Transcript DistributedIntelligenceALife

Multiagent systems and
Distributed Artificial
Intelligence
Agent?(智能体)
Agent:
Intelligent Object
Intelligent System with Only one
agent
A problem solving system by A
algorithm or A* algorithm
Multiagent system
Intelligent
System with two or
more agents—Multiagent system
Game Playing System by alphabeta procedure
Why Multi-agent system?
Difference
between systems with
one agent and multi-agents?
See an example.
An example: Boid
Who
designs and controls the
behavior of Bird flocks, Fish schools?
See a computer model for
computer simulation of the behavior
of bird flocks, fish schools.
3 rules: Separation

Separation:
steer to
avoid
crowding
local
flockmates
3 rules: Alignment
Alignment:
steer towards
the average
heading of
local
flockmates
3 rules: Cohesion
Cohesion:
steer to move
toward the
average
position of
local
flockmates
Neighborhood around an
agent
Every
agent reacts
only to flockmates
within a certain small
neighborhood around
itself.
The neighborhood is
characterized by a
distance and an angle,
Neighborhood
The
neighborhood is characterized by a
distance (measured from the center of the boid)
and an angle, measured from the boid's direction
of flight.
Flockmates outside this local neighborhood are
ignored.
The neighborhood could be considered a model
of limited perception (as by fish in murky water)
Computer Simulation to Boids

three dimensional computational
geometry of the sort normally used in
computer animation or computer aided
design.
See a demo by Java.
Sorry. Can not download it.
obstacle avoidance
Obstacle
avoidance
allowed the boids
to fly through
simulated
environments
while dodging
static objects.
Demo available?
See
a demo
No Sorry here. 
What can we get from the example?
No
Central controller.
Every agent: its behavior and the
relationship to environments
Emergence(突现，涌现)
More examples of emergence.
History of Multiagent systems
About
late 1970s
Distributed Artificial Intelligence (DAI) evolved
and diversified rapidly.
Research and application field. It brings
together and draws on results, concepts, and
idea from many disciplines: AI, computer science,
sociology, economics, organization and
management science, and philosophy.
Definition:DAI
DAI
is the study, construction, and application
of multiagent systems, that is, systems in which
several interacting, intelligent agents pursue
some set of goals or perform some set of tasks.
An agent is a computational entity such as a
software program or a robot that can be viewed
as perceiving and acting upon its environment
and that is autonomous in that its behavior at
least partially depends on its own experience.
agent
An
agent can be affected in its activities by
other agents.
Agents try to combine their efforts to
accomplish as a group what the individuals
cannot in the case of cooperation.
Agents try to get what only some of them can
have in the case of competition.
Why multiagent system?-1
Modern
computing platforms and information
environments are distributed, large, open, and
heterogeneous.
These often exceed the level of conventional,
centralized computing because they require
processing of huge amounts of data, or of data
that arises at geographically distinct locations.
Why multiagent system?-2
They
have the capacity to play an important
role in developing and analyzing models and
theories of interactivity in human societies, and
solving problems which it is difficult to solve in
conventional method.
Many interactive processes among humans are
still poorly understood, although they are an
integreted part of our everyday life.(There are
many things we do not know and we want to
know related to multiagent systems)
Major characteristics of
multiagent systems
Each
agent has just incomplete
information and is restricted in its
capabilities.
System control is distributed;
Data is decentralized; and
Computation is asynchronous.
Some attributes of multiagent
systems - 1
attribute
agents Number
Uniformity
Goals
Abilities ( sensors,
effectors, cognition)
range
From two upward
Homogeneous…
heterogeneous.
Contradicting …
complementary
Simple … advanced
Some attributes of multiagent
systems - 2
attribute
Interac Frequency
-tion
Pattern (flow of
data and control)
Variability
Perpose
range
Low … high
Decentralized …
hierarchical
Fixed … changeable
Competitive…
cooperative
Some attributes of multiagent
systems - 3
attribute
Enviro
nment
range
Forseeable …
unforseeable
Accessibility and Unlimited … limited
knowability
Dynamics
Fixed … variable
Predictability
Diversity
Poor … rich
Availability of
resources
Restricted … ample
Difference between traditional AI
and DAI-1
Traditional AI
Concentrates “Intelligent
on agents as stand-alone
systems”,
Concentrates a property of
on
systems that
Intelligence
act in
as
isolation.
DAI
“Intelligent
connected
systems”,
a property of
systems that
interact.
Difference between traditional AI
and DAI-2
Traditional AI
Concentrates Cognitive
on
processes
within
individuals
Considers
Internal
systems
reasoning
and control.
DAI
Social processes
in groups of
individuals
Reasoning and
control is
distributed
Difference between traditional AI
and DAI-3
uses
Traditional AI
Psychology
and
behaviorism
for ideas and
inspiration.
DAI
Sociology and
economics
Reasons to study multiagent
systems
 Technological
and application needs:
Offer a promising and innovative way to
understand, manage, and use
distributed, large-scale, dynamic, open,
and heterogeneous computing and
information systems.
 Natural view of intelligent systems
Another example: Floys
 flocking Artificial
creatures.
 with the social tendency to stick
together
Two behavior rules
1.
2.
A rule specifying how to relate to one's
own kind.
A rule specifying how to relate to
strangers
How to relate to one's
own kind
 Identify
two members of your flock that
are near to you and try to stay close to
them, but not too close.
How to relate to
strangers
 If
you are in your territory:
When you spot a stranger go after him,
if you are close enough - attack
 If you are not in your territory:
If local Floys chase you - run away.
Rules of Evolution-1
 eFloys
evolve sexually, where each
eFloy is the descendent of two parents.
 Mother and father are selected
according to the mechanism of 'Survival
of the Fittest by Unnatural Selection'.
Rules of Evolution-2
 Fitness
is defined by two attributes,
energy and safety.
 If you are an eFloy, you can gain or lose
these during your lifetime, and the more
you have, the fitter you are
What influences fitness?-1
 Food
is energy: each time you bite a
stranger, your energy is increased.
Your best option is to reach the stranger
first, and eat him all by yourself.
 If you are a stranger, each time you are
bitten, your energy decreases.
When your energy ends, you die.
What influences fitness?-2
 If
you move fast, your energy decreases.
The faster you move, the more energy
you lose.
 If you are close to your neighbors, your
safety increases.
The closer you are to your neighbors,
the more safety points you get
A demo.
 Wait
please.
English Books:

Artificial Intelligence: A new Synthesis, Nils J,
Nilsson, 机械工业出版社，1999，9北京
 Multiagent Systems: A modern approach to
Distributed Artificial Intelligence, Edited by
Gerhard Weiss, The MIT Press, Cambridge,
Massachusetts, London, English.2000
A demo.
Bigeye.au.tsinghua.edu.cn
人工生命/其它媒体/boids/