CS 702-Artificial Intelligence |
Introduction |
Overview of Artificial intelligence- Problems of AI, AI technique, Tic - Tac - Toe problem.
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Intelligent Agents |
Agents & environment, nature of environment, structure of agents, goal based agents, utility based agents, learning
agents. |
Problem Solving |
Problems, Problem Space & search: Defining the problem as state space search, production system, problem
characteristics, issues in the design of search programs.
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Search techniques |
Solving problems by searching :problem solving agents, searching for solutions; uniform search strategies: breadth first
search, depth first search, depth limited search, bidirectional search, comparing uniform search strategies.
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Heuristic search strategies |
Greedy best-first search, A* search, memory bounded heuristic search: local search algorithms & optimization
problems: Hill climbing search, simulated annealing search, local beam search, genetic algorithms; constraint
satisfaction problems, local search for constraint satisfaction problems.
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Adversarial search |
Games, optimal decisions & strategies in games, the minimax search procedure, alpha-beta pruning, additional
refinements, iterative deepening.
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Knowledge & reasoning |
Knowledge representation issues, representation & mapping, approaches to knowledge representation, issues in
knowledge representation.
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Using predicate logic |
Representing simple fact in logic, representing instant & ISA relationship, computable functions & predicates,
resolution, natural deduction.
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Representing knowledge using rules |
Procedural verses declarative knowledge, logic programming, forward verses backward reasoning, matching, control
knowledge.
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Probabilistic reasoning |
Representing knowledge in an uncertain domain, the semantics of Bayesian networks, Dempster-Shafer theory, Fuzzy
sets & fuzzy logics.
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Planning |
Overview, components of a planning system, Goal stack planning, Hierarchical planning, other planning techniques.
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Natural Language processing |
Introduction, Syntactic processing, semantic analysis, discourse & pragmatic processing.
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Learning |
Forms of learning, inductive learning, learning decision trees, explanation based learning, learning using relevance
information, neural net learning & genetic learning.
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Expert Systems |
Representing and using domain knowledge, expert system shells, knowledge acquisition.
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Text Books: |
1. Artificial Intelligence, Ritch & Knight, TMH
2. Artificial Intelligence A Modern Approach, Stuart Russel Peter Norvig Pearson
3. Introduction to Artificial Intelligence & Expert Systems, Patterson, PHI
4. Poole, Computational Intelligence, OUP
5. Logic & Prolog Programming, Saroj Kaushik, New Age International
6. Expert Systems, Giarranto, VIKAS
7. Artificial Intelligence, Russel, Pearson
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