Progress Report – August 2009

During the month of August, I focused on Relational Reinforcement Learning, a field that combines Relational Learning and Reinforcement Learning fields.  Please read my blog entry for Relational Reinforcement Learning.  Afterwards, I have read a number of different articles regarding RRL learning from different authors, including the RRL workshop at the ICML 2004 conference.  My initial preference is the work performed and researched by Dr. Eduardo F. Morales.

I posted my review and comments regarding part I of the book The Art of Prolog (Sterling and Shapiro 1994).  There are very few books regarding Logic Programming in recent years.  Since the mid 1990s, much of the effort in Logic Programming has been with Inductive Logic Programming and Relational Learning.  The emerging field of Statistical Relational Learning has become a new field of research. 

Relational Reinforcement Learning

I start this month reading the articles in Relational Reinforcement Learning.  It began as a curiosity due to a response in the Reinforcement Learning mailing list.  The authors point a speech by Rich Sutton and Leslie Pack Kaelbling at the IJCAI 1997 conference in Japan, in which they recommended the combination of Induction Logic Programming (aka Relational Learning) and Reinforcement Learning.  The authors of the paper demonstrate this concept by using the blocks world domain to illustrate the combination of both fields.  Perhaps, this is exactly what I needed since I have done much reading, studying, and video lectures with logic programming, ILP, and reinforcement learning.  Of course, this paper spawn much researching according to CiteSeerX and Google Scholarly.

I found the web site to the 2004 Workshop in Relational Reinforcement Learning and downloaded the proceedings.

I shall continue this path of research interest.

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.

Progress Report – May 2009

This month I submitted my paper A Brute Force Approach to Solving the Knight’s Tour using Prolog to the ICAI ‘09 conference, located at the Monte Carlo in Las Vegas, Nevada.  According to the conference chair, the conference acceptance rate was 27 percent.  This conference is in conflict the IJCAI conference in Pasadena, California.

Nevertheless, besides my research paper, much of my activity this month has been around logic programming and inductive logic programming (ILP).  I ordered from Amazon several books recently around this area, in particular The Art of Prolog and Inductive Logic Programming: From Machine Learning to Software Engineering.  The Art of Prolog has a gentle introduction to logic programming – it is very clear and instructive.  From this book I was able to understand early articles in Logic Programming.