M.Tech I Year I Semester (R19)
This course will cover several advanced topics in Artificial Intelligence. The students should possess a firm grounding in the existing techniques and component areas of Artificial Intelligence. To apply this knowledge to the development of Intelligent Systems or to the exploration of research problems.
Upon completion of this subject students are expected to:
Understand the principles of problem solving and be able to apply them successfully. Be familiar with techniques for computer-based representation and manipulation of complex information, knowledge, and uncertainty. Gain awareness of several advanced AI applications and topics such as intelligent agents, planning and scheduling, machine learning, etc.
Intelligent Agents: Agents and Environments, Good Behavior: The Concept of Rationality, the Nature of Environments, the Structure of Agents. Solving Problems by Searching Problem-Solving Agents, Example Problems, Searching for Solutions, Uninformed Search Strategies, Searching with Partial Information
Informed Search and Exploration Informed (Heuristic) Search Strategies, Heuristic Functions, Local Search Algorithms and Optimization Problems, Local Search in Continuous Spaces, Online Search Agents and Unknown Environments. Constraint Satisfaction Problems Constraint Satisfaction Problems, Backtracking Search for CSPs, Local Search for Constraint Satisfaction Problems.
Adversarial Search Games, Optimal Decisions in Games, Optimal strategies, the minimax algorithm, Optimal decisions in multiplayer games, Alpha-Beta Pruning, Cutting off search, State-of-the-Art Game Programs.
The Planning Problem, Planning with State-Space Search, Partial-Order Planning, Planning Graphs, Planning with Propositional Logic, Analysis of Planning Approaches. Time, Schedules, and Resources, Hierarchical Task Network Planning, Planning and Acting in Nondeterministic Domains, Continuous Planning, Multi Agent Planning. Learning from Observations: Forms of Learning, Inductive Learning, Learning Decision Trees, Ensemble Learning.
Natural Language Processing A Formal Grammar for a Fragment of English, Syntactic Analysis (Parsing), Efficient parsing, Augmented Grammars, Semantic Interpretation, Ambiguity and Disambiguation, Discourse Understanding. Computer Vision Image Formation, Early Image Processing Operations, Extracting Three-Dimensional Information, Object Recognition.
Artificial Intelligence: A modern approach by Stuart Russell and Peter Norvig. Second edition, Prentice Hall, 2003.
Artificial Intelligence, second Edition, by Elaine Rich, and Kevin Knight, McGraw-Hill, Inc, 1991. Artificial Intelligence structures and strategies for complex problem Solving. Third Edition. George F. Luger and William A. Stubblefield.
M.Tech. (Data Science) I Year I Semester
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