Analysis of Evolutionary Program Synthesis for Card Games
This work addresses the problem of automated strategy generation for card games, which is relevant for game AI researchers.
This paper explores the use of an evolutionary approach to synthesize rules for the card game Rack'O. The synthesized rules are compared against a human-written script, with the results analyzed for their implications.
In this report, we inspect the application of an evolutionary approach to the game of Rack'O, which is a card game revolving around the notion of decision making. We first apply the evolutionary technique for obtaining a set of rules over many generations and then compare them with a script written by a human player. A high-level domain-specific language is used that deter-mines which the sets of rules are synthesized. We report the results by providing a comprehensive analysis of the set of rules and their implications.