(futuristic jingle)
>>As a segue into our next metaheuristic
I want to first introduce the idea
of autonomous agents and virtual swarms.
First, let me start off
with the idea of agent based modeling.
An agent based model is a computer simulation
that represents individual entities
in a dynamic social system,
it takes a bottom-up versus a top-down approach.
Agents represent heterogeneous and autonomous entities
who interact with each other and/or their environment
based on a set of rules,
that is that the decision-making is decentralized.
From these interactions, macro-scale behaviors may emerge,
this is called emergent behavior.
So why agent based modeling?
Well, it's often true that the whole
is greater than the sum of its parts
and the need of analyzing the complexity of systems
with less mechanistic and more organic approaches
has emerged in different fields.
Complexity theory shows that even if we were
to be capable of having a complete understanding
of the factors affecting individual action,
this would still be insufficient to predict group behavior.
Generally, an agent has four features,
autonomy, social ability,
reactivity and proactivity.
With autonomy, each agent acts independently
and makes its own decisions.
With social ability each agent
can interact with other agents.
In reactivity, each agent will react
appropriately to different stimuli
and proactivity, each agent pursues its own goals
and acts in its own self-interest.
Let's take an example of an agent
and emergent behavior in the real world.
Ants, ants are not smart,
ants are not clever little engineers,
architects or warriors, at least not as individuals.
Ants are not smart, but ant colonies are.
A colony can solve problems unthinkable for individual ants,
for example, finding the shortest path
to the best food source and allocating workers
to different tasks.
As individuals, ants might be tiny dummies
but as colonies, they respond quickly
and effectively to their environment,
they do it with something called swarm intelligence.
Where this intelligence comes from,
raises a fundamental question in nature,
how do these simple actions of individuals
add up to the complex behavior of a group?
How do hundreds of honey bees make a critical decision
about their hive if many of them disagree?
What enables a school of herring
to coordinate its movements so precisely
that it can change direction
in a flash like a single organism.
The collective abilities of such animals,
none of which grasp the big picture,
but each of which contributes to the group success
seem miraculous even to the biologists who know them best,
yet during the past few decades,
researchers have come up with intriguing insights.
Back to our ants, like I said, ants are not smart.
One key to an ant colony, for example,
is that no-one is in charge,
no generals command the ant warriors,
no managers boss around the ant workers,
the queen plays no role except to lay eggs
and even with a half a million ants,
a colony functions just fine with no management at all,
at least none that we would recognize.
It relies, instead, upon countless
interactions between individuals, each of which
is following simple rules of thumb,
the system is self-organizing.
Ants communicate by touch and smell,
when one ant bumps into another
it sniffs with its antenna to find out
if the other belongs to the same nest
and where it has been working.
Ants that work outside the nest
smell different from those that stay inside.
Before they leave the nest each day foragers normally wait
for early morning patrollers to return.
As patrollers enter the nest,
they touch antenna briefly with foragers,
when a forager has contact with a patroller,
it is a stimulus for the forager to go out
but the forager needs several contacts,
no more than 10 seconds apart, before it will go out.
Foragers use the rate of their encounters with patrollers
to tell if its safe to go out.
Once the ants start foraging and bringing back food,
other ants join the effort, depending on the rate
at which they encounter returning foragers.
A forager won't come back until it finds something,
the less food there is, the longer it takes
for the forager to find it and get back,
the more food there is, the faster it comes back.
That's how swarm intelligence works,
simple creatures, following simple rules,
each one acting on local information.
No ant sees the big picture,
no ant tells any other ant what to do.
Some ant species go about this
with more sophistication than others
but the bottom line is that no leadership is required.
Even complex behavior may be coordinated
by relatively simple interactions.
We're going to exploit this property
of simple agents and simple rules
to look at some complex behaviors
and solve some complex problems.
(futuristic jingle)