Progress Report – July 2009

In this progress report, I attended the ICAI 09 conference in Las Vegas (the IJCAI 2009 is located in Pasadena).  I presented my work on A Brute Force Approach to Solving the Knights Tour Using Prolog.  One thing that I learned is the participants are proud to share their work with others.  Another lesson is to make your presentation within the time allotted.  A few presenters did not finish their presentations in the 20 minutes time slot.  I rehearsed my presentation a few times to have the timing within 20 minutes.  I also learned that I need to get more training with MS Power Point.

I am also continue to view the video lectures from the Summer Schools in Logic and Learning from Video Lectures.  Thus, I have completed the Intelligent Agents video lectures by John Lloyd. 

Advance Prolog Programming

Reading the comp.ai.prolog news group, I noticed the prolog FAQ did not make any references to any advance prolog programming websites.  Though it mentions the Craft of Prolog as the book for the advance reader, it was published before the ISO Prolog standard was finalized.  I have in my wish list in Amazon the book Prolog Programming in Depth by Michael Covington et. al (Fascimile edition), which seems from the table contents a book that could compliment the Bratko’s Prolog: Programming for Artificial Intelligence and The Art of Prolog.  Here is an example:

cycle(X) :-
   (    X < 1 -> true
        ;
        write(X),
        nl,
        X1 is X - 1,
        cycle(X1)
   ).

The above example is used to count down from X to 1.  In all the online tutorials, I have not seen example using the -> control construct.  I will on occasion will publish some prolog code.

AIMA Part VI

Part VI of the AIMA book covers learning and various techniques on agents can learn.  The material covers Learning from Observations, Neural Networks, Reinforcement Learning, and Knowledge in Learning.  In the second edition, the chapter in Neural Networks is replaced with Statistical Learning Methods.

I read Chapter 18 of the AIMA book called Learning from Observations.  The Chapter focuses on decision trees and decision lists as some computational learning theory.  The main classification algorithm is the ID3 algorithm for classifying examples and generating decision trees.  The ID3 algorithm is based on Hunt’s Concept Learning System (CLS).  The ID3 algorithm uses information theory for obtaining the decision tree covering most the examples.  I have studied the ID3 algorithm quite extensively since the original application was to classify the winning position in the king and rook versus the king and knight endgame (Quinlan 1983).

Progress Report – June 2008

In this report, I focused on my presentation for the upcoming ICAI ‘09 conference in Las Vegas.  It has currently 18 slides.

I am also viewing the video lectures from the Summer Schools in Logic and Learning from Video Lectures dot Net.  Thus, I have completed the Introduction to Logic video lectures.

I was also reading Michalski’s article on A Theory and Methodology of Induction Learning. Basically it was an article describing the induction process and some the issues associated with induction such as descriptive language, background information, examples, generalization, and hypothesis space.  All this sounded familar when I was reading the two books on ILP.  Induction is a common theme with the two books.  Various familar algorithms are discussed amongst this material.  It appears there are no easy answers in the induction of rules from examples.