1.1.Intelligence, Artificial Intelligence(AI), AI Perspectives: acting and thinking humanly, acting and thinking rationally 1.2.History of AI 1.3.Foundations of AI: Philosophy, Economics, Psychology, Sociology, Linguistics, Neuroscience, Mathematics, Computer Science, Control Theory 1.4.Applications of AI

2.1.Introduction of agents, Structure of Intelligent agent, Properties of Intelligent Agents 2.2.Configuration of Agents, PEAS description of Agents, PAGE 2.3.Types of Agents: Simple Reflexive, Model Based, Goal Based, Utility Based, Learning Agent 2.4.Environment Types: Deterministic, Stochastic, Static, Dynamic, Observable, Semi-observable, SingleAgent, Multi Agent

3.1.Definition,State space representation, Problem as a state space search, Problem formulation, Well-defined problems 3.2.Solving Problems by Searching, Search Strategies: Informed, Uninformed, Performance evaluation of search strategies: Time Complexity, Space Complexity, Completeness, Optimality 3.3.Uninformed Search: Depth First Search, Breadth First Search, Depth Limited Search, Iterative Deepening Search, Uniform Cost Search, Bidirectional Search 3.4.Informed Search, Heuristic Function, Admissible Heuristic, Informed Search Techniques: Greedy Best FirstSearch, A* Search, Optimality and Admissibility in A*, Hill Climbing Search, Simulated Annealing Search 3.5.GamePlaying,Adversarial Search Techniques: Mini-max Search, Alpha-Beta Pruning 3.6.Constraint Satisfaction Problems, Examples of Constraint Satisfaction Problems

4.1.Definition and importance of Knowledge, Issues in Knowledge Representation, Knowledge Representation Systems, Properties of Knowledge Representation Systems 4.2.Types of Knowledge Representation Systems: Semantic Nets, Frames, Conceptual Dependencies, Scripts, Rule Based Systems(Production System), Propositional Logic, Predicate Logic 4.3.Propositional Logic(PL): Syntax, Semantics, Formal logic-connectives, truth tables, tautology, validity, well-formed-formula, Inference using Resolution, Backward Chaining and Forward Chaining 4.4.Predicate Logic: FOPL, Syntax, Semantics, Quantification, Inference with FOPL: By converting into PL (existential and universal instantiation), Unification and lifting, Inference using resolution 4.5.Handling Uncertain Knowledge, Radom Variables, Prior and Posterior Probability, Inference using Full Joint Distribution, Bayes' Rule and its use, Bayesian Networks, Reasoning in Belief Networks 4.6.Fuzzy Logic: Fuzzy Sets, Membership in Fuzzy Set, Fuzzy Rulebase Systems

5.1.Introductionto Machine Learning , Concepts of Learning, Supervised, Unsupervised and Reinforcement Learning 5.2.Statistical-based Learning: Naive Bayes Model 5.3.Learning by Genetic Algorithms: Operators in Genetic Algorithm: Selection, Mutation, Crossover, Fitness Function, Genetic Algorithm 5.4.Learning with Neural Networks: Introduction, Biological Neural Networks Vs. Artificial Neural Networks (ANN), Mathematical Model of ANN, Activation Functions: Linear, Step Sigmoid, Types of ANN: Feed-forward, Recurrent, Single Layered, Multi-Layered, Application of Artificial Neural Networks, Learning by Training ANN, Supervised vs. Unsupervised Learning, Hebbian Learning, Perceptron Learning, Back-propagation Learning

6.1.Expert Systems, Components of Expert System: Knowledge base, inference engine, user interface, working memory, Development of Expert Systems 6.2.Natural Language Processing: Natural Language Understanding and Natural Language Generation, Steps of Natural Language Processing: Lexical Analysis(Segmentation, Morphological Analysis), Syntactic Analysis, Semantic Analysis, Pragmatic Analysis, Machine Translation, 6.3.Machine Vision Concepts: Machine vision and its applications, Components of Machine Vision System 6.4.Robotics: Robot Hardware (Sensors and Effectors) , Robotic Perceptions