An Approach to Cooperation in General-Sum Normal Form Games
Authors: Steven Damer
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 1 shows the expected outcome of the calculated strategy in randomly generated games. Figure 2 shows the performance of two agents which estimate attitude values using a particle filter and reciprocate the attitude of the opponent with a slightly larger attitude value. Figure 3 shows the effect of r and w when using RSRS in a general-sum game. |
| Researcher Affiliation | Academia | Steven Damer Department of Computer Science and Engineering University of Minnesota, Minneapolis, MN 55455, USA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper mentions 'randomly generated games' but does not provide concrete access information for a publicly available or open dataset, nor does it cite an established benchmark dataset with authors and year. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper describes how parameters are used and adjusted ('set r values and a combination of gradient descent with an exponential opponent response model to set w values') but does not provide concrete hyperparameter values or detailed training configurations (e.g., learning rates, batch sizes, number of epochs). |