An Investigation of Minimax Search for Evolving Ayo/Awari Player

An Analysis of Minimax Search and Endgame Databases in Evolving Awale Game Player

“…The problem with minimax search is that if the evaluator is applied at the leaves and backward induction is performed to compute the value of a game tree, there is no guarantee that a correct move.Also the issue of how to design a suitable evaluator and how to select a correct move without the rationality assumption [4] Studies have shown that improving the evaluation function does not still improve the results such as the six features considered for the design of an evaluation function [9] , also another agent was created using minimax search which was evolved using a genetic algorithm with the objective of showing that a better representation can lead to a deeper search [10].Six additional features were added to those used in [9] to improve performance of an Ayo agent. The result obtained at the strongest level (grandmaster level) of play is shown below in Table 1.…”

“…Another program called Softwari[14]constructs large endgame databases. There is only one commonality, they all focus on searching and database utilization and gave little attention to evaluation functions [4]. Generally, endgame databases can only be computed when few pieces remain on board and they can require solution lengths that defy the capabilities of minimax based searches for optimal play.…”

“…Minimax has been combined with other techniques to evolve players such as with Refinement assisted Minimax (RAM) [4], Case based reasoning (CBR) [15],Aggregate Malanobis Distance Function(AMDF) [16]. These combinations have shown that move selection can be assisted by employing them to guide the selection process.…”

“…The computer also defeated the world best chess player Kasparov, the human world champion in a tournament competition in 1997 [3].The success of the computer in playing chess resulted in increased interest in games by researchers in both mathematics and artificial intelligence. Also games are now the most interesting artificial intelligence application to the public [4] and one of those games is Awale also known as Ayo or Awele.…”

An Overview of Supervised Machine Learning Techniques in Evolving Mancala Game Player

“…The basic idea behind the Refinement Assisted Minimax (RAM) algorithm [28] was to find a new strategy similar to the fictitious strategies where their efficiency depended on how efficient was the given strategy. Fictitious play was originally introduced in [20] and it is the most studied process for games [21] and a very good example is the End Game Tchoukailon (EGT) positions [22,23].…”