evaluating resilience strategies based on an evolutionary multi agent system kazuhiro minami, tomoya...
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Evaluating Resilience Strategies Based on an Evolutionary Multi agent System
Kazuhiro Minami, Tomoya Tanjo, and Hiroshi Maruyama
Institute of Statistical Mathematics, Japan
December 4, 2013
CyberneticsCom 2013
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We sometimes have an unexpected event
• 9.11• Lehman financial shock
in 2008
• 3.11 earthquake and tunami
7/31/2012 Kazuhiro Minami 2
• We cannot completely prevent such disasters• Instead, we should aim to design a system that contains a damage
and is readily recoverable to an acceptable level
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Resilience: Definition
“Capacity of a (social-ecological) system to absorb a spectrum of shocks or perturbations and to sustain and develop its fundamental function, structure, identity, and feedbacks as a result of recovery or reorganization in a new context.”
-- by Buzz Holling (1973)
7/31/2012 3Kazuhiro Minami
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Resilience = Resistance + Recovery
Taoi-cho, Miyagi Pref.http://www.bousaihaku.com/cgi-bin/hp/index2.cgi?ac1=B742&ac2=&ac3=1574&Page=hpd2_view http://fullload.jp/blog/2011/04/post-265.php
+
Logstaff et al., “Building Resilient Communities,” Homeland Security Affairs, Vol VI, No.3, 2010
7/31/2012 Kazuhiro Minami 4
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Goal: How to make our systems more resilient against large unexpected events?
5Financial Systems
Civil Infrastructure
Engineering Systems
Society
Organizations
Natural Disasters
Financial Crisis
New Technologies
Malicious Attackers
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Biological science might be a major source of wisdom for resilience engineering
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Redundancy
Diversity Adaptability
Multiple pathwaysfor metabolism
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Redundancy and diversity are heavily used techniques in Computer Science
• Maintain a backup system in a cloud service– Financial companies was able to continue their
services after 9.11 event– Many web sites maintain multiple copies of the server
• Software diversity makes it difficult for hackers to compromise multiple servers of the same service– Change compiler options or use different algorithms
• Ethernet uses a randomization technique to avoid message collision
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However, applying those techniques to real-world systems is NOT so trivial
• Cost for replication would be high in NON-ICT systems
• Replication sometimes decreases the quality of service– Inconsistency of data– Timely monitoring of a system is more difficult;
thus need to sacrifice the adaptability of a system• Toyota’s supply chain system put precedence
on adaptability over redundancy8
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Multi-agent simulationsbased on a population genetics model
Colony of n agents Each robot has ten binary features (e.g., 2-leg/4-leg, flying/non-flying, …)E.g., <0110111011>
C: “fit” configurationsResource
• Resource Reserve R– Fit robots contribute to build up R – A robot consumes one unit for reconfiguring its one feature
• The colony is resilient if robots can survive a series of changing constraints C1, C2, …, Ct, …
Constraint CA Subset of 2(set of all 1,024
configurations)
A robot is fit if its configuration is in C
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Represent a changing environment as a sequence of dynamic constraints
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Ct
`
Ct+1
Time t Time t+1
fit
fit
fit fit
fit
unfit
unfit
unfit
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Need to pay a cost for adaptation
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Resource
Adaptation10110010 10110011 10110011System
bitstring
Unfit fit
Remove Add
An adaptation in our model is much faster than that in biological systems
Adaptation
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A robot could produce a clone or die
• Make a clone– when the amount of the resource is doubled
• Die – when the resource is used up
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Metrics of resilience in our model
• Redundancy– How much resource does a robot maintain?
• Diversity – Diversity index
• Adaptability– How many bits a robot can flip at a time?
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Multi-agent Simulations
• Define initial parameters– Population size– Bit length of a robot– Size and type of constraints– Initial amount of each robot’s resource– Initial diversity index– Adaptation strategy
• Random or intelligent• #flips at a time
• Run the system at 100 time steps• Examine how a population size, the diversity index vary
over time14
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Diversity at the beginning helps a population survive longer
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Parameter Value
Initial population size
100
Agent bit length
8
Constraint size 26
Constraint transition
continuous
Adaptation strategy
random
Adaptation speed
1
Time
#Age
nts
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Two adaptation Strategies
1. Random strategy (flip one bit randomly)
2. Intelligent strategy (flip one bit to be closer to the constraint)
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10110110
Constraint
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If robots adapt intelligently, the population grows much faster
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Time
#Age
nts
Time
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If agents share the common resource, the sustainability of a system can be greatly improved
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Sharedresource
Individualresources
Sudden changes of the constraint
Sudden changes of the constraint
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Summary
• Explore design space parameterized by three resilience properties based on an evolutionary multi-agent system– Redundancy– Diversity– Adaptability
• Obtain quantitative initial results regarding design strategies for building resilient systems
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Future work: Further possibilities for adaptation strategies
• Local vs Global– Local: Each robot makes its own decision
independently from others– Global: There is a global coordination. Every robot
must follow the order– Mixed
• Complete vs Incomplete knowledge on C– Complete knowledge: max 10 steps to become fit again– Incomplete knowledge: probabilistic (max 1023 steps if
the landscape is stable)
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Backup
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We consider three types of constraints
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1. Disruptive changes: a new constraint Ct is generated randomly at each time t
2. Small changes: a new constraint Ct is generated from Ct-1 by adding a neighbor configuration into Ct-1 or removing a configuration in Ct-1
T = tT = t-1 T = t+1
T = tT = t-1 T = t+1
3. Small changes with continuous topology: Same as case 2, but all configurations in Ct are connected
T = tT = t-1 T = t+1
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Measure diversity considers population abundance of each type
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where N is the size of a population and pi is the size of an individual i
Example 1: if N=5, Pr(`1101’) = 5, then D = 52/52 = 1
Example 2: if N=5, size(`1101’) = 3, and size(`1111’) = 2, then D = 52/32+22 = 25/13 = 1.92