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.  In addition, the prolog language has been very stable since the ISO Prolog standard was established.  Some the prolog has minor differences with certain predefined predicates, but it has been compatible with Edinbrugh Prolog.  The current Prolog books cover some basic AI applications such as games, mini expert systems, and meta interpreters.  What is needed is agent/environment based applications using modern tools such as Q-Learning or TD-methods.

I finally completed Scott Sanner’s video lecture in Reinforcement Learning.  An excellent tutorial.  The lecture was discussing topics such as model based and model free methods.  The subjects MDPs, dynamic programming, Monte Carlo, temporal difference, and function approximations are discussed.

I also performed research on the Warnsdorf algorithm and posted my initial results.  Most of this work was done using procedural languages and with square boards of size five and greater.

In addition, I found the ICML-06 Workshop on Structural Knowledge Transfer for Machine Learning.