Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
A Survey of Opponent Modeling in Adversarial Domains
Authors: Samer Nashed, Shlomo Zilberstein
JAIR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This survey presents a comprehensive overview of existing opponent modeling techniques for adversarial domains... We discuss all the components of opponent modeling systems... These discussions lead us to propose a new form of analysis... We then introduce a new framework that facilitates method comparison, analyze a representative selection of techniques using the proposed framework, and highlight common trends among recently proposed methods. Finally, we list several open problems and discuss future research directions... |
| Researcher Affiliation | Academia | Samer B. Nashed EMAIL Shlomo Zilberstein EMAIL University of Massachusetts Amherst Manning College of Information and Computer Sciences Amherst, MA 01002 USA |
| Pseudocode | No | The paper discusses various algorithms and methods developed by other researchers, but it does not present any structured pseudocode or algorithm blocks for its own proposed framework or analysis. |
| Open Source Code | No | The paper is a survey and theoretical framework. It does not describe a specific methodology requiring source code to be released. Therefore, there is no statement about open-sourcing code for the work presented in this paper. |
| Open Datasets | No | The paper is a survey and theoretical framework, which analyzes existing research. It does not conduct its own experiments or use a specific dataset that it would then make publicly available. It mentions datasets in the context of other research, but not for its own work. |
| Dataset Splits | No | The paper is a survey and theoretical framework. It does not conduct its own experiments or use a specific dataset that would require train/test/validation splits to reproduce. Therefore, no dataset split information is provided. |
| Hardware Specification | No | The paper is a survey and theoretical framework. It does not describe new experimental work that would require specific hardware for execution or training. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper is a survey and theoretical framework. It does not implement a new methodology or run experiments that would require specific software dependencies with version numbers. Therefore, no software dependency details are provided. |
| Experiment Setup | No | The paper is a survey and theoretical framework. It does not describe new experiments or propose a specific model that would require details on hyperparameters or training configurations. Therefore, no experimental setup details are provided. |