ME/M.Tech. Computer Tech. & Applications, Software Systems 2nd sem Elective-I MCSE/MCTA-203 Artificial Intelligence & Computing Logic Syllabus

Tags: Artificial Intelligence & Computing Logic Syllabus, ME/M.Tech 2nd semester Syllabus, Elective-I MCSE/MCTA-203 Syllabus, ME/M.Tech Second Semester Syllabus, Computer Tech. & Applications M.Tech Syllabus, Software Systems M.Tech Syllabus

Rajiv Gandhi Technological University, Bhopal (MP)
Computer Tech. & Applications (UTD, RGTU, Bpl)
Software Systems SATI (Vidisha)
Elective-I MCSE/MCTA-203
Artificial Intelligence & Computing Logic Syllabus

UNIT 1 Game playing: Overview, Mini-max Search procedure, Adding Alpha-Beta Cutoffs, Additional Refinements, Iterative Deepening, References on specific games.

UNIT 2 Planning : Overview, An example domain, components of a planning system Goal Stack planning, nonlinear Planning using constraint posting, Hierarchical planning, Reactive Systems, Other planning techniques. Understanding: What is Understanding? What makes Understanding hard? Understanding as constraint satisfaction.

UNIT 3 Natural language processing: Introduction, Syntactic processing, Semantic Analysis, Discourse and pragmatic Processing. Parallel and Distributed AI: Psychological modeling, Parallelism in Reasoning Systems, Distributed Reasoning Systems. Learning: Rote learning, learning by taking advice, Learning in Problem solving, learning from examples, Explanation-based, Discovery, Analogy, Formal learning theory, Neural Net learning and Genetic learning.

Connectionist Models: Introduction, Learning in Neural Networks, Applications of Neural Networks, Recurrent Networks, Distributed representations, connectionist AI and symbolic AI, Case studies, of NNs in pattern recognition, Image processing, Computer vision etc. Common Sense : Qualitative Physics, Common sense ontology, Memory organization, Case-based reasoning.

Fuzzy Logic : Introduction, Fuzzy set theory, Fuzzy set relations, statistical decision making, Introduction to Fuzzy Logic controllers, various fuzzyfication and defuzzification methods, Some case studies of FLCs in diagnosis, control, human activities, Robots, Image recognition, Databases, Information retrieval Expert system for damage assessment.

Introduction to Neuro-fuzzy systems & its applications in real world computing. Over view of Evolutionary Algorithms & its applications in search and optimization areas.

Reference Books :
1. Artificial Intelligence - E. Rich, K. Knight, TMH
2. Fuzzy Systems theory application- T.Terano, K.Asai, M. Sugeno, Academic P
3. Introduction to Neural Networks - Wassermann, Van Nostrand Reinhold.
4. Fuzzy sets and Fuzzy logic - G.Klir and B Yuan, PHI
5. Artificial Intelligence, an Engineering approach- R.J. Schal Koft, Mc Graw hill
6. Hand Book of Evolutionary Algorithms - Oxford University Press.

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