The Minds of Many: Opponent Modeling in a Stochastic Game
Authors: Friedrich Burkhard von der Osten, Michael Kirley, Tim Miller
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulation results demonstrate that the model performs well under uncertainty and that stereotyping allows larger groups of agents to be modelled robustly. |
| Researcher Affiliation | Academia | School of Computing and Information Systems The University of Melbourne, Australia fvon@student.unimelb.edu.au, {m.kirley,tmiller}@unimelb.edu.au |
| Pseudocode | No | The paper describes the model and its components using mathematical equations and textual descriptions, but it does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that the code is publicly available. |
| Open Datasets | No | The paper conducts simulations using a defined game model (CPRG) rather than relying on a pre-existing, externally available dataset. While the game dynamics are described and referenced, no concrete access information to a downloadable dataset used for the experiments is provided. |
| Dataset Splits | No | The paper conducts simulations and describes the number of rounds and repetitions for each setting, but it does not specify explicit training/validation/test dataset splits as it generates its own data through simulation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | Table 1 shows the parameters and variables used in the experiments: λ Learning parameter 0.5, t Timescale for assessing agents 10, Xmin Minimum effort 100, Xmax Maximum effort 500, A # of actions available to agents 10, S # of game states agents recognize 5, n # of agents in the game [2,10], C # of categories for segmentation [1,4], h % of RND agents in the population [20,50]. Each simulation is run for 5000 rounds, with each setting repeated 50 times. |