Details
| Original language | English |
|---|---|
| Title of host publication | 2023 IEEE Congress on Evolutionary Computation, CEC 2023 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (electronic) | 9798350314588 |
| ISBN (print) | 979-8-3503-1459-5 |
| Publication status | Published - 2023 |
| Event | 2023 IEEE Congress on Evolutionary Computation, CEC 2023 - Chicago, United States Duration: 1 Jul 2023 → 5 Jul 2023 |
Abstract
Baba is You is a challenging puzzle game in which the player can modify the rules of the game. This yields a large variety of puzzles and an enormous state space to be searched through. Recently, the Feature Space Search algorithm has shown great results in Sokoban, which apart from the rule modification shares many similarities to Baba is You. It uses multiple heuristics to guide the search into promising regions of the search space. In this work, we are proposing a similar concept for solving Baba is You based on multiple heuristics that are aggregated for guiding a tree-based search process. However, finding parameters for weighting/prioritizing the different heuristics is a non-trivial task. This process is done, by applying evolutionary algorithms for single- and multi-objective optimization. Specifically, we compare the effects of these different optimization schemes on the agents' general level-solving capabilities. In all cases, the agent was able to adapt well to the training and test set with no significant differences among the optimization schemes. Compared with state-of-the-art Baba is You agents our search-based approach shows an improved performance in terms of the number of levels being solved, as well as a reduction in the average time required to solve a level.
Keywords
- Baba Is You, Evolutionary Algorithms, Parameter Optimization
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Computational Mathematics
- Mathematics(all)
- Control and Optimization
- Mathematics(all)
- Modelling and Simulation
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2023 IEEE Congress on Evolutionary Computation, CEC 2023. Institute of Electrical and Electronics Engineers Inc., 2023.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Evolutionary Optimization of Baba Is You Agents
AU - Olson, Christopher
AU - Wagner, Lars
AU - Dockhorn, Alexander
PY - 2023
Y1 - 2023
N2 - Baba is You is a challenging puzzle game in which the player can modify the rules of the game. This yields a large variety of puzzles and an enormous state space to be searched through. Recently, the Feature Space Search algorithm has shown great results in Sokoban, which apart from the rule modification shares many similarities to Baba is You. It uses multiple heuristics to guide the search into promising regions of the search space. In this work, we are proposing a similar concept for solving Baba is You based on multiple heuristics that are aggregated for guiding a tree-based search process. However, finding parameters for weighting/prioritizing the different heuristics is a non-trivial task. This process is done, by applying evolutionary algorithms for single- and multi-objective optimization. Specifically, we compare the effects of these different optimization schemes on the agents' general level-solving capabilities. In all cases, the agent was able to adapt well to the training and test set with no significant differences among the optimization schemes. Compared with state-of-the-art Baba is You agents our search-based approach shows an improved performance in terms of the number of levels being solved, as well as a reduction in the average time required to solve a level.
AB - Baba is You is a challenging puzzle game in which the player can modify the rules of the game. This yields a large variety of puzzles and an enormous state space to be searched through. Recently, the Feature Space Search algorithm has shown great results in Sokoban, which apart from the rule modification shares many similarities to Baba is You. It uses multiple heuristics to guide the search into promising regions of the search space. In this work, we are proposing a similar concept for solving Baba is You based on multiple heuristics that are aggregated for guiding a tree-based search process. However, finding parameters for weighting/prioritizing the different heuristics is a non-trivial task. This process is done, by applying evolutionary algorithms for single- and multi-objective optimization. Specifically, we compare the effects of these different optimization schemes on the agents' general level-solving capabilities. In all cases, the agent was able to adapt well to the training and test set with no significant differences among the optimization schemes. Compared with state-of-the-art Baba is You agents our search-based approach shows an improved performance in terms of the number of levels being solved, as well as a reduction in the average time required to solve a level.
KW - Baba Is You
KW - Evolutionary Algorithms
KW - Parameter Optimization
UR - http://www.scopus.com/inward/record.url?scp=85174482427&partnerID=8YFLogxK
U2 - 10.1109/cec53210.2023.10253977
DO - 10.1109/cec53210.2023.10253977
M3 - Conference contribution
AN - SCOPUS:85174482427
SN - 979-8-3503-1459-5
BT - 2023 IEEE Congress on Evolutionary Computation, CEC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Congress on Evolutionary Computation, CEC 2023
Y2 - 1 July 2023 through 5 July 2023
ER -