Summary of Behavior Trees Enable Structured Programming Of Language Model Agents, by Richard Kelley
Behavior Trees Enable Structured Programming of Language Model Agents
by Richard Kelley
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 paper explores the limitations of language models trained on internet-scale data sets, which can be brittle in unexpected ways. It argues that behavior trees provide a unifying framework for combining language models with classical AI and traditional programming. The authors introduce Dendron, a Python library for programming language model agents using behavior trees. Three case studies demonstrate the approach’s effectiveness: building a chat agent, an infrastructure inspection agent for mobile robots or vehicles, and a safety-focused agent. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how big language models are good at some things, but they can be tricky to work with in certain situations. The authors think that something called “behavior trees” is a good way to make these models work better. They created a tool called Dendron that helps people program language model agents using behavior trees. This tool is useful for making chatbots, robots that inspect infrastructure, and other types of AI systems. |
Keywords
» Artificial intelligence » Language model
