Summary of Exploring and Learning Structure: Active Inference Approach in Navigational Agents, by Daria De Tinguy and Tim Verbelen and Bart Dhoedt
Exploring and Learning Structure: Active Inference Approach in Navigational Agents
by Daria de Tinguy, Tim Verbelen, Bart Dhoedt
First submitted to arxiv on: 12 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Neural and Evolutionary Computing (cs.NE); Robotics (cs.RO)
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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 novel computational model for navigation and mapping, inspired by animal navigation strategies, integrates traditional cognitive mapping approaches with an Active Inference Framework (AIF) to learn an environment structure. This model efficiently uses memory, imagination, and strategic decision-making to navigate complex environments. By combining topological mapping for long-term memory and AIF for navigation planning and structure learning, the model can dynamically apprehend environmental structures and expand its internal map during exploration. Comparative experiments with the Clone-Structured Graph (CSCG) model demonstrate the new model’s ability to rapidly learn environmental structures in a single episode with minimal navigation overlap. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way for computers to navigate and make maps, inspired by how animals find their way around. The approach combines two existing methods to create a more efficient and effective system. This system can quickly learn the structure of an environment and make good decisions about where to go next. It does this without needing prior information about the environment or what kind of observations it will encounter. |
Keywords
» Artificial intelligence » Inference
