1. Introduction to AI
Objectives, Introduction, Definition of AI, AI Techniques/Technologies, Components of Artificial Intelligence, AI Techniques Representations, Some Task Domains of AI, History of AI, Convergence of Different Fields in AI, Applications of AI, Review Questions.
2. Problem Solving in AI
Objectives, Introduction, Problem Solving Steps, Definitions, State Space, Defining the Problem As State Space Search, Water Jug Problem (WJP), Production System and its Components, Functioning in Production Systems, Characteristics of Production System, Control Strategies, Breadth First Search (BFS), Some Problems and Their Descriptions, Problem’s Characteristics, Review Questions.
3. Search and Control Techniques
Objectives, Introduction, Blinding Search (Uninformed Search) Techniques, Recursive DFS, Depth Bounded or Depth Limited Search, Depth First Iterative Deepening (DFID), Uniform Cost Search, Iterative Broadening, Iterative Lengthening Search, Bi-directional Search, Back Track Search, Informed or Heuristic Search Techniques, Generate and Test Method, Local Search Algorithm and Optimization Problems, Greedy or Best First Search, Problem Reduction, Constraint Satisfaction Problem : (CSP), Mean-End-Analysis, Properties of Heuristic Search Techniques, Performance of Search Method, Review Questions.
4. Knowledge Representation
Objectives, Introduction, Declarative vs Procedural Knowledge, Representation & Mapping, Approaches/Properties of Knowledge Representation, Issues In Knowledge Reprentation, Representing Knowledge Using Rules, Logic Programming, Matching, Problems in Knowledge Representation, Review Questions.
5. Symbolic Logic : Propositional Calculus and Predicate Calculus
Objectives, Introduction, Propositional Calculus or Propositional Logic, Syntax of Propositional Calculus, Semantics of Propositional Calculus, Predicate Calculus or First Order Predicate Logic, Syntax of Predicate Logic, Semantics of Predicate Calculus, Representation of Simple Facts in Logic, Properties of Well Formed Formulas (WFFs), Inference Rule, Inference Proceduse, Computable Function and Predicates, Unification, Resolution, Conversion to Clause Form, The Resolution Principle, Question Answering, Control Strategies for Resolution, Problems with Resolution, Deduction, Review Questions.
6. Symbolic Reasoning
Objectives, Introduction, Styles of Reasoning, Forward Reasoning, Backward Reasoning, Selection Criteria, Classes of Reasoning, Reasoning Under Certainty, Reasoning Under Uncertainty, Logics for Non-monotonic Reasoning, Default Reasoning, Logics Based on Minimum Models (Minimalist Reasoning), Implementation Issues, Review Questions.
7. Game Playing : Adversarial Search
Objectives, Introduction, Games Trees, Minimax Search Procedure, Minimax in Multiplayer Game, Problems in MINIMAX Search, Alpha-Beta Pruning, Alpha-Beta Algorithm, Additional Refinement, Review Questions.
8. Introduction to Planning
Objectives, Introduction, A Planning Domain Example: The Block World, Planning System and Its Components, Planning as a Search, Situation/State-Space Search, Plan/Constraint Posting-Space Search, Goal Stack Planning: STRIPS Planning Method, Susman Anomaly, Non Linear Planning: Tweak Method of Planning, Hierarchical Planning: ABSTRIPS Method of Planning, Reactive System, Understanding, Review Questions.
9. Natural Language Processing
Objectives, Introduction, NLP is a Hard Process, Level of Analysis, Steps in NLP Process, Morphological Analysis, Syntactic Processing, Grammar, Parsing, Parsing Algorithm, Augmented Transition Network (ATN)Parser, Augmented Transition Network (ATN), ATN Parser, Semantic Analysis, Lexical Processing, Sentence Level Processing, Pragmatic Analysis (or The Conversation Context Level), Practical Application of NLP, Review Questions.
10. Learning
Objectives, Introduction, Components of Learning Model, Performance Measures, Types of Learning, How can we Learn ?, Rote Learning/Memorization, Learning by Taking Advice, Automated Advice Taking, Knowledge Base Maintenance, Advice Based Learning System–FOO, Learning in Problem Solving, Learning by Parameter Adjustment, Learning by Macro Operators, Learning by Chunking, Inductive Learning (Learning by Example), Two Classes of Inductive Problems, Definitions, Winston’s Learning Program, Version Space Search, Decision Tree, EBL (Explanation Based Learning), Formal Leaning Theory (Computational Learning Theory), Open Problems in Learning, Review Questions.
11. An Introduction to Neural Networks
Objectives, Introduction, The Biological Neuron Model, Artificial Neural Networks (A Mathematical Model), Neural Networks Architecture, Learning/Training of ANNs, Types of Neural Networks, Development of An Ann Model, Applications of Neural Networks, Single Layer Perceptron, Multilayer Perceptron, Neural Networks vs Conventional Computers, Commonsense Reasoning, Review Questions.
12. Fuzzy Logic System
Objectives, Introduction, Fuzzy Sets and Crisp Sets, Operations on Fuzzy Sets, Fuzzy Classification, Overview of Fuzzy Inference Process, Customization, Review Questions.
13. An Introductin to Expert System
Objectives, Introduction, Importance of Expert Systems, Characteristics and Features of Expert Systems, Components of an Experts System, Knowledge Base (Representing and Using Domain Knowledge), Expert System Shells, Knowledge Acquisition, Explanation, Application of Expert Systems, Developing An Expert System, Expert System Architecture, Examples of Expert Systems, Review Questions.
14. Probabilistic Reasoning and Baye’s Theorem
Objectives, Introduction, Probability and Probability Theory, Baye’s Theorem, Certainty Factor and Ruled Based System, Applications of Probabilistic Reasoning, Review Questions.
15. Genetic Algorithms
Objectives, Fundamentals of Genetic Algorithms, Introduction to Genetic Algorithms, Significant of Genetic Operators, Reproduction or Selection, Crossover, Mutation, Basic Genetic Algorithm, Genetic Algorithm Approach to Problem, Genetic Parameters (Termination Parameter), Conditions Criteria for the Parameters, Niching Methods for Multimodal Optimization, Sharing Method, Crowding Method, Clearing Method, Other Niching Methods, Specification of an Interactive Genetic Algorithm, Algorithm Mechanics, Evolving Neutral Networks, Ant Algorithms and the TSP, Algorithm, Review Questions.
P. Papers