CS308

ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS

Objectives

  • To know about basic concepts of NLP and Machine Learning
  • To obtain a thorough knowledge of various knowledge representation schemes
  • To have an overview of various AI applications
  • To study about various heuristic and game search algorithms
  • To know about various Expert System tools and applications

 

Outcomes

  • Technical knowhow of AI applications, heuristics, Expert Systems, NLP, and Machine Learning techniques
  • Acquaintance with programming languages such as LISP and PROLOG.

 

Unit – I

          Search Strategies- Hill climbing - Backtracking - Graph search - Properties of A* algorithm - Monotone restriction - Specialized production systems - AO* algorithm.

 

Unit – II

          Searching game trees- Minimax procedure - Alpha-beta pruning - Introduction to predicate calculus.

 

Unit – III

           Knowledge Representation- Reasoning - STRIPS - Structured representation of knowledge - Dealing with uncertainty.

 

Unit – IV

           Introduction to Expert Systems- Inference - Forward chaining - Backward chaining - Languages and tools - Explanation facilities - Knowledge acquisition.

 

Unit – V

           Natural Language Processing- Introduction - Understanding - Perception - Machine learning.

 

TEXT BOOKS

  • G. Luger, W. A. Stubblefield, "Artificial Intelligence", Third Edition, Addison-Wesley Longman, 1998.

 

REFERENCE

  • N. J. Nilsson, "Principles of Artificial Intelligence", Narosa Publishing House, 1980