Abstract
A species of pufferfish builds fascinating circular nests on the sea floor to attract mates. This project simulates the nest building behavior in a cellular automaton using the morphognosis model. The model features hierarchical spatial and temporal contexts that output motor responses from sensory inputs. By considering the biological neural network of the pufferfish as a black box, and decomposing only its external behavior, an artificial counterpart can be generated. In this way a complex biological system producing a behavior can be filtered into a system containing only functions that are essential to reproduce the behavior. The derived system not only has intrinsic value as an artificial entity but also might help to ascertain how the biological system produces the behavior.
Highlights
Building on the replication of C. elegans locomotion and foraging, the pufferfish nest building task represented a further challenge for the Morphognosis model
The result is set of metamorph rules and trained artificial neural network (ANN) that accomplish the task
Morphognosis features: 1. A method for integrating knowledge of events occurring in space and time dimensions in scalable complexity
Summary
The nest contains a central smooth area surrounded by radial furrows and is about 2 meters in diameter. This project simulates pufferfish nest building behavior in a cellular automaton using the Morphognosis model. Through a black box decomposition of nest building behavior into space-time rules, an artificial pufferfish is embodied an ANN. A morphognostic can be viewed as a structure of progressively larger nested chunks of space-time knowledge that form a hierarchy of contexts.
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