Introduction:
- Intelligence, Artificial Intelligence (AI), AI Perspectives: acting and thinking humanly, acting and thinking rationally
- History of AI
- Foundations of AI: Philosophy, Economics, Psycology, Sociology, Linguistics
- Neuroscience, Mathmatics, Computer Science, Control Theory
- Applications of AI
Intelligent Agents:
- Introduction of agents, Structure of Intelligent agent, Properties of Intelligent Agents
- Configuration of Agents, PEAS description of Agents, PAGE
- Types of Agents: Simple Reflexive, Model Based, Goal Based, Utility Based, Learning Agent
- Environment Types: Deterministic, Stochastic, Static, Dynamic, Observable, Semi-observable, Single Agent, Multi Agent
Problem Solving by Searching:
- Definition, State space representation, Problem as a state space search, Problem formulation, Welldefined problems
- Solving Problems by Searching, Search Strategies: Informed, Uninformed, Performance evaluation of search strategies: Time Complexity, Space Complexity, Completeness, Optimality
- Uninformed Search: Depth First Search, Breadth First Search, Depth Limited Search, Iterative Deepening Search, Uniform Cost Search,
- Bidirectional Search
- Informed Search, Heuristic Function, Admissible Heuristic, Informed Search Techniques: Greedy Best First Search, A* Search, Optimality and Admissibility in A*, Hill Climbing Search, Simulated Annealing Search
- Game Playing, Adversarial Search Techniques: Mini-max Search, Alpha-Beta Pruning
- Constraint Satisfaction Problems, Examples of Constraint Satisfaction Problems
Knowledge Representation:
- Definition and importance of Knowledge, Issues in Knowledge Representation, Knowledge Representation Systems,
- Properties ofKnowledge Representation Systems
- Types of Knowledge Representation Systems: Semantic Nets, Frames, Conceptual
- Dependencies, Scripts, Rule Based Systems (Production System), Propositional Logic, Predicate Logic
- Propositional Logic(PL): Syntax, Semantics, Formal logic-connectives, truth tables, tautology, validity, well-formed- formula, Inference using Resolution, Backward Chaining and Forward Chaining
- Predicate Logic: FOPL, Syntax, Semantics, Quantification, Inference with FOPL: By converting into PL (existential and universal instantiation), Unification and lifting, Inference using resolution
Machine Learning:
- Introduction to Machine Learning, Concepts of Learning, Supervised, Unsupervised and
- Reinforcement Learning
- Statistical-based Learning: Naive Bayes Model
- Learning by Genetic Algorithms: Operators in
- Genetic Algorithm: Selection, Mutation,
- Crossover, Fitness Function, Genetic Algorithm
- 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
Applications of AI:
- Expert Systems, Components of Expert System: Knowledge base, inference engine, user interface, working memory, Development of Expert Systems
- Natural Language Processing: Natural Language
- Understanding and Natural Language Generation, Steps of Natural Language Processing: Lexical Analysis(Segmentation, Morphological Analysis), Syntatic Analysis, Semantic Analysis, Pragmatic Analysis, Machine Translation,
- Machine Vision Concepts: Machine vision and its applications, Components of Machine Vision System
