Summary of Multi-agent Norm Perception and Induction in Distributed Healthcare, by Chao Li et al.
Multi-Agent Norm Perception and Induction in Distributed Healthcare
by Chao Li, Olga Petruchik, Elizaveta Grishanina, Sergey Kovalchuk
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multiagent Systems (cs.MA)
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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 paper proposes a Multi-Agent Norm Perception and Induction Learning Model to facilitate the integration of autonomous agent systems into distributed healthcare environments through dynamic interaction processes. The model enables agents to simultaneously learn descriptive norms, which capture collective tendencies, and prescriptive norms, which dictate ideal behaviors. By leveraging parameterized mixed probability density models and practice-enhanced Markov games, the multi-agent system perceives descriptive norms in dynamic interactions and captures emergent prescriptive norms. Experiments were conducted using a dataset from a neurological medical center spanning from 2016 to 2020. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about creating better communication between computers that help healthcare professionals work together effectively. It uses special computer models to learn how people behave and what they should do, so these computers can work well together too. The model helps the computers understand both how people usually act (descriptive norms) and what they should be doing (prescriptive norms). This is important for healthcare because it will help computers make better decisions and work more efficiently. |
Keywords
» Artificial intelligence » Probability
