Transcript shabtay06.ppt

Amit Shabtay
Zinovi Rabinovich
Supervised by: Jeffrey S. Rosenschein
In collaboration with:
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Parasites- Paradigm Motivation
• Our paradigm employs a special kind of agent (called
“Behaviosite”) that manipulates the behavior of other
agents.
• Affecting the behavior of several agents in a distributed
manner will facilitate altered performance of the entire
system.
• By definition, the behaviosite is not necessary for the
normal conduct of the system, thus termed a kind of
“parasite”.
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Lecture Layout
1.
Parasites in biological context and in computer
science
2.
Formalization of the Behaviosite Paradigm
3.
Presenting the paradigm in the
El Farol problem and Behaviosite
paradigm and floys
1.
Discussion and Future work.
Scanning EM of
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African Trypanosomes
Nature 414 ( (2001)
Parasite
Concept in Biology
& CS
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1 Parasite Concept
Parasite In Nature
• A parasite is an organism that lives inside or outside
the living tissue of a host organism at the expense of
it.
• The biological interaction between the host and the
parasite is called parasitism. The parasite usually
harms the host, but not necessarily.
• It can have a complex life cycle.
• They may help the host, as in the
case of bees.
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1 Parasite Concept
Parasites in Computer Science
• Parasites appear in three forms in CS:
– As an observed phenomena in evolution
• Tierra Virtual World (Thomas Ray 1992)
– As helpers in genetic algorithms using co-evolution.
• Co-evolving parasites improving the sorting problem (Hillis
WD. 1990 and many more examples)
– As malware in the electronic world.
• Parasite is a known concept: Computer viruses, Worms,
Trojan Horses as parasites (R.J Bagnall).
• Viruses today are more focused and interested in quietly
stealing our data and control over the computer than just
crashing it (Meet the Sonic Worm, Zone Alarm 2000)
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Behaviosite
Formalization
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2 Behaviosite Formalization
Behaviosites Formalization I
• Behaviosites act as a society of special agents within a
system composed a society of agents and environment
A distributed solution to issues raised in a distributed
environment
• The behaviosite is an additional property/information
added to the system (and not the agent).
• Behaviosites must be beneficial to the system in some
sense, not necessarily in regards to the initial purpose of
the system.
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2 Behaviosite Formalization
Behaviosites Formalization II
• Basically, behaviosites are designed in two levels:
infection strategy and manipulation strategy.
– Infection strategy: finding the best host to infect at
the current time step and how to move between
agents.
– Manipulation strategy: possible options for the
behaviosite to manipulate the behavior of the
infected agent.
One may also include “behaviosite ecology”- where
do they come from?
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2 Behaviosite Formalization
Behaviosites Formalization III
• Benefiting the system
• Deep system knowledge
• Use existing capabilities
• Small numbers
• Mobility between hosts
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2 Behaviosite Formalization
External vs. Internal Behaviosites
• Behaviosites can alter the input or output of the agent
vis-á-vis the environment (external behaviosites) or
using an internal hook (internal behaviosites).
External
Internal
• An agent designer can have an incentive to create such
a hook, if it is required of him, or if it can be guarantied
that the overall performance of the agent will not
degrade because of it.
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2 Behaviosite Formalization
Behaviosites Optional Traits
• Hidden vs. Apparent infection.
There are some settings in which the
sheer knowledge that an agent is
infected, is sufficient for the behavior
manipulation.
• Behaviosite communication.
Behaviosites may communicate within
an infected host or across hosts to form
some kind of an inner network.
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The El Farol
Problem
El Farol
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3 The El Farol Problem
The El Farol Problem
• The El Farol problem is an example of a distributed
system (Brian Arthur 1994), first suggested as a Congestion
Problem in economics.
• All agents want to go to a bar called “El Farol”, but it
has a limited (comfortable) capacity.
0.5

