AL3391 ARTIFICIAL INTELLIGENCE Anna University Syllabus R2021

 

 AL3391 ARTIFICIAL INTELLIGENCE Anna University Syllabus R2021 

AL3391                   ARTIFICIAL INTELLIGENCE                 L T P C 3003

COURSE OBJECTIVES:

The main objectives of this course are to:

• Learn the basic AI approaches

•  Develop problem solving agents

•  Perform logical and probabilistic reasoning

UNIT I                              INTELLIGENT AGENTS                              9

Introduction to AI – Agents and Environments – concept of rationality – nature of environments –
structure of agents. Problem solving agents – search algorithms – uninformed search strategies.

UNIT II                                   PROBLEM SOLVING                           9

Heuristic search strategies – heuristic functions. Local search and optimization problems – local
search in continuous space – search with non-deterministic actions – search in partially observable
environments – online search agents and unknown environments

UNIT III                            GAME PLAYING AND CSP                              9

Game theory – optimal decisions in games – alpha-beta search – monte-carlo tree search –
stochastic games – partially observable games. Constraint satisfaction problems – constraint
propagation – backtracking search for CSP – local search for CSP – structure of CSP.

UNIT IV                             LOGICAL REASONING                                 9

Knowledge-based agents – propositional logic – propositional theorem proving – propositional
model checking – agents based on propositional logic. First-order logic – syntax and semantics –
knowledge representation and engineering – inferences in first-order logic – forward chaining –
backward chaining – resolution.

UNIT V                             PROBABILISTIC REASONING                             9

Acting under uncertainty – Bayesian inference – naïve Bayes models. Probabilistic reasoning –
Bayesian networks – exact inference in BN – approximate inference in BN – causal networks.

COURSE OUTCOMES:

At the end of this course, the students will be able to:
CO1: Explain intelligent agent frameworks
CO2: Apply problem solving techniques
CO3: Apply game playing and CSP techniques
CO4: Perform logical reasoning
CO5: Perform probabilistic reasoning under uncertainty
TOTAL:45 PERIODS

TEXT BOOKS:

1. Stuart Russell and Peter Norvig, “Artificial Intelligence – A Modern Approach”, Fourth
Edition, Pearson Education, 2021.

REFERENCES

1. Dan W. Patterson, “Introduction to AI and ES”, Pearson Education,2007
2. Kevin Night, Elaine Rich, and Nair B., “Artificial Intelligence”, McGraw Hill, 2008
3. Patrick H. Winston, "Artificial Intelligence", Third Edition, Pearson Education, 2006
4. Deepak Khemani, “Artificial Intelligence”, Tata McGraw Hill Education, 2013.
5. http://nptel.ac.in/