EL7001 ARTIFICIAL INTELLIGENCE SYLLABUS FOR ME ECE 1ST SEMESTER - Anna University Internal marks 2018

EL7001 ARTIFICIAL INTELLIGENCE SYLLABUS FOR ME ECE 1ST SEMESTER


 ANNA UNIVERSITY, CHENNAI
REGULATIONS - 2013
EL7001 ARTIFICIAL INTELLIGENCE SYLLABUS
ME 1ST SEM ELECTRONICS AND COMMUNICATION ENGINEERING SYLLABUS
EL7001 ARTIFICIAL INTELLIGENCE SYLLABUS
EL7001 ARTIFICIAL INTELLIGENCE SYLLABUS
OBJECTIVES:
To provide in-depth knowledge about
 Searching Techniques
 Knowledge Representation
 Learning

UNIT I INTRODUCTION
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.

UNIT II SEARCHING TECHNIQUES
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 – 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
First order logic - syntax and semantics – Using first order logic – Knowledge engineering – Inference – 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
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
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

OUTCOMES:
Students will be able to
 Explain Uniform search strategies and searching with partial information
 Understand Backtracking, Local and Adversarial Search
 Describe Learning decision trees
 Explain Probabilistic language processing

REFERENCES:
1. Stuart Russell, Peter Norvig, “Artificial Intelligence – A Modern Approach”, Second Edition, Pearson Education / Prentice Hall of India, 2004.
2. Nils J. Nilsson, “Artificial Intelligence: A new Synthesis”, Harcourt Asia Pvt. Ltd., 2000.
3. Elaine Rich and Kevin Knight, “Artificial Intelligence”, Second Edition, Tata McGraw Hill, 2003.
4. George F. Luger, “Artificial Intelligence-Structures And Strategies For Complex Problem Solving”, Pearson Education / PHI, 2002.

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