Summary of Learning and Sustaining Shared Normative Systems Via Bayesian Rule Induction in Markov Games, by Ninell Oldenburg and Tan Zhi-xuan
Learning and Sustaining Shared Normative Systems via Bayesian Rule Induction in Markov Games
by Ninell Oldenburg, Tan Zhi-Xuan
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a framework for building artificial intelligence (AI) agents that can cooperate with humans by assuming shared norms. The authors hypothesize that AI agents can learn the norms of an existing population by observing compliance and violation, and then use this knowledge to bootstrap common understanding among other agents. This leads to the stability of normative systems, enabling new entrants to rapidly learn and adhere to the norms. The researchers formalize this framework using Markov games and demonstrate its operation in a multi-agent environment via approximately Bayesian rule induction of obligative and prohibitive norms. The approach enables AI agents to sustain cooperative institutions, promoting collective welfare while still allowing them to act in their own interests. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us build better artificial intelligence that can work with humans. The researchers think about how AI agents can learn the rules that people follow, even if they don’t know what those rules are exactly. They believe that by assuming these shared rules exist, an AI agent can figure out what the rules are just by watching what other people do and don’t do. This helps groups of AI agents work together better and create systems that everyone follows. The researchers test this idea using a special kind of game and show how it works in a group of AI agents. |
