Summary of Dynamic Planning in Hierarchical Active Inference, by Matteo Priorelli and Ivilin Peev Stoianov
Dynamic planning in hierarchical active inference
by Matteo Priorelli, Ivilin Peev Stoianov
First submitted to arxiv on: 18 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); 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 paper explores the concept of dynamic planning in active inference, which refers to the brain’s ability to infer and impose motor trajectories based on cognitive decisions. Building on previous studies that applied active inference to explain human and animal behaviors, this research aims to develop a comprehensive model for planning realistic actions in changing environments. The study focuses on two key aspects of biological behavior: understanding and exploiting affordances for object manipulation, and learning hierarchical interactions between the self and the environment. By comparing different design choices and providing basic examples, the paper presents a novel direction in active inference: hybrid representations in hierarchical models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how our brains make decisions and take actions. It’s like when you pick up a pencil to draw or use a tool to fix something. The researchers want to understand how we do this in different situations, like changing environments or working with other people. They’re looking at two important parts of this: understanding what things can be used for (like using a stick as a shovel) and learning how our actions affect the world around us. The paper compares different ideas about how this works and shows some simple examples to help explain it. |
Keywords
* Artificial intelligence * Inference
