Artificial Fishes: Autonomous Locomotion, Perception, Behavior, and Learning in a Simulated Physical World

Abstract

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.