Research (download as word doc)
Social insect colonies dynamically allocate individuals, resources and tasks to collectively solve many computationally difficult problems without centralized control. System behavior appears out of local interactions between individuals with simple rule sets and no global knowledge. Furthermore, such behavioral algorithms are typically robust to change, maintaining their effectiveness in highly varied environments or resource distributions over a wide range of tasks. The mechanisms of such behavior are of great interest from a biological standpoint because they provide insight into how organization appears in nature [1, 2]. Concepts such as collective intelligence and self-organization are also finding increasingly successful application in many non-biological fields such as robotics, artificial intelligence, data mining and network routing [e.g. 3, 4-6]. However, non-biological applications are usually based on generalized metaphors of social insect behavior rather than a direct knowledge and application of the rules actually used by insects [7, 8]. While there are obviously different constraints on non-biological systems, the goals and problems such systems are being designed to solve can be remarkably similar to those facing living systems. This is particularly true in the design of multi-agent autonomous systems where groups of interacting agents or robots attempt to collectively solve a problem. It is probable that there are basic laws governing how individual rules scale to system-level behaviors , yet developing a rigorous description of these rules has proven very difficult. To this end, more research is needed on natural systems in which the emergent behaviors or "goals" of the system and the behaviors of the individuals can be quantitatively described and modeled. I believe that the appearance of coordinated system-level behavior and optimal task allocation in social insects is, in a large part, dependent on rates of feedback and communication between individuals. My research focuses on identifying the mechanisms governing the organization and coordination of swarming behavior in the New World army ant, Eciton burchelli, through field work and computer modeling. Accurate computer models of such systems will allow researchers to examine precisely the influence that specific mechanisms such as individual behavioral rules or communication rates have on the system's function and how changes in these rules affect the system's efficiency.
Nearly 70 years of
field work on Eciton has yielded great advances in our understanding
of the life-history and biology of army ants [13-16, 17 and others]. But
no one has yet quantitatively measured the communication and feedback
processes underlying the organization of E. burchelli swarms. The few
existing computer models of E. burchelli swarm behavior contain limited
biological parameters and were not designed to specifically address the
questions detailed above [18, 19]. I hypothesize that the bulk of basic
observed behaviors in E. burchelli swarms can be explained through
simple interaction rules based on ant density, pheromone concentrations
and resource distribution.
To examine the behavioral rules involved in the organization of E. burchelli swarms, I am creating a detailed individual-based computer simulation of swarm behavior based on parameters determined through my field work and from the literature. Predictions of the model will be tested in the field to verify the accuracy of the model and the modeling work will inform and shape the direction of my field work and allow me to investigate questions that are not easily addressed in the field.
Predictions for model
It is conventionally believed that searching behavior and recruitment rates play a large role in the organization of E. burchelli army ant swarms [13, 15]. However, my observations suggest that the main challenge faced by foraging E. burchelli is maintaining an optimal rate of exchange between covering the widest area possible to maximize food discovery while preserving a sufficiently uniform density of ants in all areas of the swarm to prevent prey from escaping. Because prey items can be as large as 10 cm, ants must also be efficiently allocated for prey processing which can take hours (personal observation). After processing, prey items must be returned to the bivouac along trails of limited width. The model will enable precise examination of what behavioral parameters are most important for maximizing fitness and producing observed swarm patterns over different variations in prey size and distribution.
Model Insights and generalizations
Examination of E.
burchelli foraging behavior suggests the importance of understanding the
type of information or resources the system being examined is designed
to discover and the distribution of these resources in the environment.
This is a crucial question in the development of multi-agent systems as
well - what rule sets should agents have under a given set of assumptions
about the agent's collective goals and the environment in which they will
function. A well designed simulation of army ant swarming behavior offers
a stepping-off point for examining many questions relating to collective
intelligence and the design of self-organizing systems. Army ant foraging
systems have three principle features that are essential to well designed
multi-agent systems as well:
The above three features
of the E. burchelli system are most likely created through the interactions
of four principle components: communication, network size, task fidelity
and task specialization.
Swarm efficiency is influenced by 1) how many forms of communication or
"channels" are available to the system and 2) the vocabulary
size or range of communication available to each channel (i.e. bits /
channel). As discussed earlier, E. burchelli have to make a trade-off
between maintaining sufficient ant density in a given area, maximizing
area covered, and allocating enough ants to process and retrieve prey
items. This trade-off is mediated through two primary forms of communication
in army ants, ant-ant contact rate and response to pheromone. Variations
in contact rate happen on a fast timescale and give the ant feedback about
local density. Variations in pheromone concentration give the ant temporal
feedback about swarm behavior, direction of travel and foraging success.
Density is most likely tuned by the relation between an ant's fidelity
to trail pheromone, and turn angle as influenced by both pheromone concentration
and contact rate (a direct measure of density). This hypothesis is supported
by recent work modeling work showing that spontaneous lane formation can
occur in model army ants responding only to pheromone and avoid collisions
. Pheromone response has been shown to be sigmoidal  but the behavioral
effects of this response curve have been yet been quantitatively examined.
Although army ants can successfully swarm with even a few thousand individuals
(personal observation), it is not yet known how swarm efficiency scales
with swarm size. If self-organizing processes are at work, one would expect
swarm efficiency to increase non-linearly. The size of this effect is
of great interest both for increasing scientific understanding of self-organization
and for providing insight into economies of scale in multi-agent system
design. As system size increases, dynamic allocation of individuals and
tasks also become increasingly important as do the consequences of unrestrained
positive or negative feedback.
Task fidelity and
task specialization: Task fidelity and task specialization play a
huge role in organizing E. burchelli swarms [12, 25, 26]. One might expect
that 1) the benefits of task fidelity or specialization should increase
as potential tasks or environmental variation become more predictable
and 2) system efficiency should increase with the systems' ability to
allocate resources. In army ants, individuals involved in foraging and
prey retrieval are found in three distinct caste sizes . The largest-sized
group, the "sub-majors", constitute only 3% of the ants in the
colony but make up 26% of all ants retrieving prey. The other two castes
also retrieve prey, often working in teams with the sub-majors, but they
generally spend more time in the swarm and capturing prey than in prey
retrieval . However, there is wide variation both in the numbers and
size classes of ants involved in prey retrieval . The primary tools
the colony has to control task allocation are communication and feedback.
The effects of "noise" in the system must be considered as well.
In particular, in a stochastic environment, too low fidelity leads quickly
to chaos (ants keep switching tasks and never get anything done). But
too high fidelity leads to inefficient resource allocation (all ants end
up in one location or task).
1. Kauffman, S., The
origins of order, self-organization and selection in evolution. 1993,
NY: Oxford University Press.
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