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:

  1. Able to understand the basic concept of artificial intelligence architecture and agents.
  2. Have clear knowledge of different search techniques used in AI applications.
  3. Able to represent knowledge using different systems and languages.
  4. Able to understand the basic concept of evolutionary computing and its operations.
  5. 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:

  1. An introduction to logic programming through Prolog - Michael Spivey
  2. Artificial Intelligence - A Modern Approach - Stuart J. Russell and Peter Norvig
  3. Artificial Intelligent - A new synthesis - Nils J. Nilsson
  4. LOGIC, PROGRAMMING AND PROLOG - Ulf Nilsson and Jan Maluszy´nski