BIT Artificial Intelligence

Unit I: Introduction (3 Hrs.)

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

Unit II: Agents (5 Hrs.)

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.

Unit III: Problem Solving by Searching (10 Hrs.)

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*.

Unit IV: Knowledge Representation (14 Hrs.)

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.

Unit V: Neural Network (2 Hrs.)

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.

Unit VI: Machine Learning (5 Hrs.)

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

Unit VII: Expert System (3 Hrs.)

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

Unit VIII: Natural Language Processing (3 Hrs.)

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.

The laboratory work consists of implementation of intelligent agents and expert systems, searching techniques, knowledge representation systems and machine learning techniques. Students are advised to use LISP, PROLOG, or any other high level language.