ARTIFICIAL INTELLIGENCE

Mumbai University-Fourth / Final / Final Year -Semester VII Information Technology Syllabus (Revised) ARTIFICIAL INTELLIGENCE

ELECTIVE – I : ARTIFICIAL INTELLIGENCE
CLASS B.E. ( INFORMATION TECHNOLOGY) SEMESTER VII
HOURS PER WEEK	LECTURES	:	04
TUTORIALS	:	--
PRACTICALS	:	02
	HOURS	MARKS
EVALUATION SYSTEM:	THEORY		3	100
PRACTICAL		--	--
ORAL		--	25
TERM WORK		--	25
Prerequisite: programming language like JAVA or Python
Objective: This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. Students will develop a basic understanding of the building blocks of AI as presented in terms of intelligent agents. This course will attempt to help students understand the main approaches to artificial intelligence such as heuristic search, game search, logical inference, decision theory, planning, machine learning, neural networks and natural language processing. Students will be able to recognize problems that may be solved using artificial intelligence and implement artificial intelligence algorithms for hands-on experience

1. Artificial Intelligence: Introduction to AI, History of AI, Emergence Of Intelligent Agents

2. Intelligent Agents: PEAS Representation for an Agent, Agent Environments, Concept of Rational Agent, Structure of Intelligent agents, Types of Agents.

3. Problem Solving: Solving problems by searching, Problem Formulation, Uninformed Search Techniques- DFS, BFS, Iterative Deepening, Comparing Different Techniques, Informed search methods – heuristic Functions, Hill Climbing, Simulated Annealing, A*, Performance Evaluation.

4. Constrained Satisfaction Problems: Constraint Satisfaction Problems like, map Coloring, Crypt Arithmetic, Backtracking for CSP, Local Search.

5. Adversarial Search: Games, Minimax Algorithm, Alpha Beta pruning.

6. Knowledge and Reasoning: A knowledge Based Agent, Introduction To Logic, Propositional Logic, Reasoning in Propositional logic, First Order Logic: Syntax and Semantics, Extensions and Notational Variation, Inference in First Order Logic, Unification, Forward and backward chaining, Resolution.

7. Knowledge Engineering: Ontology, Categories and Objects, Mental Events and Objects.

8. Planning: Planning problem, Planning with State Space Search, Partial Order Planning, Hierarchical Planning, Conditional Planning.

9. Uncertain Knowledge and Reasoning: Uncertainty, Representing knowledge in an Uncertain Domain, Overview of Probability Concepts, Belief Networks, Simple Inference in Belief Networks.

10. Learning: Learning from Observations, General Model of Learning Agents, Inductive learning, learning Decision Trees, Introduction to neural networks, Perceptrons, Multilayer feed forward network, Application of ANN, Reinforcement learning: Passive & Active Reinforcement learning.

11. Agent Communication: Communication as action, Types of communicating agents, A formal grammar for a subset of English

Text Book:

1. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 2nd Edition, Pearson Publication.

Reference Books:

1. George Lugar, “AI-Structures and Strategies for Complex Problem Solving”, 4/e, 2002, Pearson Educations

2. Robert J. Schalkolf, Artificial Inteilligence: an Engineering approach, McGraw Hill, 1990.

3. Patrick H. Winston, Artificial Intelligence, 3rd edition, Pearson.

4. Nils J. Nilsson, Principles of Artificial Intelligence, Narosa Publication.

5. Dan W. Patterson, Introduction to Artificial Intelligence and Expert System, PHI.

6. Efraim Turban Jay E.Aronson, "Decision Support Systems and Intelligent Systems” PHI.

7. M. Tim Jones, Artificial Intelligence – A System Approach, Infinity Science Press -Firewall Media.

8. Christopher Thornton and Benedict du Boulay, “Artificial Intelligence – Strategies, Applications, and Models through Search, 2nd Edition, New Age International Publications.

9. Elaine Rich, Kevin Knight, Artificial Intelligence, Tata McGraw Hill, 1999.

10. David W. Rolston, Principles of Artificial Intelligence and Expert System Development, McGraw Hill, 1988.

Term Work: Term work shall consist of at least 10 experiments covering all topics and one written test. Distribution of marks for term work shall be as follows: Attendance (Theory and Practical) 05 Marks Laboratory work (Experiments and Journal) 10 Marks Test (at least one) 10 Marks The final certification and acceptance of TW ensures the satisfactory Performance of laboratory Work and Minimum Passing in the term work. Suggested Experiment list: (Can be implemented in JAVA)

1. Problem Formulation Problems

2. Programs for Search

3. Constraint Satisfaction Programs

4. Game Playing Programs

5. Assignments on Resolution

6. Building a knowledge Base and Implementing Inference

7. Assignment on Planning and reinforcement Learning

8. Implementing Decision Tree Learner

9. Neural Network Implementation

10. Bayes’ Belief Network (can use Microsoft BBN tool)

11. Assignment on Agent Communication – Grammar Representation For Simple Domains

ORAL EXAMINATION