MCA E2010
Artificial Intelligence
Notes are on their way.
For now, use the syllabus outline to see what this subject covers.
Syllabus topics
Introduction: Overview of Artificial Intelligence - Problems of AI, AI Technique, Tic-Tac-Toe Problem. Intelligent Agents: Agents & Environment, Nature Of Environment, Structure Of Agents, Goal Based Agents, Utility Based Agents, Learning Agents. Problem Solving: Problems, Problem Space & Search: Defining The Problem As State Space Search, Production System, Problem Characteristics, Issues In The Design Of Search Programs. [Q-2]
Notes will be added here.
Syllabus: supplied PDF, page 28.
Syllabus topics
Solving Problems By Searching, Problem Solving Agents, Searching For Solutions; Uniform Search Strategies: Breadth First Search, Depth First Search, Depth Limited Search, Bi -directional Search, Comparing Uniform Search Strategies. Heuristic Search Strategies: Greedy Best-First Search, A* Search, Memory Bounded Heuristic Search: Local Search Algorithms & Optimization Problems : Hill Climbing Search, Simulated Annealing Search, Local Beam Search, Genetic Algorithms; Constraint Satisfaction Problems, Local Search For Constraint Satisfaction Problems. Adversarial Search: Games, Optimal Decisions & Strategies in Games, The Mini Max Search Procedure, Alpha-Beta Pruning, Additional Refinements, Iterative Deepening. [Q-2]
Notes will be added here.
Syllabus: supplied PDF, page 28.
Syllabus topics
Knowledge Representation Issues, Representation & Mapping, Approaches to Knowledge Representation, Issues in Knowledge Representation. Using Predicate Logic: Representing Simple Fact in Logic, Representing Instant & ISA Relationship, Computable Functions & Predicates, Resolution, and Natural Deduction. Representing Knowledge Using Rules: Procedural Verses Declarative Knowledge, Logic Programming, Forward Verses Backward Reasoning, Matching, Control Knowledge. [Q-2]
Notes will be added here.
Syllabus: supplied PDF, page 28.
Syllabus topics
Representing Knowledge in an Uncertain Domain, Bayesian Networks, Dempster -Shafer Theory. Planning: Overview, Components of A Planning System, Goal Stack Planning, Hierarchical Planning. Learning: Forms Of Learning, Inductive Learning, Explanation Based Learning, Neural Net Learning & Genetic Learning. [Q-1] Module 5: Natural Language Processing and Robotics: Brief introduction to Syntactic Processing, Semantic Analysis, Discourse & Pragmatic Processing. Robotics: Introduction, Robot hardware, robotic perception, planning to move, planning uncertain movements, robotic software architecture, application domains. [Q-1]
Notes will be added here.
Syllabus: supplied PDF, page 28.
Check your university’s syllabus for the topics covered in your exams.