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DOC/LP/01/28.02.02 LP-CS2351 LESSON PLAN LP: Rev. No: 01 Sub Code & Name : CS2351–ARTIFICIAL INTELLIGENCE Unit : I Branch : CS Date: 17/11/2014 Semester : VI Page 1 of 6 UNIT I PROBLEM SOLVING 9 Introduction – Agents – Problem formulation – uninformed search strategies – heuristics – informed search strategies – constraint satisfaction Objective: To introduce Artificial intelligence, to learn the basics of designing intelligent agents that can solve general purpose problems and to explore the idea of problem solving by several search strategies Session No 1 2 3 4 5 6 7 8 9 10 Topics to be covered Introduction to Artificial Intelligence, Foundations and History of Artificial Intelligence Intelligent Agents – Agents and environment. Concept of Rationality, Nature of environments and Structure of agents Problem solving agents - Problem formulation with suitable examples, Searching for solutions Uninformed search strategies – Breadth-first search, Uniform-cost search, depth-first search, Depth-limited search Uninformed search strategies – Iterative deepening depth-first search, Bidirectional search, avoiding repeated states Informed search strategies – Greedy best first search, A* search Informed search strategies – Memory-bounded heuristic search, Heuristic Functions Local search algorithms and optimization problems – Hill climbing, Simulated Annealing and Local beam search Local search algorithms and optimization problems – Genetic algorithms, Local search in continuous spaces Constraint satisfaction problems – Backtracking and local search, Local search for CSP, Review Time in min 50 Ref T1 Teaching Method LCD 50 T1 LCD 50 T1 LCD 50 T1 LCD 50 T1 LCD 50 T1 LCD 50 T1 LCD 50 T1 LCD 50 T1 LCD 50 T1 LCD DOC/LP/01/28.02.02 LP-CS2351 LESSON PLAN LP: Rev. No: 01 Sub Code & Name : CS2351–ARTIFICIAL INTELLIGENCE Date: 17/11/2014 Unit : II Page: 2 of 6 Branch : CS Semester : VI UNIT II LOGICAL REASONING 9 Logical agents – propositional logic – inferences – first-order logic – inferences in firstorder logic – forward chaining – backward chaining – unification – resolution Objective: To illustrate the basic concepts of logic and knowledge-based agents and to discuss the representation of knowledge and the reasoning process. Also to define effective procedures for answering questions by making inferences in first-order logic Session No 11 12 13 14 15 16 17 18 19 20 Topics to be covered Logical agents – Knowledge based agents, Wumpus world Logic Propositional logic – Syntax, Semantics, knowledge base, Inference Reasoning patterns in propositional logic Agents based on propositional logic First-order logic – Representation, Syntax and Semantics of FOL First-Order logic- Assertions and queries, Kinship domain, Numbers, sets and lists First-order logic representation for Wumpus world Inference in First-order logic - Propositional Vs First order inference, Unification and lifting Inference in First-order logic - Forward chaining, Backward Chaining, Resolution, Review CAT I Time In min 50 Ref T1 Teaching Method LCD 50 50 T1 T1 LCD LCD 50 50 50 T1 T1 T1 LCD LCD LCD 50 T1 LCD 50 T1 LCD 50 T1 LCD 50 T1 LCD DOC/LP/01/28.02.02 LP-CS2351 LESSON PLAN LP: Rev. No: 01 Sub Code & Name : CS2351–ARTIFICIAL INTELLIGENCE Date: 17/11/2014 Unit : III Page 3 of 6 Branch : CS Semester : VI UNIT III PLANNING 9 Planning with state-space search – partial-order planning – planning graphs – planning and acting in the real world . Objective: To introduce the most basic concepts, representations and algorithms for planning, to explain the method of achieving goals from a sequence of actions (planning) and how better heuristic estimates can be achieved by a special data structure called planning graph. To understand the design of agent architectures to deal with uncertain environments Session No 21 22 23 24 25 26 27 28 29 Topics to be covered Planning with state-space search – Forward state-space search, Backward state-space search Partial-order planning – Concept with an example and heuristics for Partial-order planning Planning graphs Planning graphs for heuristic estimation, Graphplan algorithm Planning and acing in the real world – Time, schedules and resources Planning and acing in the real world – Hierarchical Task network planning Planning and acing in the real world – Planning and acting in