Queen's School of Computing

CISC 352/3.0 Artificial Intelligence

Original author: Jim Rodger
Last Revised: October 16, 2006

Calendar Description

An introduction to the basic principles and tools of artificial intelligence. Problem solving methods and knowledge representation techniques.

Prerequisites: CISC 235/3.0, 260/3.0.


Artificial Intelligence (AI) has existed as a recognizably distinct field of study for about five decades. Historically, AI emphasized reasoning and thought processes and fidelity to human models. More recent work has shifted the focus to include the actions that artificial systems perform, and the application of an ideal standard of rationality to that behaviour.

This first course in Artificial Intelligence provides students an introduction to the field, presenting some of the major ideas that have emerged from the roughly past half century of AI research.

The courses to which this course is a prerequisite are:

  • CISC-452/3.0 (Neural and Genetic Computing)
  • CISC-453/3.0 (Topics in Artificial Intelligence)
  • CISC-471/3.0 (Computational Biology)
  • COGS-300/3.0 (Programming Cognitive Models)
  • COGS-400/3.0 (Neural and Genetic Computing)

This course is required in BMCO and COGS.

history; foundations and contributing disciplines; the Intelligent-Agent theme; agent design hierarchy; the state of the art

Problem Solving
searching; uninformed search strategies; avoiding repeated states; informed search; heuristic functions; local search and optimization; constraint satisfaction problems; variable and value ordering; adversarial search and games; minimax; alpha-beta pruning; state of the art

Knowledge and Reasoning
logical agents and propositional logic; reasoning and effective inference; first order logic; assertions and queries in FOL; knowledge engineering; knowledge representation; categories and objects; actions, situations and events; mental events, objects, beliefs; reasoning with categories; reasoning with default information; truth maintenance

Perception (Computer Vision)
image formation; early processing and edge detection; extracting three-dimensional information (shape from x); object recognition and pose estimation; vision for navigation

planning problems; state-space search; partial order planning; planning graphs; planning and propositional logic (as time allows)
Possible Texts
  • Russell, S. and Norvig, P. Artificial Intelligence A Modern Approach (2nd Edition), Prentice Hall, 2003.

  • Winston, P. H. Artificial Intelligence (3rd Edition), Addison-Wesley, 1992.

  • Nilsson, N. Artificial Intelligence A New Synthesis, Morgan Kaufmann, 1998.