atlas

Research map / Multi-agent systems and games / Human-AI and goal-conditioned

Partner-Aware Algorithms in Decentralized Cooperative Bandit Teams

AAAI 2022 · Multi-agent systems and games › Human-AI and goal-conditioned · approach: Collaborative multi-agent perception · 3 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use partner-aware action prediction instead of Naïve UCBHuman-robot bandit team · Burger stackingcumulative regret: 3.6±0.31 -> 2.7±0.31Helped
Use partner-aware action prediction instead of Naïve UCBHuman-robot bandit team · Burger stackingsatisfied customers: 12.7±0.6 -> 13.5±0.6Helped
Use partner-aware action prediction instead of Naïve UCBHuman-AI casino team · Online casino study, 25 casinoscoins earned: 17,717.2±235.0 -> 18,825.8±39.3Helped
Use partner-aware action prediction rather than naïve decentralized UCB or Thompson samplingDecentralized bandit team · Fixed and random reward-mean simulationscumulative regretHelped
Set the leader action repetition parameter to L=1 rather than larger L values (ablation)Partner-Aware UCB · Fixed reward-mean simulationcumulative regretNo clear effect
Increase the follower's action-history window from W=1 (ablation)Partner-Aware UCB with a UCB leader · Fixed reward-mean simulationcumulative regretHurt
Use W=25 rather than other tested window widths (ablation)Partner-Aware UCB follower with a knowledge-gradient leader · Random reward-mean simulationcumulative regretHelped
Use a knowledge-gradient leader instead of a UCB leader (ablation)Partner-Aware UCB follower team · Random reward-mean simulationcumulative regretHurt
Extend the partner-aware hierarchy to more agentsPartner-Aware UCB · Simulations with 2, 3, and 4 agentscumulative regretHelped
Apply Partner-Aware UCB rather than Naïve UCBHuman-AI casino team · Online casino study, participant-level comparisoncumulative regretHelped
Apply Partner-Aware UCB rather than Naïve UCBHuman-AI casino team · Online casino study, casino-level comparisoncumulative regretMixed

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy