Approximating Value Equivalence in Interactive Dynamic Influence Diagrams Using Behavioral Coverage
Authors: Ross Conroy, Yifeng Zeng, Jing Tang
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We theoretically analyze properties of the approximate techniques and show empirical results in multiple problem domains. We empirically examine VE identiļ¬cation framework, discussing improvements driving a new line of I-DID research. |
| Researcher Affiliation | Academia | Ross Conroy, Yifeng Zeng , Jing Tang Teesside University, Middlesbrough, UK {Ross.Conroy, Y.Zeng, x9019186}@tees.ac.uk |
| Pseudocode | Yes | Algorithm 1 VE Identiļ¬cation Framework, Algorithm 2 Model Selection, Algorithm 3 Value Computation |
| Open Source Code | No | The paper does not include any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper mentions 'UAV benchmark' and 'Star Craft 1' problem domains, but does not provide specific links, DOIs, repository names, or explicit statements on how to access the exact datasets used for experiments. |
| Dataset Splits | No | The paper does not provide specific percentages or counts for training, validation, or test dataset splits. It only mentions the number of simulations for evaluation. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU models, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide any specific software names with version numbers that were used for the experiments. |
| Experiment Setup | Yes | In Fig. 5, we show agent i s average rewards over 500 simulations for T=6 and 10 respectively. For a fair comparison, we let top-K and ABE maintain the same number of j s models at each time step in the I-DIDs. In Fig. 6, we show the performance of VE approaches for solving I-DIDs in UAV. The rewards of agent i are averaged over 100 simulations. ... As a small number of j s candidate models are tested, we implemented an optimal search algorithm (OS) to select top K models. Fig. 7a shows that VE+GS performs closely with VE+OS for solving I-DIDs. |