Artificial Intelligence (Course No. CS323)

Computer Science and Engineering

  1. Un-informed search strategies: Breadth first search, Depth-first search, Depth-limited search, Iterative deepening depth-first search, bidirectional search
  2. Informed search and exploration: Greedy best-first search, A* search, Memory-bounded heuristic search
  3. Local search algorithms and Optimization: Hill climbing, Simulated Annealing, Local beam search, Genetic Algorithms
  4. Constraint Satisfaction Problems: Backtracking search for CSPs, Local search for CSPs
  5. Adversarial Search: Optimal Decision in Games, The minimax algorithm, Alpha-Beta pruning
  6. Knowledge and Reasoning: Propositional Logic, Reasoning Patterns in propositional logic; First order logic: syntax, semantics, Inference in First order logic, unification and lifting, backward chaining, resolution
  7. Knowledge Representation: Ontological engineering, categories, objects, actions, situations, Situation Calculus, semantic networks, description logics, reasoning with default systems
  8. Planning: Planning with state space search, Partial-Order Planning, Planning Graphs, Planning with Propositional Logic, hierarchical task network planning, non-deterministic domains, conditional planning, continuous planning, multi-agent planning
  9. Miscellaneous Topics: Fuzzy logic systems, Natural Language Processing

1 Course Objectives

To provide the foundations for AI problem solving techniques and knowledge representation formalisms

2 Learning Outcomes

  1. Ability to identify and formulate appropriate AI methods for solving a problem
  2. Ability to implement AI algorithms
  3. Ability to compare different AI algorithms in terms of design issues, computational complexity, and assumptions

3 Reference Books

  1. Stuart Russel and Peter Norvig, Artificial Intelligence: A Modern Approach, 2009
  2. Elaine Rich, Kevin Knight and Shivashankar B. Nair, Artificial Intelligence, Tata McGraw-Hill, 2008