Building Policies for Scrabble

Research to learn policies using Evolutionary Algorithms along with training examples has been done for the domains of the Blocks World and the KRKa2 chess ending in our previous work [1,2]. Although the results have been positive, we believe that a more challenging domain is necessary to test the performance of this technique. The game of Scrabble, played in Spanish, in its competitive form (one vs. one) intends to be used and studied to test how good evolutionary techniques perform in building policies that produce a plan. To conduct proper research for Scrabble a Spanish lexicon was built and a heuristic function mainly based on probabilistic leaves was developed recently [3]. Despite the good results obtained with this heuristic function, the experimental games played showed that there is much room for improvement. In this paper a sketch of how can policies be built for the domain of Scrabble is presented; these policies are constructed using attributes (concepts and actions) given by a Scrabble expert player and using the heuristic function presented in [3] as one of the actions. Then to evaluate the policies a set of training examples given by a Scrabble expert is used along with the evolutionary learning algorithm presented in [2]. The final result of the process is an ordered set of rules (a policy) which denotes a plan that can be followed by a Scrabble engine to play Scrabble. This plan would also give useful information to construct plans that can be followed by humans when playing Scrabble. Most of this work is still under construction and just a sketch is presented. We believe that the domain of games is well-suited for testing these ideas in planning.