CM7014 INTRODUCTION TO ARTIFICIAL INTELLIGENCE Syllabus | Anna University ME

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Anna University Syllabus - Anna University ME Syllabus

CM7014 INTRODUCTION TO ARTIFICIAL INTELLIGENCE Syllabus | Anna University ME Computer Integrated Manufacturing Second Semester Syllabus Regulation 2013. Below is the Anna University 2013 Regulation Syllabus for 2nd Semester for ME Computer Integrated Manufacturing, Textbooks, Reference books, Exam portions, Question Bank, Previous year question papers, Model question papers, Class notes, Important 2 marks, 8 marks, 16 marks topics.
It is applicable for all students admitted in the Academic year 2013-2014 onwards for all its Affiliated institutions in Tamil Nadu.

CM7014 INTRODUCTION TO ARTIFICIAL INTELLIGENCE L T P C 3 0 0 3

OBJECTIVE:
 To introduce the student various types of artificial intelligence and its applications.
UNIT I INTRODUCTION: 8
Intelligent Agents – Agents and environments - Good behavior – The nature of environments –
structure of agents - Problem Solving - problem solving agents – example problems – searching for
solutions – uniformed search strategies - avoiding repeated states–searching with partial information.
30
UNIT II SEARCHING TECHNIQUES : 10
Informed search and exploration – Informed search strategies – heuristic function – local search
algorithms and optimistic problems – local search in continuous spaces – online search agents and
unknown environments - Constraint Satisfaction Problems (CSP) – Backtracking search and Local
search for CSP – Structure of problems - Adversarial Search – Games – Optimal decisions in games
– Alpha – Beta Pruning – imperfect real-time decision – games that include an element of chance.
UNIT III KNOWLEDGE REPRESENTATION: 10
First order logic – representation revisited – Syntax and semantics for first order logic – Using first
order logic – Knowledge engineering in first order logic - Inference in First order logic – prepositional
versus first order logic – unification and lifting – forward chaining – backward chaining - Resolution -
Knowledge representation - Ontological Engineering - Categories and objects – Actions - Simulation
and events - Mental events and mental objects.
UNIT IV LEARNING: 9
Learning from observations - forms of learning - Inductive learning - Learning decision trees -
Ensemble learning - Knowledge in learning – Logical formulation of learning – Explanation based
learning – Learning using relevant information – Inductive logic programming - Statistical learning
methods - Learning with complete data - Learning with hidden variable - EM algorithm - Instance
based learning - Neural networks - Reinforcement learning – Passive reinforcement learning - Active
reinforcement learning - Generalization in reinforcement learning.
UNIT V APPLICATIONS: 8
Communication – Communication as action – Formal grammar for a fragment of English – Syntactic
analysis – Augmented grammars – Semantic interpretation – Ambiguity and disambiguation –
Discourse understanding – Grammar induction - Probabilistic language processing - Probabilistic
language models – Information retrieval – Information Extraction – Machine translation.
TOTAL: 45 PERIODS
OUTCOME:
At the end of this course the student should be able to apply the AI techniques to create applications
which involve perception, reasoning and learning.
REFERENCES
1. Russell, S. and Norvig, P., “Artificial Intelligence – A Modern Approach”, 2nd Edition ,Pearson
Education / PrenticeHallofIndia,2004.
2. Nilsson, N.J., “Artificial Intelligence: A new Synthesis”, Harcourt Asia Pvt. Ltd., 2000.
3. Rich, E. and Knight, K., “Artificial Intelligence”, 2nd Edition, Tata McGraw-Hill,2003.
4. Luger, G.F., “Artificial Intelligence-Structures And Strategies For Complex Problem Solving”,
Pearson Education / PHI, 2002.