Provably Optimal Learning Algorithms for Assistance Games

2026-07-09 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Mathematics & Computational Sciences · Depth: Expert, quick

Summary

This paper presents the first provably efficient learning algorithms for repeated assistance games, an online framework where an informed human and an uninformed assistant interact over T timesteps to optimize a shared reward. The human observes a latent world state, while the assistant only sees the human's actions. The authors introduce "assistance regret," defined as the difference between cumulative utility and optimal joint policies in hindsight. They propose decentralized algorithms achieving a (1-1/e)-approximate assistance regret rate of Õ(T^3/4), with polynomial runtime in action and state space sizes. These algorithms are general, supporting any no-regret algorithm for the assistant. The research also proves that improving the regret approximation factor beyond (1-1/e) is computationally intractable. Furthermore, a pseudo-decentralized setting, utilizing a shared random string, yields an improved rate of Õ(T^1/2), which is optimal up to logarithmic factors. The paper was published on 2026-07-09.

Key takeaway

For AI Scientists designing human-AI collaborative systems, this research provides critical benchmarks for learning in assistance games. You should consider the (1-1/e) approximation factor as a theoretical limit for regret in decentralized settings. When aiming for optimal performance, explore pseudo-decentralized architectures that utilize shared random strings to achieve Õ(T^1/2) regret, significantly outperforming fully decentralized approaches. This informs algorithm selection for robust, efficient human-assistant interactions.

Key insights

Provably efficient learning algorithms for human-assistant interaction achieve near-optimal regret in online assistance games.

Principles

• Optimal regret approximation is (1-1/e).
• Decentralized algorithms can use any no-regret method.
• Shared randomness improves regret rates.

Method

Decentralized algorithms for human and assistant achieve Õ(T^3/4) regret. Pseudo-decentralized methods, using shared random strings, achieve Õ(T^1/2) regret.

In practice

• Design human-AI systems with assistance regret in mind.
• Explore shared randomness for improved coordination.

Topics

• Assistance Games
• Online Learning Algorithms
• Regret Minimization
• Human-AI Interaction
• Decentralized Learning
• Optimal Policies

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.