By Gheorghe Tecuci, Dorin Marcu, Mihai Boicu, David A. Schum
This ebook provides an important development within the concept and perform of information engineering, the self-discipline eager about the improvement of clever brokers that use wisdom and reasoning to accomplish challenge fixing and decision-making projects. It covers the most levels within the improvement of a knowledge-based agent: realizing the appliance area, modeling challenge fixing in that area, constructing the ontology, studying the reasoning ideas, and checking out the agent. The publication makes a speciality of a distinct classification of brokers: cognitive assistants for evidence-based reasoning that study advanced problem-solving services at once from human specialists, aid specialists, and nonexperts in challenge fixing and selection making, and train their problem-solving services to scholars. a strong studying agent shell, Disciple-EBR, is integrated with the publication, permitting scholars, practitioners, and researchers to boost cognitive assistants quickly in a large choice of domain names that require evidence-based reasoning, together with intelligence research, cybersecurity, legislation, forensics, drugs, and schooling.
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Additional resources for Knowledge Engineering: Building Cognitive Assistants for Evidence-based Reasoning
These are situations in which analysts are most vulnerable and in which Baconian ideas are most helpful. 20 Chapter 1. 3 Baconian Probability of Boolean Expressions Some of the most important properties of Baconian probabilities concern their application to Boolean combinations of propositions, such as hypotheses. Because the probabilities in the Baconian system have only ordinal properties, we can say only that hypothesis H1 is more likely than H2, but we cannot say how much more likely H1 is than H2.
B(G) ! B(H). Now what we wish to determine is the Baconian probability B(F or G or H). In this case, B(F or G or H) ! B(F), where B(F) is the largest of the Baconian probability for the events we are considering. This is the MAX rule for Baconian probability, and what it says is that the probability of a disjunction of events is at least as large as the largest Baconian probability of any of the individual events. Baconian Negation: Baconian negation is not complementary. The Baconian rule is quite complex; here’s what it says: If we have A and ¬A, if B(A) > 0, then B(¬A) = 0.
But we must not overlook generalization G itself; we do so by assigning it the value 1; so we have N + 1 things to consider. Now we are in a position to show what happens in any possible case. First, suppose we perform none of these N evidential tests. We could still proceed by giving generalization G the beneﬁt of the doubt and detach a belief that F occurred (or will occur) just by invoking this generalization G regarding the linkage between events E and F. ” This would amount to saying that the Baconian probability of event F is B(F) = 1/(N + 1).