Artificial Intelligence
Subject Code: IT/T/326
Credit Hours/Class:
Theory Class: 3 Periods/Week
Sessional Class: 6 Lectures/Week
- Theory Class - Sessional Class
Marks Distribution:
Theory Class: Two Class Test of 30 marks each for one hour duration.
Final exam of 100 marks for 3 hours written exam
Sessional Class: Continuous Evaluation of laboratory performance throughout the semester
Marks for attendance in the laboratory
Final Test on laboratory assignment
Course Outcomes:
- Able to understand the basic concept of artificial intelligence architecture and agents.
- Have clear knowledge of different search techniques used in AI applications.
- Able to represent knowledge using different systems and languages.
- Able to understand the basic concept of evolutionary computing and its operations.
- Have basic knowledge of ANN.
Syllabus Outline:
Part I: Introduction and 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.
Part II:Problem Solving, Problems, Problem Space & search
Defining the problem as state space search,
Production system, problem characteristics,
Issues in the design of search programs.
Part III:Search techniques
Problem solving agents, searching for solutions;
Uniform search strategies: breadth first search, depth first search, depth limited search,
Bidirectional search, comparing uniform search strategies.
Heuristic search strategies Greedy best-first search, A* search, AO* search,
Memory bounded heuristic search: local search algorithms & optimization problems:
Hill climbing search, simulated annealing search, local beam search.
Part IV:Constraint satisfaction problems
Local search for constraint satisfaction problems. Adversarial search, Games,
Optimal decisions & strategies in games, the minimax search procedure,
Alpha-beta pruning, additional refinements, iterative deepening.
Part V: Knowledge & reasoning
Knowledge representation issues, representation & mapping, approaches to knowledge representation.
Using predicate logic, Representing simple fact in logic, representing instant & ISA relationship,
Computable functions & predicates, resolution, natural deduction. Representing knowledge using rules,
Procedural verses declarative knowledge, logic programming,
Forward verses backward reasoning, matching, control knowledge.
Part VI: Constraint satisfaction problems
Representing knowledge in an uncertain domain, the semantics of Bayesian networks,
Dempster-Shafer theory, Planning Overview, components of a planning system,
Goal stack planning, Hierarchical planning, other planning techniques.
Part VII: Expert Systems
Representing and using domain knowledge, expert system shells, knowledge acquisition
Reference Books:
- An introduction to logic programming through Prolog - Michael Spivey
- Artificial Intelligence - A Modern Approach - Stuart J. Russell and Peter Norvig
- Artificial Intelligent - A new synthesis - Nils J. Nilsson
- LOGIC, PROGRAMMING AND PROLOG - Ulf Nilsson and Jan Maluszy´nski