Ad Hoc Teamwork With Behavior Switching Agents
Authors: Manish Ravula, Shani Alkoby, Peter Stone
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | When evaluating our algorithm on the modified predator prey domain, we find that it outperforms existing Bayesian CPD algorithms. We provide a detailed experimental evaluation of the proposed method in a modified predator-prey domain [Benda et al., 1986]. |
| Researcher Affiliation | Academia | Manish Ravula , Shani Alkoby and Peter Stone The University of Texas at Austin, Texas, USA manishreddy@utexas.edu, shani@cs.utexas.edu, pstone@cs.utexas.edu |
| Pseudocode | Yes | Algorithm 1 MAP Type Estimation, Algorithm 2 Convolutional Changepoint Detection (Conv CPD) for each agent, Algorithm 3 Template for Agent Types |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing the code for the described methodology or links to a code repository. |
| Open Datasets | No | The paper describes a 'modified predator-prey domain' and refers to [Benda et al., 1986] for the original domain, but does not provide concrete access information (link, DOI, specific repository, or explicit statement of public availability) for a dataset used in training. |
| Dataset Splits | No | The paper states 'The Conv CPD algorithm is trained with 10,000 samples', but it does not provide specific training/validation/test split percentages, sample counts, or references to predefined splits for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions using Convolutional Neural Networks and SGD optimizer, but it does not specify version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | The Conv CPD algorithm is trained with 10,000 samples (batch size = 64, learning rate = 0.01, decay = 0.1, optimizer = SGD) involving equal proportions of all classes. |