COM 1410 Artificial Intelligence

Course Format
Course Coordinator
Textbooks and References

Prerequisites by Topic
Major Topics Covered in Course
Laboratory Projects

Course Description

Focuses on approaches to making computers act intelligently by studying current methods for automated understanding, problem solving, optimal search, knowledge representation, vision, and learning. Students perform experiments with semantic nets, logical deduction systems, evidential reasoning systems, and/or neural net.

4 QH credit
Prerequisite: COM 1201, COM 1340.

Course Information

Course is offered only during the Winter quarter. CS majors are guaranteed a place in class.
 

Winter 2001

Course Format

This is an elective course for  BS CS majors, a 'focused elective' course for BA CS majors, and an elective for BS IS majors.

Course Coordinators

Professor Ronald Williams and Carole Hafner
rjw@ccs.neu.edu and hafner@ccs.neu.edu

Textbooks and References

Fall 2000

• Artificial Intelligence: A Modern Approach," by Stuart Russell and Peter Norvig.

 

References:
 

• Common Lisp, The Language, by Guy Steele.

 

Course Goals

To expose the students to some of the types of problems AI attempts to address and some of the solutions that have been developed for them, and to help them appreciate why many challenges remain in this area.

Prerequisites by Topic

Algorithms and Data Structures II
Lisp programming (now part of the Recursive Thinking course).

Major Topics Covered in the Course

Problem solving through search, including informed search and game playing

Reasoning agents, inference in first-order logic, and the use of first-order logic to describe to describe situations in the world

Planning

Laboratory projects

Completing and running a Lisp program to solve water-jug puzzles, given instructor-supplied code for performing iterative-deepening depth-first search.  (2 weeks)

Completing and running a Lisp program to find shortest 8-puzzle solutions, given instructor-supplied code for performing A* search.  This included testing three different heuristic functions and reporting number of nodes expanded using each.  (2 weeks)

Encoding some simple knowledge in the form of "facts" and "rules" and running instructor-supplied forward and backward chaining programs on them. (2 weeks)