Animat

About: Animat is a research topic. Over the lifetime, 268 publications have been published within this topic receiving 5940 citations.

Knowledge Growth in an Artificial Animal

Abstract: This paper describes work using an artificial, behaving, animal model (termed an “ani-mat”) to study intelligence at a primitive level. The motivation for our somewhat unusual approach is the view that the essence of intelligence is exhibited by animals surviving in real environments. Therefore, insight into intelligence should be obtainable from simulated animals and environments, even simple ones, provided the simulations suitably reflect the animal’s survival problems. The starting point for the research is an explicit definition of intelligence which guides model construction. In experiments, a particular animat is placed in an environment and evaluated as to its rates of improvement in performance and perceptual generalization. Learning is central, because we wish to provide the animat with adaptive mechanisms which yield rapid and solid improvement but themselves contain minimal a priori information.

Artificial Minds

Abstract: From the Publisher: Stan Franklin is the perfect tour guide through the contemporary interdisciplinary matrix of artificial intelligence, cognitive science, cognitive neuroscience, artificial neural networks, artificial life, and robotics that is producing a new paradigm of mind. Along the way, Franklin makes the case for a perspective that rejects a rigid distinction between mind and non-mind in favor of a continuum from less to more mind, and for the role of mind as a control structure with the essential task of choosing the next action. Selected stops include the best of the work in these different fields, with the key concepts and results explained in just enough detail to allow readers to decide for themselves why the work is significant. Major attractions include animal minds, Newell's SOAR, the three Artificial Intelligence debates, Holland's genetic algorithms, Wilson's Animat, Brooks' subsumption architecture, Jackson's pandemonium architecture, Ornstein's multimind, Minsky's society of mind, Maes's behavior networks, Edelman's neural Darwinism, Drescher's schema mechanisms, Kanerva's sparse distributed memory, Hofstadter and Mitchell's Copycat, and Agre and Chapman's deictic representations.

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.

Classifier Systems and the Animat Problem

Abstract: This paper characterizes and investigates, from the perspective of machine learning and, particularly, classifier systems, the learning problem faced by animals and autonomous robots (here collectively termed animats). We suggest that, to survive in their environments, animats must in effect learn multiple disjunctive concepts incrementally under payoff (needs-satisfying) feedback. A review of machine learning techniques indicates that most relax at least one of these constraints. In theory, classifier systems satisfy the constraints, but tests have been limited. We show how the standard classifier system model applies to the animat learning problem. Then, in the experimental part of the paper, we specialize the model and test it in a problem environment satisfying the constraints and consisting of a difficult, disjunctive Boolean function drawn from the machine learning literature. Results include: learning the function in significantly fewer trials than a neural-network methods learning under payoff regimes that include both noisy payoff and partial reward for suboptimal performances demonstration, in a classifier system, of a theoretically predicted property of genetic algorithms: the superiority of crossovers to point mutationss and automatic control of variation (search) rate based on system entropy. We conclude that the results support the classifier system approach to the animat problem, but suggest work aimed at the emergence of behavioral hierarchies of classifiers to offset slower learning rates in larger problems.