New AI Research Improves Algorithm Selection for General Game Playing Systems
A new research paper has introduced an innovative way to find the best algorithm for specific tasks in situations with multiple challenges. This strategy views the problem as a series of best arm identification tasks using multi-armed bandits, with each bandit representing a different task and each arm signifying a potential algorithm. An optimistic selection method, based on a defined confidence interval, evaluates these arms based on their likely impact on overall regret. The researchers tested this approach in two significant gaming frameworks: the General Video Game AI (GVGAI) and the Ludii system, aiming to discover top-performing agents for each game while limiting trials. This work, noted as arXiv:2507.00451v2, aims to improve algorithm selection in complex multi-task scenarios in AI.
Key facts
- The research presents a procedure for identifying the best algorithm for sub-tasks in multi-problem domains.
- The approach treats the problem as a set of best arm identification problems for multi-armed bandits.
- Each bandit corresponds to a specific task, and each arm corresponds to a specific algorithm or agent.
- An optimistic selection process ranks arms based on their potential to influence overall simple regret.
- The method was evaluated on the General Video Game AI (GVGAI) framework.
- The method was also evaluated on the Ludii general game playing system.
- The goal was to select a high-performing agent for each game using a limited number of trials.
- Performance was compared to previous best arm identification algorithms for multi-armed bandits.
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