Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.01349 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909690877706240 |
|---|---|
| author | Baranski, Alex Tani, Jun |
| author_facet | Baranski, Alex Tani, Jun |
| contents | Living beings are able to solve a wide variety of problems that they encounter rarely or only once. Without the benefit of extensive and repeated experience with these problems, they can solve them in an ad-hoc manner. We call this capacity to always find a solution to a physically solvable problem $hyperadaptability$. To explain how hyperadaptability can be achieved, we propose a theory that frames behavior as the physical manifestation of a self-modifying search procedure. Rather than exploring randomly, our system achieves robust problem-solving by dynamically ordering an infinite set of continuous behaviors according to simplicity and effectiveness. Behaviors are sampled from paths over cognitive graphs, their order determined by a tight behavior-execution/graph-modification feedback loop. We implement cognitive graphs using Hebbian-learning and a novel harmonic neural representation supporting flexible information storage. We validate our approach through simulation experiments showing rapid achievement of highly-robust navigation ability in complex mazes, as well as high reward on difficult extensions of classic reinforcement learning problems. This framework offers a new theoretical model for developmental learning and paves the way for robots that can autonomously master complex skills and handle exceptional circumstances. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01349 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Life, uh, Finds a Way: Hyperadaptability by Behavioral Search Baranski, Alex Tani, Jun Artificial Intelligence Neural and Evolutionary Computing Living beings are able to solve a wide variety of problems that they encounter rarely or only once. Without the benefit of extensive and repeated experience with these problems, they can solve them in an ad-hoc manner. We call this capacity to always find a solution to a physically solvable problem $hyperadaptability$. To explain how hyperadaptability can be achieved, we propose a theory that frames behavior as the physical manifestation of a self-modifying search procedure. Rather than exploring randomly, our system achieves robust problem-solving by dynamically ordering an infinite set of continuous behaviors according to simplicity and effectiveness. Behaviors are sampled from paths over cognitive graphs, their order determined by a tight behavior-execution/graph-modification feedback loop. We implement cognitive graphs using Hebbian-learning and a novel harmonic neural representation supporting flexible information storage. We validate our approach through simulation experiments showing rapid achievement of highly-robust navigation ability in complex mazes, as well as high reward on difficult extensions of classic reinforcement learning problems. This framework offers a new theoretical model for developmental learning and paves the way for robots that can autonomously master complex skills and handle exceptional circumstances. |
| title | Life, uh, Finds a Way: Hyperadaptability by Behavioral Search |
| topic | Artificial Intelligence Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2410.01349 |