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incomplete Therefore, experts, by necessity must employ heuristics or rules of thumb to arrive at satisfactory, but not necessarily optimal solutions In a rulebased expert system, in which expertise is captured as a collection of IF THEN rules, a knowledge engineer is forced to establish concrete correlations between a rule s premise and its conclusions Unfortunately, in many domains, these correlations are weak and not clearly understood (Indeed, science is forever progressing so that new and stronger empirical associations are continually being discovered and ne-tuned) Moreover, in many domains, there are contradictory opinions as to what the solutions to the problems are Combining the views of multiple experts into a consensus knowledge base can be a huge challenge In the second case, the actual case data may be imprecise or unreliable.

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Reasons for imprecision might include any of the following: the unreliability of sensors such that precise measurements are not possible, lab test results that are only approximate, reports that are subjective and ambiguous, evidence that may be contradictory, and an assortment of human errors and biases that can stymie even the most exacting and meticulous of experts Even when it is possible to collect more precise data, it may not be practical or reasonable to do so One case in point: It may be possible for a doctor to obtain better patient data by means of some type of invasive surgery, but such procedures could be costly and risky to the patient s health Another limitation is that collecting more data takes time, and quick decisions sometimes need to be made.

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Example 16.10 The apparent inscrutability of messages sloshing around a factor graph can be alleviated somewhat by considering a sequence of messages for a particular example. We return to the factor graph of Figure 16.1(b). Marginalizing on all the variables can be accomplished with six steps of message passing. At each step, each node that has messages from all its neighbors except one passes its message to that neighbor. Figure 16.6, which shows the factor graph of Figure 16.l(b) redrawn, summarizes the steps of the algorithm and includes the implicit j3{i) nodes for the marginalized functions. The numbers in the circles indicate the step in which the message is passed along the edge.

In a moment of crisis, for example, a decision maker must rely on incomplete information to come to a quick decision, and there is simply not enough time to gather all the data and perform a rigorous and painstaking analysis on the collected data Because of the presence of uncertainty in almost every interesting domain, a good expert system must be able to handle uncertainty For this reason, a number of formalisms have been developed for handling inexact reasoning, among them: fuzzy logic, the theory of belief functions (also known as Dempster-Shafer theory of evidence), subjective probability theory (including Bayesian reasoning), and certainty factors2 This chapter focuses on two of the more popular inexact reasoning paradigms: certainty factors and Bayesian reasoning A theme that emerges in this discussion is that inexact reasoning can be aided by a diagrammatic representation.

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This chapter will show how the certainty factors approach and Bayesian networks can employ in uence and causal diagrams to illustrate their reasoning process, and shed light on how uncertainty propagates in a system..

Figure 9.1 (Continued )

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Example 2.8 Consider the set of numbers { 1,2, 3,4,5}using the operation of multiplication modulo 6. The operation is shown in the following table: