Adaptive Behavior, 4 (3/4)
Adaptive Behavior
Volume 4, Number 3/4
Winter/Spring 1996
Table of Contents
Peter M. Todd
Introduction to the Special Issue on Environment Structure and Behavior
Jim H. Belanger and Mark A. Willis
Adaptive Control of Odor-Guided Locomotion: Behavioral Flexibility as an Antidote to Environmental Unpredictability
Adaptive Behavior, 4 (3/4), 217-253.
Christophe Le Page and Philippe Cury
How Spatial Heterogeneity Influences Population Dynamics: Simulations in SEALAB
Adaptive Behavior, 4 (3/4), 255-281.
Jeffrey A. Fletcher, Martin Zwick, and Mark A. Bedau
Dependence of Adaptability on Environmental Structure in a Simple Evolutionary Model
Adaptive Behavior, 4 (3/4), 283-315.
Filippo Menczer and Richard K. Belew
From Complex Environments to Complex Behaviors
Adaptive Behavior, 4 (3/4), 317-363.
Peter M. Todd and Holly A. Yanco
Environmental Effects on Minimal Behaviors in the Minimat World
Adaptive Behavior, 4 (3/4), 365-413.
David Kirsh
Adapting the Environment Instead of Oneself
Adaptive Behavior, 4 (3/4), 415-452.
Peter Godfrey-Smith
Précis of Complexity and the Function of Mind in Nature
Daniel W. McShea
Unpredictability! and the Function of Mind in Nature
Susan Oyama
The Ins and Outs of Nature and Mind
Mark A. Bedau
The Extent to Which Organisms Construct Their Environments
Richard K. Belew
Developments Across the Internalist/Externalist Dichotomy
Peter Godfrey-Smith
Replies to Four Critics
Pages 211-215
Introduction to the Special Issue on Environment Structure and Behavior
By Peter M. Todd
Adaptive Control of Odor-Guided Locomotion: Behavioral Flexibility as an Antidote to Environmental Unpredictability
By Jim H. Belanger, Mark A. Willis
Abstract
Many animals find distant unseen resources by guiding their locomotion through fluid media, using olfactory information acquired from plumes of odorant molecules issuing from the resources of interest. This behavior occurs in birds and fish, but much of our knowledge of it derives from flying insects, especially moths. It is a highly integrative behavior, requiring not only the integration of olfactory information with a behavioral strategy to maintain contact with the odor plume, but also an ability to detect the direction of fluid flow that is carrying the odor cue. The temporal-spatial structure of the odor plume is determined by the fluid dynamics of the environment, and it profoundly affects the behavior. Thus, the success of animals (or artificial agents) is determined by an interaction between sensory input and internally generated behaviors. We have implemented behavioral-level simulations of odor-modulated moth flight to understand how the properties of the odor stimulus and the behavioral system interact to result in successful source location. Even simple reflexive models can track predictable, laminar-flow plumes, but only models with internally generated behaviors can track unpredictable, turbulent plumes. The "best" behavioral strategy depends on both the structure of the odor stimulus and an agent's performance limits.Key Words
orientation; pheromone; moth; flight; simulation; fluid dynamics; olfaction; search behavior
How Spatial Heterogeneity Influences Population Dynamics: Simulations in SEALAB
By Christophe Le Page, Philippe Cury
Abstract
The influence of nest site selection on population dynamics is explored by considering two reproductive strategies. The first one, described as opportunist, is the most common in ecology. It postulates that an individual tries to select and track the optimal environmental conditions that maximize its total reproductive output. The second one, described as obstinate, comes from a generalization of "natal homing" recently proposed by Cury (1994). It assumes that a newborn individual memorizes early environmental cues that later determine its reproductive environment. We use an individual-based model named SEALAB to track artificial fish in a heterogeneous environment displayed as a lattice of hexagonal patches. The effects of two components of the lattice structure - namely the composition (amount of each patch type) and the configuration (spatial arrangement of patches) - on the success of the searching behavior are examined. For the obstinate strategy, whose searching behavior is characterized by a simple random walk, a spatial redundancy index seems sufficient to account for the spatial heterogeneity influence, whereas for the opportunist strategy, more subtle indices are needed. We develop a quantitative measure of spatial local optima that could apply to any searching behavior based on local hill-climbing or local gradient information. Our results indicate how heterogeneity causes opportunist individuals to get stuck in local spatial optima. The use of a spatially explicit individual-based model such as SEALAB is justified by the possibility of carefully estimating simultaneously the value and the sensitivity of global parameters in relation to the spatial heterogeneity of the environment.Key Words
artificial life; fish; natal homing; nest site selection; population dynamics; spatial heterogeneity
Dependence of Adaptability on Environmental Structure in a Simple Evolutionary Model
By Jeffrey A. Fletcher, Martin Zwick, Mark A. Bedau
Abstract
This article concerns the relationship between the detectable and useful structure in an environment and the degree to which a population can adapt to that environment. We explore the hypothesis that adaptability will depend unimodally on environmental variety, and we measure this component of environmental structure using the information-theoretical uncertainty (Shannon entropy) of detectable environmental conditions. We define adaptability as the degree to which a certain kind of population successfully adapts to a certain kind of environment, and we measure adaptability by comparing a population's size to the size of a nonadapting, but otherwise comparable, population in the same environment. We study the relationship between adaptability and environmental structure in an evolving artificial population of sensorimotor agents that live, reproduce, and die in a variety of environments. We find that adaptability does not show a unimodal dependence on environmental variety alone, although there is justification for preserving our unimodal hypothesis if we consider other aspects of environmental structure. In particular, adaptability depends not just on how much structural information is detectable in the environment but also on the extent to which this informations is unambiguous and valuable (i.e., whether the information accurately signals a difference that makes a difference). How best to measure and integrate these other components of environmental structure remains unresolved.Key Words
