Summary of Data-driven Goal Recognition Design For General Behavioral Agents, by Robert Kasumba et al.
Data-Driven Goal Recognition Design for General Behavioral Agents
by Robert Kasumba, Guanghui Yu, Chien-Ju Ho, Sarah Keren, William Yeoh
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 This paper proposes a data-driven approach to goal recognition design in decision-making environments. The existing methods are computationally demanding and assume optimal agent behavior, which is not realistic. The proposed approach uses worst-case distinctiveness (wcd) as a measure of difficulty in inferring the goal of an agent. A machine learning model predicts wcd for a given environment and agent behavior, and a gradient-based optimization framework optimizes decision-making environments for enhanced goal recognition. The approach outperforms existing methods in reducing wcd and improving runtime efficiency in conventional settings. It also adapts to complex environments, flexible budget constraints, and suboptimal agent behavior. The method is tested through human-subject experiments that confirm its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better decisions by making it easier to figure out what others are trying to achieve. Right now, there are many ways to do this, but they can be slow or only work well if people act perfectly. The researchers came up with a new way that uses machine learning and math to find the best solution. They tested it in simulations and found that it works better than other methods and is fast enough to use in real-life situations. They even did experiments with humans to show that it can be used to make decisions in the real world. |
Keywords
* Artificial intelligence * Machine learning * Optimization
