Automated Reasoning CMPS 521
(Focusing on Intelligent Systems)
Fall 2008

Time and Place

3:30 PM  to 4:45 PM on Tuesday and Thursday in ACTR 118

Given by

Dr. Raja Loganantharaj
The Center for Advanced Computer Studies
Contact: 482-5345 Voice and logan@cacs.louisiana.edu (e-mail)
Prerequisites Strong background in logics and computer science. Knowledge and background in artificial is preferred.

Outline

This course focuses on the theoretical and the practical aspects of acquiring, representing and reasoning with knowledge. In a structured environment with complete knowledge, predicate calculus is used to represent and to reason with knowledge. When the knowledge is incomplete non monotonic reasoning may be applied to infer knowledge.

We discuss about planning methods for a goal oriented rationale agent situated in a static and dynamic environment. We will talk about partial order planner and graph-based planner.

When an agent is situated in an environment where little or no prior knowledge is available, the agent is forced to discover knowledge and association rules between set of concepts. We will discuss different aspects of machine learning including clustering, and classification. For illustrative purposes, we will bring large data sets from life sciences.

The topics covered include

  • Agent Architecture
  • Knowledge and Reasoning
  • Reasoning with complete and incomplete knowledge
  • Planning
  • Partial order planning
  • Graph planning
  • Machine learning algorithms
  • Evaluation of classifiers
  • Unsupervised learning
  • Applications of machine learning
  • Mining genomic sequences
Text book and reading materials Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach, Vipin Kumar 2005.

Reference Books

  1. Artificial Intelligence: A modern Approach, by S. Russell and P. Norvig 2 nd Edition, Prentice Hall 2003 ( http://aima.cs.berkeley.edu)
  2. Artificial Intelligence: A New Synthesis by Nils J. Nilsson, published by Morgan Kaufmann in 1998
  3. Logical Foundations of Artificial Intelligence , by Michael R. Genesereth and Nils J. Nilsson, published by Morgan Kaufmann 1987.
  4. Automated Reasoning (2 nd Edition) by Larry Wos, Ross Overbeek, Ewing Lusk and Jim Boyle, published by McGraw Hill 1992
Evaluation It is based on homework, mid term, projects and final. Midterm 30%, final 35%, homework 15%, class participation 0% and semester project 20%. Lack of class participation -5%.