IOE Syllabus of Artificial Intelligence[CT653]
Course Objectives:
The main objectives of this course are:
- To provide basic knowledge of Artificial Intelligence
- To familiarize students with different search techniques
- To acquaint students with the fields related to AI and the applications of AI
Syllabus:
- Introduction (4 hrs)
- Definition of Artificial Intelligence
- Importance of Artificial Intelligence
- AI and related fields
- Brief history of Artificial Intelligence
- Applications of Artificial Intelligence
- Definition and importance of Knowledge, and learning.
- Problem-solving (4 hrs)
- Defining problems as a state space search,
- Problem formulation
- Problem types, Well- defined problems, Constraint satisfaction problem,
- Game playing, Production systems.
- Search techniques (5 hrs)
- Uninformed search techniques- depth first search, breadth first search, depth limit search, and search strategy comparison,
- Informed search techniques-hill climbing, best first search, greedy search, A* search Adversarial search techniques-minimax procedure, alpha beta procedure
- Knowledge representation, inference and reasoning (8 hrs)
- Formal logic-connectives, truth tables, syntax, semantics, tautology, validity, well- formed-formula,
- Propositional logic, predicate logic, FOPL, interpretation, quantification, horn clauses,
- Rules of inference, unification, resolution refutation system (RRS), answer extraction from RRS, rule based deduction system,
- Statistical Reasoning-Probability and Bayes' theorem and causal networks, reasoning in belief network
- Structured knowledge representation (4 hrs)
- Representations and Mappings,
- Approaches to Knowledge Representation,
- Issues in Knowledge Representation,
- Semantic nets, frames,
- Conceptual dependencies and scripts
- Machine learning (6 hrs)
- Concepts of learning,
- Learning by analogy, Inductive learning, Explanation based learning
- Neural networks,
- Genetic algorithm
- Fuzzy learning
- Boltzmann Machines
- Applications of AI (14 hrs)
- Neural networks
- Network structure
- Adaline network
- Perceptron
- Multilayer Perceptron, Back Propagation
- Hopfield network
- Kohonen network
- Expert System
- Architecture of an expert system
- Knowledge acquisition, induction
- Knowledge representation, Declarative knowledge, Procedural knowledge
- Development of expert systems
- Natural Language Processing and Machine Vision
- Levels of analysis: Phonetic, Syntactic, Semantic, Pragmatic
- Introduction to Machine Vision
Practical:
- Laboratory exercises should be conducted in either LISP or PROLOG.
- Laboratory exercises must cover the fundamental search techniques, simple question answering, inference and reasoning.
References:
- E. Rich and Knight, Artificial Intelligence, McGraw Hill, 2009.
- D. W. Patterson, Artificial Intelligence and Expert Systems, Prentice Hall, 2010.
- P. H. Winston, Artificial Intelligence, Addison Wesley, 2008.
- Stuart Russel and Peter Norvig, Artificial Intelligence A Modern Approach, Pearson, 2010