BIT Artificial Intelligence

Artificial Intelligence (AI), History of AI, AI Perspectives, Turing Test, Foundations of AI, Scope of Symbolic AI, Applications of AI.

Introduction of Agents; Configuration of Agents: PEAS description of Agents; 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; State Space Representation; Problem Formulation; Constraint Satisfaction Problems. Solving Problems by Searching; Performance evaluation of search techniques; Uninformed Search: Depth First Search, Breadth First Search, Depth Limited Search, Iterative Deepening Search, Bidirectional Search. Informed Search: Greedy Best First Search, A* search, Hill Climbing. Game playing: Adversarial search techniques, Mini-max Search, Alpha-Beta Pruning. Problem Decomposition: Goal Trees, AO*.

Knowledge; Knowledge Representation; Issues in Knowledge Representation, Knowledge Representation Systems; Properties of Knowledge Representation Systems. Logic Based: Propositional and Predicate; Propositional Logic: Syntax, Semantics; CNF Form; Inference using Resolution; Backward Chaining and Forward Chaining; Predicate Logic: FOPL: Syntax, Semantics; Quantification; Inference with FOPL: Unification and Lifting; Inference using Resolution. Semantic Nets, Frames, Rule Based Systems, Scripts, Conceptual Dependency. Statistical Reasoning: Uncertain Knowledge, Random Variables, Prior and Posterior Probability, Bayes' Rule, Bayesian Networks, Reasoning in Belief Networks, Dempster-Shafer Theory.

Neural Networks: Introduction; Mathematical Model of ANN, Designing a neuron, Types of ANN: Feed-forward, Recurrent, Single Layered, Multi-Layered, Learning Rule, Learning Rate, Application of Artificial Neural Networks.

Machine Learning; Concepts of Learning: Supervised, Unsupervised and Reinforcement Learning; Learning by Analogy; Learning by Genetic Algorithm; Learning by Back-propagation.

Expert Systems; Architecture of Expert System; Development of Expert Systems; Applications of Expert Systems.

Natural Language Processing: Natural Language Understanding and Natural Language Generation. Steps in NLP: Lexical Analysis, Syntactic Analysis, Semantic Analysis, Discourse and Pragmatic Analysis; Ambiguities in NLP.