People Do Not Just Plan,They Plan to Plan
Authors: Mark Ho, David Abel, Jonathan Cohen, Michael Littman, Thomas Griffiths1300-1307
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our account makes quantitative predictions about how people should plan and meta-plan as a function of the overall structure of a task, which we test in two experiments with human participants. We find that people s reaction times reflect a planned use of information processing, consistent with our account. |
| Researcher Affiliation | Academia | 1Department of Psychology, Princeton University, Princeton, NJ 2Department of Computer Science, Brown University, Providence, RI |
| Pseudocode | No | Finally, at the end of this section and in the supplementary materials, we present a gradient-based algorithm for solving this objective. |
| Open Source Code | No | The paper does not provide any explicit statements or links regarding open-source code availability for the described methodology. |
| Open Datasets | No | The paper describes how the Gridworld mazes were generated for the experiments, but it does not provide any information, links, or citations for public access to these datasets. |
| Dataset Splits | No | The paper describes experiments with human participants and mazes but does not mention any specific training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not specify any particular hardware used for running the models or experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions the 'Adam optimizer' and 'psi Turk framework' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Step costs were included ( .1 points) and the discount rate was set to .99, with λ = 0.01. Planning iterations were chosen such that value iteration would converge (H = 100), while meta-planning parameters were solved using the Adam optimizer (Kingma and Ba 2014) for N = 200 iterations (see supplementary materials for details on the algorithm). |