. In the field of probabilistic systems, the causal relationships among variables of interest are viewed as if-then or production rules whose certainty factors are quantified as conditional probabilities. With some additional assumptions about the variables of interest, such as conditional independence, standard probability theory can be applied to carry out the reasoning processes. In more general situations, in which all information in the premises as well as the conclusions is in unconditional and conditional form-or in only conditional form-current probabilistic machinery requires more development to cope with this new situation. After identifying typical situations, we present a theory of conditional information in the form of the new concept of conditional events, compatible with all conditional probability quantifications. We specify applications of this theory to various problems in intelligent systems. The approach taken here to conditional events is through intervals of events. Applied research, Basic research, Command, Control and Communications.
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