EMOTE: An Explainable Architecture for Modelling the Other through Empathy
Authors: Manisha Senadeera, Thommen Karimpanal George, Stephan Jacobs, Sunil Gupta, Santu Rana
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experiment on minigrid environments showing EMOTE: (a) produces more consistent reward estimates relative to other IRL baselines (b) is robust in scenarios with composite reward and action-value functions (c) produces human-interpretable states, helping to explain how the agent views other agents. We conducted two sets of experiments to assess EMOTE s performance |
| Researcher Affiliation | Academia | Manisha Senadeera1 , Thommen Karimpanal George 1,2 , Stephan Jacobs3 , Sunil Gupta1 and Santu Rana1 1Applied Artificial Intelligence Institute, Deakin University 2School of Information Technology, Deakin University 3University of Queensland |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Supplementary materials available1. 1 https://github.com/manishasena/EMOTE |
| Open Datasets | No | The paper mentions 'minigrid environments' and 'Environments were designed using Marl Grid [Ndousse, 2020] based on Mini Grid [Chevalier-Boisvert et al., 2018].' These references describe the simulation environment/framework used for training agents, not a specific publicly available dataset with concrete access information for data used in the experiments. |
| Dataset Splits | No | The paper does not provide specific percentages or counts for training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'DQN [Mnih et al., 2015]' for policy learning and 'Marl Grid [Ndousse, 2020] based on Mini Grid [Chevalier-Boisvert et al., 2018]' for environment design, but it does not specify version numbers for any of these software components or other libraries used for implementation. |
| Experiment Setup | Yes | The hyperparameter δ [0, 1] balances the importance of reconstructing the empathetic state se to be similar to the original state si (interpretability) and the accuracy of the learning agent s predicted actions in se. This was applied to 3 Agent game (ψ = 5e 4). |