Modeling Human Ad Hoc Coordination
Authors: Peter Krafft, Chris Baker, Alex Pentland, Joshua Tenenbaum
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our mechanism for multiagent coordination as a model for human decisions in a simple coordination game using existing experimental data. We then explore via simulations whether modeling humans in this way may improve human-agent collaboration. ... We now present our empirical results. |
| Researcher Affiliation | Academia | Peter M. Krafft , Chris L. Baker , Alex Sandy Pentland , Joshua B. Tenenbaum Massachusetts Institute of Technology, Cambridge, MA USA Computer Science and Artificial Intelligence Laboratory, MIT Media Lab, Department of Brain and Cognitive Sciences {pkrafft,clbaker,pentland,jbt}@mit.edu |
| Pseudocode | Yes | Algorithms 1-4 present the functions needed to compute perceived maximal common p-belief. ... Algorithm 1 common p belief(C, i, ω) |
| Open Source Code | Yes | All of our code is available online at https://github.com/pkrafft/modeling-human-ad-hoc-coordination. |
| Open Datasets | Yes | The dataset comes from the Thomas experiments (Thomas et al. 2014). |
| Dataset Splits | No | The paper uses existing experimental data from the Thomas experiments but does not specify explicit train, validation, or test splits, nor does it provide percentages or sample counts for such splits. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., CPU, GPU models, or cloud computing specifications) used for running its experiments or simulations. |
| Software Dependencies | No | The paper states that its code is available online but does not list specific software dependencies or their version numbers (e.g., programming language versions, library versions, or solver versions). |
| Experiment Setup | Yes | These models share a free parameter δ. We take δ = 0.25. ... For the two iterated reasoning models we use a grid search over [0, 1, 2, 3, 4, 5] to find the best fitting k for each model (ultimately k = 1 in iterated maximization and k = 3 in iterated matching). |