Transcript Lead, Follow, or Get in the Way

Lead, Follow, or Get in the Way
Creating Social Agent Systems in Simulated
Physical Environments
Principal Investigator: Dr. Ben Aguirre
Research Assistant: Eric Best
([email protected])
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Why Social Agent Systems?
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Most simulations create a number of avatars
that are largely homogeneous and do not
have linked goals.
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Based on our research, we believe this is an
inaccurate way to create models of human
beings.
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Disaster Research Center Work
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Based on our work at the Disaster Research
Center (DRC), we have found that people
evacuating a hazard do not rely on individual
goals and behaviors.
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Group behaviors and crowd behaviors impact
goals and reasoning.
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Why Group Behavior?
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Historically, the DRC has found that evacuees
use social behavior when assessing and
reacting to hazards.
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Social bonds are not ignored when dangers
are present.
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Individual, social, and crowd behaviors
interact together to influence decisions and
goals.
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Why Group Behavior?
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Recently, Dr. Ben Aguirre conducted a study
that gathered detailed information about the
victims and evacuees of The Station Nightclub
fire.
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We are aware of detailed exit goals, social
behaviors, and networks among victims and
evacuees.
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Why Group Behavior in ABM?
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Using this detailed dataset, we chose to
create a set of models incorporating social
behavior to replicate the immediate reaction to
the disaster at The Station.
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Eventually, we hope to encourage other model
builders to consider group behavior and nonhomogenous agents.
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Why Group Behavior in ABM?
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Humans do not interact in a vacuum, but most
simulations of humans account only for
individual behavior.
Agent-Based modeling with learning agents is
an obvious avenue for creating simulations with
evolving group behavior models.
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Our Agent-Based Models
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Model builders are largely ignoring group and
crowd behaviors, so we set out to build a
model incorporating social behavior models.
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Today, we will discuss some of the conceptual
and practical issues that must be addressed
when creating models with group behavior
attributes.
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In Practice
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One of
several first
efforts in
physical
social agent
simulation
Several
benchmarks
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In Practice
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Goals for model creation:
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Benchmark current models and actual results.
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Analyze data and create an individual behavior
model.
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Create models with group behavior attributes.
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Apply framework to other situations.
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Benchmarking
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For The Station, we have a detailed dataset
with results that we can compare to output
from models of the disaster.
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There are several models that have been
created about The Station, many with a
different focus, but none with social behavior.
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We have exit data and exit time data. Most
results compare flow rates in aggregate.
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Analysis
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The DRC conducted a study gathering highly
detailed data of victims and evacuees,
including social bonds to other participants.
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Using this data, we were able to approximate
the likelihood of behavior changes based on
different social bonds.
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Individual Behavior Model
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Like many others, we created a model with
individual behavior.
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This became our personal benchmark. If we
can control all of the parameters, we can see
the exact impacts of adding group behavior
traits.
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Our individual behavior model has similar flow
rates to data results and prior models.
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Background
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We began with an issue of geography. We
had to create an accurate environment of The
Station. For now in two dimensions.
•
We then added hazards, reasons to leave the
building.
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We then created our avatars, or agents.
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Agents
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Our avatars have different behaviors based off
of observed traits, such as age, gender,
familiarity with the environment, and other
factors. They are not homogenous.
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Every avatar has a large set of constraints,
such as the inability to move through walls.
•
Avatars also have to navigate in the changing
environment due to other avatars moving.
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Adding Behavior Layers
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Once our individual behavior model was
running, we added other layers:
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Individual layer.
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Social group layer.
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Crowd layer.
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Adding Social and Group
Behaviors
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After creating an individual behavior model,
we began to add group elements.
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Almost immediately, our efficiency plummeted,
avatars stopped exiting, and our results made
little sense when examined by a human.
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What Happened?
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It is easy to forget that computers are not
nearly as smart as you are.
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Every time you add a variable to an agent's
decision tree, you have to account for
interactions.
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Conflicting goals will render a machine's
reasoning system useless unless they are
programmed to perform an action or prefer a
result.
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What Happened?
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The moment the actions of other avatars
matter to an agent, a high number of
interaction variables emerge.
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A simple first-order interaction of 500 agents
creates 125,250 unique relationships.
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Adding additional complications can increase
interactions exponentially.
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Where to begin?
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Before you can create
rules or preferences,
you have to identify
the issues.
The simplest
interactions can be
hard to quantify.
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Building a Model
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There is a multi-step
process of
conceptualization,
implementation, and
robustness testing.
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This process is
inherently
interdisciplinary.
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Complications
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These physical issues
quickly become
difficult computational
and judgement
problems.
Simple group
movements require
constant recalculation
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Complications
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Individual, group,
and crowd
behaviors create
large numbers of
scheduling issues
and interactions that
must be understood.
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Complications
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Reasoning that humans take for granted is
often difficult to implement in computerized
systems.
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It is not feasible to truly simulate humans, so it
becomes important to focus on a few
programmable traits.
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How are social traits programmable?
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Complications
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Here is an example
meant to relay the
concept of object
permanence.
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How do you teach
this to a computer,
or even
communicate it to a
programmer?
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An Interdisciplinary Approach
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Identifying and coding behaviors merges ideas
and techniques from sociology, psychology,
computer science, economics, and statistics
among others.
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The DRC is uniquely positioned to accept
these challenges thanks to strong
interdisciplinary programs and expertise
sharing.
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Where Does ABM Fit In?
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Agents that can interact together are a natural
fit for physical environment simulations. In
theory, they just require one more layer of rules
to create a physical reality.
In practice, group behavior interactions and
physical limitations are going to keep a young
field busy for quite awhile. Humans are more
difficult to simulate than one might imagine.
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How Do We Make Working
Models?
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Hurdles:
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High-Performance Computing: Relationships
tax computing resources. Thankfully, we now
have parallel systems and GPU computing.
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Peer Review: Designers are reluctant to share
code, and even if shared, it is often difficult to
understand. Debugging is very time intensive.
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What attributes to code: Who decides what
aspects of group behavior are important?
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Where Are We Today?
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Currently, I spend most of my time tackling the
third issue; which traits are most important to
include.
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To begin, I created a version of our model with
a simple leader/follower group model.
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These groups are small social dictatorships,
where followers pattern a leader exhibiting an
individual behavior model.
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Where Will We Go?
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Our eventual goal is to implement distributed
social behavior models, approximating
complex social relationships.
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These models will have decentralized and
robust leadership, much like networked
computer systems.
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Beyond groups, we will incorporate crowd
models.
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