Numerical recipes: the art of scientific computing
Numerical recipes: the art of scientific computing
Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Intelligence as adaptive behavior: an experiment in computational neuroethology
Intelligence as adaptive behavior: an experiment in computational neuroethology
Sensory elements in pattern-generating networks
Making them move
Artificial Intelligence
Simulation of adaptive behavior in animats: review and prospect
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
The motion dynamics of snakes and worms
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
Designing Autonomous Agents: Theory and Practice from Biology to Engineering and Back
Designing Autonomous Agents: Theory and Practice from Biology to Engineering and Back
Robot Vision
Interaction and intelligent behavior
Interaction and intelligent behavior
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This article develops artificial life patterned after animals as evolved as those in the superclass Pisces. It demonstrates a virtual marine world inhabited by realistic artificial fishes. Our algorithms emulate not only the appearance, movement, and behavior of individual animals, but also the complex group behaviors evident in many aquatic ecosystems. We model each animal holistically. An artificial fish is an autonomous agent situated in a simulated physical world. The agent has a a three-dimensional body with internal muscle actuators and functional fins that deforms and locomotes in accordance with biomechanic and hydrodynamic principles; b sensors, including eyes that can image the environment; and c a brain with motor, perception, behavior, and learning centers. Artificial fishes exhibit a repertoire of piscatorial behaviors that rely on their perceptual awareness of their dynamic habitat. Individual and emergent collective behaviors include caudal and pectoral locomotion, collision avoidance, foraging, preying, schooling, and mating. Furthermore, artificial fishes can learn how to locomote through practice and sensory reinforcement. Their motor learning algorithms discover muscle controllers that produce efficient hydrodynamic locomotion. The learning algorithms also enable artificial fishes to train themselves to accomplish higher level, perceptually guided motor tasks, such as maneuvering to reach a visible target.