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Active Shape Discrimination with Compliant Bodies as Reservoir Computers
Chris Johnson, Andrew Philippides, Philip Husbands1
1University of Sussex.
Artificial Life
|March 3, 2016
Summary
Complex body dynamics, when coupled with simple nervous systems, enable adaptive behavior. This research highlights the body as a key computational element, processing information and generating reflexive or minimally cognitive actions.
Area of Science:
- Robotics
- Computational Neuroscience
- Artificial Intelligence
Background:
- Compliant bodies with complex dynamics offer potential for simplified control and adaptive behavior in sensorimotor loops.
- Traditional approaches often focus on complex neural networks for control.
Purpose of the Study:
- To investigate the role of compliant bodies as the primary computational locus for adaptive behavior.
- To demonstrate that complex body dynamics can perform information processing tasks.
Main Methods:
- Revisiting Beer's experiment, replacing continuous-time recurrent neural networks with reservoir computing networks abstracted from compliant bodies.
- Utilizing reservoir computing to model the dynamics of compliant bodies.
Main Results:
- Adaptive behavior was produced in an agent where the body served as the main computational hub.
- Bodies with complex dynamics demonstrated capabilities for information integration, storage, and processing.
- The addition of a simple nervous system to these bodies resulted in behavior describable as reflexive or minimally cognitive.
Conclusions:
- The physical embodiment and its complex dynamics are crucial for generating adaptive and cognitive-like behaviors.
- Reservoir computing abstracted from compliant bodies offers a viable alternative to traditional neural networks for embodied AI.
- This approach emphasizes the body's role in computation, paving the way for more efficient and adaptive artificial agents.
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