In the fall of 2013 I did an undergraduate research assistantship with professors Robin Cohen and Thomas Tran on the topic of earning trust in multi-agent systems. A multi-agent system consists of many interacting autonomous agents in an environment. Such systems are common in the real world: traffic, marketplaces, and computer networks are all examples. The study of systems in which many independent agents act to further their own goals helps us understand and improve these real-world applications.
Trust
is
an
important
concept
in
multi-agent
systems.
Trust
is
an
expectation
of
how
other
agents
will
behave,
and
establishing
trustworthiness
allows
an
agent
to
make
better
decisions
with
less
risk.
In
the
paper
A
Comprehensive
Approach
to
Trust
Management,
Sandip
Sen
discusses
the
notion
of
trust
in
multi-agent
systems
and
notes
that
although
the
problem
of
evaluating
the
trustworthiness
of
others
has
been
well
studied,
there
has
been
less
interest
in
the
complementary
problem
of
earning
trust.
My
research
was
based
on
this
paper
and
Sen's
call
for
study
into
trust
establishment,
among
other
components
of
a
proposed
comprehensive
trust
management
scheme.
My
research
involved
simulating
and
studying
trust
establishment
in
the
context
of
an
electronic
marketplace.
The
simulated
environment
consisted
of
buyers
(trustors)
and
sellers
(trustees).
The
seller
provides
a
service
at
some
price
and
quality.
If
the
buyer
is
satisfied
with
the
price
and
quality
then
its
trust
in
the
seller
increases,
otherwise
it
decreases.
I
implemented
an
exploratory
simulation
filled
with
several
categories
of
buyers
and
sellers.
The
purpose
of
this
simulation
was
to
provide
a
baseline
understanding
of
some
simple
trustee
behaviours
and
to
evaluate
the
effectiveness
of
the
simulation
itself.
The
exploratory
simulation
contained
a
population
of
three
types
of
buyers:
price-sensitive,
balanced,
and
quality-sensitive;
indicating
the
relative
value
of
low
price
vs.
high
quality
transactions.
The
seller
population
consisted
of
profit-maximizing
sellers
that
always
offered
the
same
price
and
quality,
randomized
sellers
that
randomly
picked
some
price
and
quality
at
fixed
profit,
and
classifying
sellers
that
attempted
to
predict
the
buyer
category
then
made
a
corresponding
offer.
The
simulation
results
demonstrated
that
the
sellers
which
predicted
the
buyer's
desires
were
more
effective
at
earning
trust
than
prot-maximizing
or
randomized
sellers.
However,
the
environment
proved
too
simple
to
evaluate
less
trivial
trustee
strategies.
The
sellers
were
too
easy
to
predict
and
consistently
satisfy.
My
work
resulted
in
software
for
simulating
an
electronic
marketplace
environment
with
a
variety
of
buyer
and
seller
agents.
Prediction
of
trustor
desires
was
shown
to
be
a
promising
strategy
for
earning
trust.
Finally
a
list
of
recommendations
was
created
for
improving
the
sellers
and
the
simulation
environment
to
permit
testing
of
more
interesting
trust-earning
behaviours.