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Presentation transcript:

2/24/2019

Subfields (sub)symbolic Bayes rules Expert systems Hybrid systems Rules Agents Learning 2/24/2019

Rules If condition1 and condition2 then Consequence Inference engine, lists of rules (knowledge base), data (database), knowledge encapsulation, explanation, user Rule chaning – foreward, backward, combinations Deduction, abduction, induction Semantic networks, objects, frames, trees, ontologies 2/24/2019

+ some concepts Declarative-procedural programming Search: depth-first, breadth-first, beam-search, alfa-beta, A* Meta- Progaming languages: Prolog, Logo, Lisp ... Logic – closed world 2/24/2019

Probabilistic-Bayesian Optimal in theory, in future not P(H|E) = P(H)xP(E|H) / P(E) P(H)xP(E|H) P(H|E) = -------------------------------------- P(H)xP(E|H) + P(~H)xP(E|~H) Likehood ratio, odds O(H) = P(H)/ P(~H) 2/24/2019

Naïve Baesian Classifier P(A1=v1&A2=v2…|C=c) P(C|A)= P(C=c) x --------------------------------- P(A1=v1&A2=v2…) P(A1=v1|C=c)xP(A2=v2|C=c)… 2/24/2019

Baesian Network P(F) = P(F|L,A)P(LΛA) + P(F|¬L,A) P(¬LΛA) + 2/24/2019

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