1. 2/24/2019

2. Subfields (sub)symbolic
Bayes rules
Expert systems
Hybrid systems
Rules
Agents
Learning
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3. 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

4. + 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

5. 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

6. 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

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