Summary of Affectively Framework: Towards Human-like Affect-based Agents, by Matthew Barthet et al.
Affectively Framework: Towards Human-like Affect-Based Agents
by Matthew Barthet, Roberto Gallotta, Ahmed Khalifa, Antonios Liapis, Georgios N. Yannakakis
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Affectively Framework is a novel reinforcement learning framework that incorporates human affect models into the observation space and reward mechanism, leveraging the interactive nature of game environments to train virtual agents. This medium-difficulty summary provides an overview of the framework’s key components, including three game environments that integrate affect as part of their observation space. Baseline experiments demonstrate the effectiveness and potential of this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way for training virtual agents using game environments. It creates special spaces where people can play and express emotions, helping AI learn how to understand these feelings. The framework is called Affectively, and it includes three games that let humans interact with AI in new ways. By playing these games, the AI learns to recognize and respond to human emotions. |
Keywords
* Artificial intelligence * Reinforcement learning
