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LESSON PLAN Artificial Intelligence and Agent Technology Faculty : Dr.S.Sridhar, Dean-CCCF Subject Code : 14SCS24 Date : IA Marks : 50 Exam Hours : 3 Exam marks : 100 Total Lecture Hours : 50 Time Table....M.Tech.(CSE) –II Semester Course Outcomes CO1: Understand uncertainty and Problem solving techniques, various symbolic knowledge representation to specify domains and reasoning tasks of a situated software agent, different logical systems for inference over formal domain representations, various learning techniques and agent technology CO2: Identify symbolic knowledge representation to specify domains and reasoning tasks of a situated software agent, different logical systems for inference over formal domain representations, a particular inference algorithm for a given problem specification and agent technology CO3: Analyze intelligent agents for problem solving, reasoning, planning, decision making, performance constraints for a large system CO4: Implement AI technique to a given concrete problem relatively by considering a large system UNIT TITLE : What is Artificial Intelligence OBJECTIVE : To understand AI Problems, problem spaces and intelligent agents S.No. SUBJECT TOPIC PERIODS 1. The AI Problems, The Underlying assumption, What is an AI Technique?, The Level of the model, Criteria for success, some general references, One final word and beyond. 2 2. Problems, problem spaces, and search: Defining, the problem as a state space search, Production systems Problem characteristics, Production system characteristics, Issues in the design of search programs, Additional Problems 3 Intelligent Agents: Agents and Environments, The nature of environments, The structure of agents. 3 3. 4. 2 UNIT TITLE : Heuristic search techniques OBJECTIVE : To understand Heuristic search techniques, knowledge representation rules, predicate logic and logical agents S.No. SUBJECT TOPIC PERIODS 1. Heuristic search techniques: Generate-and-test, Hill climbing, Best-first search, Problem reduction, Constraint satisfaction, Mean-ends analysis. 2 2. Knowledge representation issues: Representations and mappings, Approaches to knowledge representation, Issues in knowledge representation, The frame problem. 3 3. Using predicate logic: Representing simple facts in logic, representing instance and ISA relationships, Computable functions and predicates, Resolution, Natural Deduction. 2 4. Logical Agents: Knowledge –based agents, the Wumpus world, LogicPropositional logic, Propositional theorem proving, Effective propositional model checking, Agents based on propositional logic. 3 UNIT TITLE : Symbolic Reasoning Under Uncertainty: OBJECTIVE : To identify reasoning through Bayesian networks, statistical reasoning to quantify uncertainty S.No. SUBJECT TOPIC PERIODS 1. Logic for nonmonotonic reasoning Bayesian Networks, Dempster-Shafer Theory, Fuzzy logic. 2 2. Implementation Issues, Augmenting a problem-solver, Depthfirst search, Implementation: Breadth-first search. Implementation: 3 3. Statistical Reasoning: Probability and bayes Theorem, Certainty factors and rule-based systems, 3 4. Quantifying Uncertainty: Acting under uncertainty, Basic probability notation, Inference using full joint distributions, Independence, Bayes’ rule and its use, The Wumpus world revisited. 2 UNIT TITLE : Weak Slot-and-filter structures: OBJECTIVE : To analyze Semantic nets, strong slot and filter structures, optimal decisions and alternatives. S.No. SUBJECT TOPIC PERIODS 1. Semantic Nets, Frames 2 2. Strong slot-and –filler structures: Conceptual dependency, scripts, CYC. 2 3. Adversarial Search: Games, Optimal Decision in Games, Alpha-Beta Pruning, Imperfect Real-Time Decisions, Stochastic Games, Partially Observable Games, State-Of-The-Art Game Programs, Alternative Approaches 3 4. 3 UNIT TITLE : Learning From examples: OBJECTIVE : To implement AI technique to a given concrete problem relatively by considering a large system S.No. SUBJECT TOPIC PERIODS Forms of learning, Supervised learning, Learning decision trees, Evaluating and choosing the best hypothesis The theory of learning ,PAC, Regression and Classification with linear models, Nonparametric models, Support vector machines, Ensemble learning. 2 3. Learning Probabilistic Models: Statistical learning, learning with complete data 3 4. learning with hidden variables: The EM algorithm. 2 1. 2. 3 Suggested References (Name of the Book, Authors, Publisher and year of Publication) Text Books: 1. Elaine Rich,Kevin Knight, Shivashanka B Nair:Artificial Intelligence, Tata CGraw Hill 3rd edition. 2013 2. Stuart Russel, Peter Norvig: Artificial Intelligence A Modern Approach, Pearson 3rd edition 2013. Reference Books: 3. Nils J. Nilsson: “Principles of Artificial Intelligence”, Elsevier