Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Do computer simulations really cognize?
Journal of Experimental & Theoretical Artificial Intelligence
Synthetic ethology and the evolution of cooperative communication
Adaptive Behavior
Autonomous Robots
Understanding intelligence
Brainchildren: Essays on Designing Minds
Brainchildren: Essays on Designing Minds
Being There: Putting Brain, Body, and World Together Again
Being There: Putting Brain, Body, and World Together Again
From Living Eyes to Seeing Machines
From Living Eyes to Seeing Machines
Knowledge Growth in an Artificial Animal
Proceedings of the 1st International Conference on Genetic Algorithms
Ant Colony Optimization
Comparisons in Evolution and Engineering: The Collective Intelligence of Sorting
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
A Comprehensive Overview of the Applications of Artificial Life
Artificial Life
Landmark vectors with quantized distance information for homing navigation
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Electrolocation with an electric organ discharge waveform for biomimetic application
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
A review of long-term memory in natural and synthetic systems
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
What nervous systems do: early evolution, input-output, and the skin brain thesis
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
How universal can an intelligence test be?
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Hi-index | 0.00 |
The overlapping fields of adaptive behavior and artificial life are often described as novel approaches to biology. They focus attention on bottom-up explanations and how lifelike phenomena can result from relatively simple systems interacting dynamically with their environments. They are also characterized by the use of synthetic methodologies, that is, building artificial systems as a means of exploring these ideas. Two differing approaches can be distinguished: building models of specific animal systems and assessing them within complete behaviorâ聙聰environment loops; and exploring the behavior of invented artificial animals, often called animats, under similar conditions. An obvious question about the latter approach is, how can we learn about real biology from simulation of non-existent animals? In this article I will argue, first, that animat research, to the extent that it is relevant to biology, should also be considered as model building. Animat simulations do, implicitly, represent hypotheses about, and should be evaluated by comparison to, animals. Casting this research in terms of invented agents serves only to limit the ability to draw useful conclusions from it by deflecting or deferring any serious comparisons of the model mechanisms and results with real biological systems. Claims that animat models are meant to be existence proofs, idealizations, or represent general problems in biology do not make these models qualitatively different from more conventional models of specific animals, nor undermine the ultimate requirement to justify this work by making concrete comparisons with empirical data. It is thus suggested that we will learn more by choosing real, and not made-up, targets for our models.