The selection of the appropriate cognitive or behavioral strategy from the mind's toolbox is a fundamental process that affects our ability to effectively solve tasks. Despite this, the mechanisms underlying strategy selection are not well understood. The rational meta-reasoning framework proposed by F. Lieder & T. L. Griffith, 2017, suggests that strategy selection is based on a subjective assessment of accuracy and costs, but these variables are often not directly accessible to the decision maker. Therefore, there is a need to better understand how these crucial parameters are cognitively and computationally implemented, in order to improve ecologically rational strategy selection. This research has the potential to enhance our understanding of cognitive processes and improve the ability of robotic systems to perform tasks more effectively.
This study aims to verify the concept of the "mind's toolbox" by simulating an environment where an agent must solve randomly generated mechanical puzzles (Lockboxes). The agent has a variety of strategies at their disposal, and must choose the best one for each Lockbox. The agent can change strategies mid-puzzle if needed.
Your tasks include:
The scope is flexible and not limited to the listed tasks.
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