Summary of Cooperative Bayesian Optimization For Imperfect Agents, by Ali Khoshvishkaie et al.
Cooperative Bayesian Optimization for Imperfect Agents
by Ali Khoshvishkaie, Petrus Mikkola, Pierre-Alexandre Murena, Samuel Kaski
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 proposed cooperative Bayesian optimization problem involves two agents working together to optimize a black-box function of two variables. Each agent has control over one variable, and they must strategically plan their queries to identify the global maximum of the function. The solution is formulated as sequential decision-making, where an agent models the user as computationally rational with prior knowledge about the function. By using Bayes Adaptive Monte Carlo planning and incorporating a user model that accounts for conservative belief updates and exploratory sampling, the agents can better identify the global maximum. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of artificial intelligence (AI) assistants works together to optimize a special kind of math problem. Each AI has control over one part of the problem and must figure out which parts to look at next. The goal is to find the best answer. To do this, they use a planning strategy that takes into account how humans think about problems and how they make decisions. This approach helps the AIs learn better and faster. |
Keywords
* Artificial intelligence * Optimization
