Course Course
Name of Course L T P Credit
Type Code
OE CSO501 Principles of Artificial Intelligence 3 0 0 9
Course Objective
Course will introduce the basic principles in artificial intelligence, which covers blind and heuristic search strategies,
simple knowledge representation schemes, introduction to CSP problems and use for general purpose heuristic for
constraint propagation, genetic algorithm, rule based system, Introduction to probabilistic reasoning, planning and
learning neural network models, Areas of application, natural language processing, will be explored. The PROLOG
programming language will also be introduced.
Learning Outcomes
Understanding of the following: Problem as Search - Converting real world problems into AI search problems and
explain important search concepts, such as the difference between informed and uninformed search, the definitions of
admissible and consistent heuristics and completeness and optimality. Understanding of various heuristic search
techniques, MiniMax search for game playing. Constraint Satisfaction - Formulation of real world problem as CSP
problem and solution for CSP using general purpose heuristics, Genetic Algorithm for optimization. Knowledge
representation using First order logic, proofs in first order using techniques such as resolution, unification. Rule based
system and logic programming using Prolog programming language, Planning techniques, Bayesian network and
reasoning Fundamentals of learning using neural net, decision tree, naïve- Bayes, nearest neighbor, inductive learning,
Fundamentals of NLP.
Unit Lecture
Topics to be Covered Learning Outcome
No. Hours
Artificial Intelligence Introduction, Brief history, Learning various Informed
Problem solving by search: state space, Search and and
1 Knowledge representation. Uninformed search : 4 Uninformed search techniques.
Breadth First Search, Depth First Search, Depth First
with Iterative Deepening and Uniform Cost Search,
Heuristic Search: Hill climbing, Simulated Learning heuristic search
2 Annealing, A*, problem reduction, Algorithm, 5
Minimax search
Binary and Higher order CSP, Constraint Satisfaction Learning various techniques constraint
Graph, MRV, Degree, Least Constraining, Forward satisfaction problems.
3 4
Checking and Arc
Consistency General purpose heuristics for CSP
Introduction to genetic algorithm, operations: Learning various techniques in the context
4 selection, crossover, mutation examples 3
of AI.
Propositional logic, Definition of logic formula, Learning various logic representation
Meaning of logic formula, Classification of logic techniques includes forward and backward
formula, unification and resolution, horn clause, chaining. Understanding towards inference
5 Logic based representations (PL, FoL) and inference, 7 mechanism in declarative programming
Logic Programming: Prolog. Rule based
languages through Prolog programming
representations, forward and backward chaining,
language
matching algorithms.
Planning Techniques: Goal Stack Learning various planning techniques in the
6 Planning, 4 context of AI.
Constraint posting
Probabilistic Reasoning: Bayesian Network and Learning various probabilistic techniques
7 3
reasoning. includes Bayesian network and reasoning.
Learning: Neural Network models, Statistical Learning various techniques in NN,
8 methods: Naive-Bayes, Nearest Neighbor, Decision 4 Decision tree and learning methods.
trees, Inductive Learning
9 Introduction to Natural Language Processing 2 Learning various techniques in NLP.
Introduction to Fuzzy Logic, Reasoning through
fuzzy logic, Expert system, Definition of fuzzy set, Understanding of fuzzy fundamentals and
Membership function ,Notation of fuzzy set,
fuzzy inference systems
10 Operations of fuzzy set • Fuzzy number and 4
operations, Extension principle , Fuzzy rules,
De-fuzzification, Fuzzy control
Text Books:
1. Artificial Intelligence Modern Approach Third Edition by S. Russell. Norvig,PHI
Reference Books:
1. Artificial Intelligence Third Edition by Kevin Knight (Author), Elaine Rich (Author)
2. Artificial Intelligence, Structures and Strategies for Complex Problem Solving George F Luger, Sixth
Edition, Pearson.
3. Machine Learning by Mitchell, Tom M. Indian Edition.
