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In domains where measures of utility for automatically-designed artefacts (or agents performing subjective tasks) are difficult or impossible to mathematically describe (such as ‘be interesting’), human interactive search algorithms are an attractive alternative. However, despite notable achievements, they are still designed around a specific search method, resulting in a lack of problem generality: applying a new search algorithm requires an excessive amount of redesign such that an altogether new interactive method is formed in the process. This leads to missed opportunities for human interactive methods to utilize the power of state of the art optimization algorithms. Here, we introduce for the first time a framework for human interactive optimization that is agnostic to both the search method and the application problem. Using 13 different search methods on 24 fitness functions commonly found in evolutionary algorithm benchmarks, we show that our approach works on the majority of tested applications: many of the search methods, provided with access to the fitness functions, performed no better than our framework, which employs surrogate human participants who act as less informed and erroneous representations of the fitness function. In this way, our framework for interactive optimization provides a scalable solution by facilitating the integration of numerous types of current state of the art or future search algorithms. Future work will involve generalizing this method to admit multi-objective optimization methods and validation with human participants.
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Curriculum Generation for Learning Guiding Functions in State-Space Search Algorithms
This paper investigates methods for training parameterized functions for guiding state-space search algorithms. Existing work commonly generates data for training such guiding functions by solving problem instances while leveraging the current version of the guiding function. As a result, as training progresses, the guided search algorithm can solve more difficult instances that are, in turn, used to further train the guiding function. These methods assume that a set of problem instances of varied difficulty is provided. Since previous work was not designed to distinguish the instances that the search algorithm can solve from those that cannot be solved with the current guiding function, the algorithm commonly wastes time attempting and failing to solve many of these instances. In this paper, we improve upon these training methods by generating a curriculum for learning the guiding function that directly addresses this issue. Namely, we propose and evaluate a Teacher-Student Curriculum (TSC) approach where the teacher is an evolutionary strategy that attempts to generate problem instances of ``correct difficulty'' and the student is a guided search algorithm utilizing the current guiding function. The student attempts to solve the problem instances generated by the teacher. We conclude with experiments demonstrating that TSC outperforms the current state-of-the-art Bootstrap Learning method in three representative benchmark domains and three guided search algorithms, with respect to the time required to solve all instances of the test set.
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- Award ID(s):
- 2238979
- PAR ID:
- 10577186
- Publisher / Repository:
- Vol. 17 (2024): Seventeenth International Symposium on Combinatorial Search
- Date Published:
- Journal Name:
- Proceedings of the International Symposium on Combinatorial Search
- Volume:
- 17
- ISSN:
- 2832-9171
- Page Range / eLocation ID:
- 91 to 99
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1609/socs.v17i1.31546
