Why less is sometimes more: using Boolean literals to solve 2048

Journal
Information technology  
Volume
66
Issue
4/5
Start Page
174
End Page
186
Citation
IT - Information Technology 66 (4): 174-186 (2025)
Publisher DOI
10.1515/itit-2024-0072
Scopus ID
2-s2.0-85217220139
Publisher
De Gruyter
Explaining and understanding AI-generated policies of control problems is crucial for the acceptance of such policies. Based on an optimisation challenge that took place at GECCO 2024, we describe different solutions for optimising generated policies for the well-known game 2048. At the same time, these generated policies aim to be simpler to understand and, thus, their decisions explainable in contrast to current solutions for 2048, as, e.g., neural network-based models. Our approach uses only Boolean expressions in the policy, and the optimisation shows that such a policy has advantages compared to more complex policy variants. The optimisation generates better results in a shorter time than for non-Boolean expressions. Additionally, the Boolean policies are smaller in size and can be reduced even more when applying existing techniques for term rewriting and simplification. These simplifications, again, may aid in understanding the policy's decision.
Subjects
Boolean expressions | EvoAl | evolutionary algorithm | explainability
DDC Class
600: Technology