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Artificial Intelligence – Knowledge Representation – issues, predicate logic, rules  , April 03, 2008 , posted by  myreaders ,  http://myreaders.wordpress.com/ ,   R C Chakraborty. Click – Title to view Slides [pdf]  Courseware, Lectures – 8, (8 hrs), Slides – 79, Topics: Knowledge Representation Introduction – KR model, typology, relationship, framework, mapping, forward & backward representation, system requirements; KR schemes – relational, inheritable, inferential, declarative, procedural; KR issues – attributes, relationship, granularity. KR Using Predicate Logic – Logic representation,  Propositional logic -   statements, variables, symbols, connective, truth value, contingencies, tautologies, contradictions,  antecedent,  consequent, argument;  Predicate logic – expressions,   quantifiers, formula; Representing “IsA” and “Instance” relationships, computable functions and predicates;  Resolution. KR Using Rules – Types of Rules – declarative, procedural, meta rules; Procedural verses declarative knowledge & language;  Logic programming – characteristics, Statement, language, syntax & terminology,  simple &  structured data objects, Program Components – clause,  predicate, sentence, subject; Programming paradigms – models of computation, imperative model, functional model, logic model; Forward & backward reasoning – chaining, conflict resolution;  Control knowledge.Reference.