nondeterministic domains Planning and acing in the real world – Conditional Planning in fully and partially observable environments Planning and acing in the real world – Continuous Planning Planning and acing in the real world – Multiagent Planning, Review Time In min 50 Ref T1 Teaching Method LCD 50 T1 LCD 50 T1 LCD 50 T1 LCD 50 T1 LCD 50 T1 LCD 50 T1 LCD 50 T1 LCD 50 T1 LCD DOC/LP/01/28.02.02 LP-CS2351 LESSON PLAN LP: Rev. No: 01 Sub Code & Name : CS2351–ARTIFICIAL INTELLIGENCE Date: 17/11/2014 Unit : IV Page 4 of 6 Branch : CS Semester : VI UNIT IV UNCERTAIN KNOWLEDGE AND REASONING 9 Uncertainty – review of probability - probabilistic Reasoning – Bayesian networks – inferences in Bayesian networks – Temporal models – Hidden Markov models Objective: To understand the concept of uncertainty and to learn the syntax and semantics of probability theory and Bayesian networks and other approaches to uncertain reasoning Session No 30 31 32 33 34 35 36 37 38 Topics to be covered Uncertainty – Acting under Uncertainty handling uncertain knowledge, Uncertainty and rational decisions Uncertainty – Basic Probability Notation – Propositions, Atomic events, Prior probability, and The Axioms of probability Uncertainty – Inference using full joint distribution, Independence, Bayes’ rule and its use Probabilistic Reasoning – Representing knowledge in an Uncertain domain, Semantics of Bayesian Networks Probabilistic Reasoning – Exact Inference in Bayesian networks Probabilistic Reasoning – Approximate Inference in Bayesian networks Probabilistic Reasoning – Other approaches to Uncertain reasoning (Rule-based methods for uncertain reasoning, Dempster-Shafer theory, Fuzzy sets and fuzzy logic) Probabilistic Reasoning over time – Inference in temporal models, Hidden Markov models, Review CAT II Time in min 50 Ref T1 Teaching Method LCD 50 T1 LCD 50 T1 LCD 50 T1 LCD 50 T1 LCD 50 T1 LCD 50 T1 LCD 50 T1 LCD 50 75 T1 LCD DOC/LP/01/28.02.02 LP-CS2351 LESSON PLAN LP: Rev. No: 01 Sub Code & Name : CS2351–ARTIFICIAL INTELLIGENCE Date: 17/11/2014 Unit : V Page 5 of 6 Branch : CS Semester : VI UNIT V LEARNING 9 Learning from observation - Inductive learning – Decision trees – Explanation based learning – Statistical Learning methods - Reinforcement Learning. Objective: To understand the basic concepts of several learning techniques Session No 39 40 41 42 43 44 45 46 47 48 Topics to be covered Learning from observation – Forms of learning, Inductive learning Learning from observation - Learning Decision trees Knowledge in learning - Explanation based learning Statistical Learning methods – Statistical learning, Learning with complete data Statistical Learning methods – Learning with hidden variables (EM algorithm), Instance based learning Statistical Learning methods – Neural Networks (Network structures, Single layer feed-forward neural network, Multilayer feed-forward neural network) Statistical Learning methods – Kernel Machines Reinforcement Learning – Introduction, Passive Reinforcement Learning Reinforcement Learning- Active Reinforcement Learning, Generalization in Reinforcement Learning, Policy search Review CAT III Time In min 50 Ref T1 Teaching Method LCD/OHP 50 T1 LCD/OHP 50 T1 LCD 50 T1 LCD 50 T1 LCD 50 T1 LCD 50 50 T1 T1 LCD LCD 50 T1 LCD 50 75 T1 LCD DOC/LP/01/28.02.02 LP-CS2351 LESSON PLAN LP: Rev. No: 01 Date: 17/11/2014 Sub Code & Name : CS2351–ARTIFICIAL INTELLIGENCE Branch : CS Page 6 of 6 Semester : VI Course Delivery Plan Week Units 1 2 3 4 5 6 7 8 9 10 11 12 I II I II I II I II I II I II I II I II I II I II I II I II 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 4 4 4 4 4 5 5 5 5 5 BOOKS FOR STUDY: TEXT BOOK: 1. S. Russel and P. Norvig, “Artificial Intelligence – A Modern Approach”, Second Edition, Pearson Education, 2003. REFERENCES: 1. David Poole, Alan Mackworth, Randy Goebel,”Computational Intelligence: a logical approach”, Oxford University Press, 2004. 2. G. Luger, “Artificial Intelligence: Structures and Strategies for complex problem solving”, Fourth Edition, Pearson Education, 2002. 3. J. Nilsson, “Artificial Intelligence: A new Synthesis”, Elsevier Publishers, 1998. Prepared by Approved by Signature Name Designation Date Ms.R.Jayabhaduri Ms.K.S.Gayathri Ms.G.Janakasudha Associate Professor/CS Associate Professor/CS Assistant Professor/CS 17/11/2014 Dr.Susan Elias HOD, Department of CS 17/11/2014