TL;DR
This paper explores using evolutionary algorithms to automatically generate decision-making rules for the card game Rack'O, comparing evolved rules with human-crafted scripts to analyze their effectiveness.
Contribution
It introduces an evolutionary approach with a domain-specific language for synthesizing game rules, providing a detailed analysis of the resulting strategies.
Findings
Evolved rules show competitive decision-making performance.
Comparison reveals strengths and weaknesses of automated versus human strategies.
Evolutionary process effectively generates diverse rule sets.
Abstract
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.
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