April 3, 2008 Return to >> Home >> Front Page
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.
January 22, 2008 Return to >> Home >> Front Page
Artificial Intelligence – Problem Solving, Search and Control Strategies January 22, 2008 , posted by myreaders , http://myreaders.wordpress.com/ , R C Chakraborty.Courseware, Lectures – 8, (8 hrs), Slides – 74, Topics: General Problem solving – Problem definitions : problem space, problem solving, states, state change, State space, structure of state space, problem solution, problem description; Examples of problem definition; Search and Control strategies – Search related terms : algorithm’s performance and complexity, computational complexity, “Big – o” notations, tree structure, stacks and queues; Search : hierarchical representation of search algorithms, search space, formal statement, search notations, estimate cost and heuristic function; Control strategies : strategies for search, forward and backward chaining; Exhaustive Searches – Depth-first search Algorithm; Breadth-first search Algorithm; Compare depth-first and breadth-first search; Heuristic Search Techniques – Characteristics of heuristic search; Heuristic search compared with other search; Example of heuristic search; Types of heuristic search algorithms; Constraint Satisfaction problems (CSPs) and Models – Examples of CSPs; Constraint Satisfaction Models : Generate and Test, Backtracking algorithm, Constrain Satisfaction Problems (CSPs) : definition, properties and algorithms; Reference.
March 30, 2007 Return to >> Home >> Front Page
Filed under: Invited Talk — myreaders @ 9:33 am Edit This
National Science Day – Lecture on 5th Generation System & Artificial Intelligence
National Science Day – Lecture on 5th Generation System & Artificial Intelligence R C Chakraborty, rcchak@gmail.com Click – Title to view Slides [pdf]
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June 11, 2007 Return to >> Home >> Front Page
Filed under: Courseware — myreaders @ 11:03 am Edit This
Courseware on Artificial Intelligence
Courseware on Artificial Intelligence , by RC Chakraborty, rcchak@gmail.com. I am striving for making an undergraduate ” Courseware on AI” to be available on the Web, free of charge, to any user anywhere in the world before next session (Jan–April, 2008). Click – Title to view complete [pdf]
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August 4, 2007 Return to >> Home >> Front Page
Filed under: Courseware — myreaders @ 5:36 pm Edit This
Artificial Intelligence – Introduction, Courseware Lecture Slides
Artificial Intelligence – Introduction, Courseware Lecture Slides, August 04, 2007, posted by myreaders , http://myreaders.wordpress.com/ , R C Chakraborty. Click – Title to view Slides [pdf]
A Courseware, Lectures – 5, (5hrs), Slides – 51, Topics: Definitions: Artificial Intelligence, Intelligence, Intelligent behavior, Understanding, “Hard” or “Strong” AI, “Soft” or “Weak” AI, Cognitive Science; Goals; Approaches: Cognitive science, Laws of thought, Turing Test, Rational agent; Techniques: Describe and match, Goal reduction, Constraint satisfaction, Tree Searching, Generate and test, Rule based systems, Neural Networks, Genetic Algorithms, Reinforcement learning; Branches: Logical AI, Search in AI, Pattern Recognition, Knowledge Representation, Inference, Common sense knowledge and reasoning, Learning, Planning, Epistemology, Ontology, Heuristics, Genetic programming; Applications: Game playing, Speech Recognition, Understanding Natural Language, Computer Vision, Expert Systems; References.
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I will go through and the write a comment.
Comment by Prof SPS Saini — March 29, 2008 @ 8:59 am