adaptation; environment; environmental structure; evolution; sensorimotor function; Shannon entropy
From Complex Environments to Complex Behaviors
By Filippo Menczer, Richard K. Belew
Abstract
Adaptation of ecological systems to their environments is commonly viewed through some explicit fitness function defined a priori by the experimenter or measured a posteriori by estimations based on population size or reproductive rates. These methods do not capture the role of environmental complexity in shaping the selective pressures that control the adaptive process. Ecological simulations enabled by computational tools such as latent energy environment (LEE) model allow us to characterize more closely the effects of environmental complexity on the evolution of adaptive behaviors. LEE is described in this article. Motivation for the development of the LEE model arises from the need to vary complexity in controlled and predictable ways, without assuming the relationship of these changes to the adaptive behaviors they engender. This goal is achieved through a careful characterization of environments in which different forms of "energy" are well defined. A genetic algorithm using endogenous fitness, and local selection is used to model the evolutionary process. Individuals in the population are modeled by neural networks with simple sensorimotor systems, and variations in their behaviors are related to interactions with varying environments. We outline the results of three experiments that analyze different sources of environmental complexity and their effects on the collective behaviors of evolving populations.Key Words
latent energy environments; complexity; behavior; adaptation; endogenous fitness; space
Environmental Effects on Minimal Behaviors in the Minimat World
By Peter M. Todd, Holly A. Yanco
Abstract
The structure of an environment affects the behaviors of the organisms that have evolved in it. How is that structure to be described, and how can its behavioral consequences be explained and predicted? We aim to establish initial answers to these questions by simulating the evolution of very simple organisms in simple environments with different structures. Our artificial creatures, called "minimats", have neither sensors nor memory and behave solely by picking amongst the actions of moving, eating, reproducing, and sitting, according to an inherited probability distribution. Our simulated environments contain only food (and multiple minimats) and are structured in terms of their spatial and temporal food density and the patchiness with which the food appears. Changes in these environmental parameters affect the evolved behaviors of minimats in different ways, and all three parameters are of importance in describing the minimat world. One of the most useful behavioral strategies that evolves is "looping" movement, which allows minimats - despite their lack of internal state - to match their behavior to the temporal (and spatial) structure of their environment. Ultimately we find that minimats construct their own environments through their individual behaviors, making the study of the impact of global environment structure on individual behavior much more complex.Key Words
environment structure; evolved behavior; resource density, resource regrowth; resource patches, simulation
Adapting the Environment Instead of Oneself
By David Kirsh
Abstract
This article examines some of the methods used by animals and humans to adapt their environment. Because there are limits on the number of different tasks a creature can be designed to do well in, creatures with the capacity to redesign their environments have an adaptive advantage over those who can adapt only passively to existing environmental structures. To clarify environmental redesign, I rely on the formal notion of a task environment as a directed graph in which the nodes are states and the links are actions. One natural form of redesign is to change the topology of this graph structure so as to increase the likelihood of task success or to reduce its expected cost, measured in physical terms. This may be done by eliminating initial states, hence eliminating choice points; by changing the action repertoire; by changing the consequence function; and, lastly, by adding choice points. Another major method for adapting the environment is to change its cognitive congeniality. Such changes leave the state space formally intact but reduce the number and cost of mental operations needed for task success; they reliably increase the speed, accuracy, or robustness of performance. The last section of the article describes several of these epistemic or complementary actions found in human performance.Key Words
adaptation; task environment; redesign; epistemic actions; complementary actions
Pages 453-465
Précis of Complexity and the Function of Mind in Nature
By Peter Godfrey-Smith
Pages 466-470
Unpredictability! and the Function of Mind in Nature
By Daniel W. McShea
Pages 471-475
The Ins and Outs of Nature and Mind
By Susan Oyama
Pages 476-482
The Extent to Which Organisms Construct Their Environments
By Mark A. Bedau
Pages 483-485
Developments Across the Internalist/Externalist Dichotomy
By Richard K. Belew
Pages 486-493
Replies to Four Critics
By Peter Godfrey-Smith
Pages 495-497
Author Index to Volume 4
Pages 499-502
Key Word Index to Volume 4
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