Summary of Toward Human-ai Alignment in Large-scale Multi-player Games, by Sugandha Sharma et al.
Toward Human-AI Alignment in Large-Scale Multi-Player Games
by Sugandha Sharma, Guy Davidson, Khimya Khetarpal, Anssi Kanervisto, Udit Arora, Katja Hofmann, Ida Momennejad
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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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 method aims to evaluate human-AI alignment in complex multi-agent games by introducing an interpretable task-sets framework that focuses on high-level behavioral tasks rather than low-level policies. By analyzing extensive human gameplay data from Xbox’s Bleeding Edge, the researchers uncovered behavioral patterns in a complex task space and created a behavior manifold capturing interpretable axes such as fight-flight, explore-exploit, and solo-multi-agent. The approach consists of three components: analyzing human gameplay data, training an AI agent to play Bleeding Edge using a Generative Pretrained Causal Transformer, and projecting both human and AI gameplay to the proposed behavior manifold for comparison. The study highlights stark differences in policy between humans and AI agents, emphasizing the need for interpretable evaluation, design, and integration of AI in human-aligned applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that artificial intelligence (AI) players behave like real people do when playing games together. To make this happen, researchers created a new way to measure how well AI players match up with humans. They looked at what people do when they play the game Bleeding Edge and found patterns in their behavior. Then, they used an AI model called a Generative Pretrained Causal Transformer to get the AI player to behave like people do. The researchers compared how both types of players behave by projecting them onto a special map that shows different behaviors. They found that while humans are more flexible and play with others, AI players tend to stick to their own plan. This means we need to find new ways to make sure AI players work well with humans. |
Keywords
» Artificial intelligence » Alignment » Transformer