Util (ag[i ])  0
0.5

Attended and undercrowded
Did not attend
Attended and overcrowded
• With no option for communication or collusion, an
agent must learn the behavior of other agents en-masse,
in order to reach a decision.
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3 The El Farol Problem
Parasitized El Farol Problem
• The system reaches an equilibrium around the capacity,
where every agent has a unique, simple learning
decision algorithm.
• However, personal and social utilities are suboptimal.
• We show that using behaviosites with simple infection
and manipulation strategies, both utility and social
fairness improve, overcoming learning ability of agents.
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3 The El Farol Problem
Parasitized El Farol Problem
• Infection strategy: infect all, infect attending, infect when
overcrowded.
• Manipulation strategy: lower the believed capacity of the
infected agent (50 40, 60 40, 80 60).
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3 The El Farol Problem
Mean Attendance and Social Utility
• Infect all had the most severe effect on attendance,
while infect when overcrowded had the least effect.
•Attendance for capacity of 60
•Utility for infect attending:
80
Overcrowded
60
Attending
50
All
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3 The El Farol Problem
Simulation Social Fairness
• Formula for social fairness according to attendance:
1
PersonalAtt.SD[t ]
1
 MeanAtt.[t ]
# trials ttrials
• For capacity of 60:
Attending
Overcrowded
All
50%
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Controlling a
Swarm of Floys
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3 The Floys Problem
Controlling a Swarm of Floys
• Controlling a swarm has received much attention (UGV,
computer graphics)
• Reynolds (1987) showed that it is possible to create a
swarm behavior using three rules:
– Separation
– Cohesion
– Alingment
Rome
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3 The Floys Problem
A Swarm of Floys
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3 The Floys Problem
Controlling a Swarm of Floys
• Infection Strategy:
Jump to an uninfected floy within sight.
• Manipulation Strategy:
Make the floy move two “turn units” toward the
goal point.
If in vicinity of goal, switch to next goal.
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3 The Floys Problem
Tasks for Behaviosites
• Keep swarm in one place
• Move swarm between check points (rectangle, circle)
• Move between equilibrium points
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3 The Floys Problem
Parasitized Swarm Simulation
• It takes only 5% infection rate for achieving control
Number of drawn rectangles
Distance from true path
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3 The Floys Problem
Parasitized Swarm Simulation
• Can create a movement of the swarm along a path
• Robust to malfunctioning, ill-functioning, or destroyed behaviosites
• Behaviosites are endemic, thus protected by the swarm from
external harm
• Few can control many
• Behaviosites can move to the most effective position at a given time
without disturbing the swarm (unlike herdsman).
• All tasks were accomplished using only one infection and
manipulation strategy, and one type of simple behaviosite.
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Discussion &
Future Work
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4 Discussion & Future work
Discussion
• The core of the Behaviosite Paradigm is creating a
distributed behavioral changes in a small number of
agents using infection and manipulation strategies,
to achieve a global effect.
• We described the Parasitized El Farol Problem, and a
method for controlling a swarm
• Behaviosites are not a type of “lie” in the system,
since they cannot be disregarded or overcome.
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4 Discussion & Future work
Future Work- Appetisers
• Use behaviosites as an information
propagation mechanism in array of
sensors
• Use behaviosites in a congestion
problem like traffic
routing (packet routing)
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4 Discussion & Future work
Future Work- Appetisers
• Turn floys to boids and deal
with obstacle avoidance
• Automatic story
generation
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4 Discussion & Future work
Future Work- Ant Foraging
• Using behaviosites in a colony of ants for foraging when
food sources suddenly appear
B
Nest
Food source
A
Infection Strategy?
Manipulation Strategy?
Ecology?
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4 Discussion & Future work
Future Work- Ant Foraging
• Using behaviosites in a colony of ants for foraging
mutually exclusive appearing/disappearing food
sources
Nest
A
B
Infection Strategy?
Food source
Manipulation Strategy?
Ecology?
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4 Discussion & Future work
Future Work- Ant Foraging
• Final stage- food sources appear and disappear
randomly.
Infection Strategy?
Manipulation Strategy?
Ecology?
Combination of Behaviosites?
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