1. PhD thesis of University Pierre and Marie Curie, Paris, France
Using agent-based models and machine learning
to enhance spatial decision support systems
Application to resource allocation in situations of urban catastrophes
Defended by CHU Thanh-Quang, the 1st July 2011
Supervisor:
M. Alexis DROGOUL, DR, MSI/IRD, UMI 209 UMMISCO
Co-supervisor:
M. Alain BOUCHER, Prof. AUF, MSI/IRD UMI 209 UMMISCO
Reviewers:
M. Nicolas BREDECHE, MdC HDR, LRI, Université Paris-Sud
M. Bernard PAVARD, Prof., IRIT, Université Paul Sabatier, Toulouse
Examinators:
M. NGUYEN Hong Phuong, Prof., VAST, Hanoi, Vietnam
Mme. Julie DUGDALE, MdC, LIG, Université Pierre Mendès-France
M. Christophe GONZALES, Prof., LIP6, UPMC, Paris, France
1

2. Outline
• Context: Spatial Decision Support System (SDSS)
for resource allocation in emergency response
• Proposal:
• ABM&GIS: Agent-Based Modeling and Geographic
Information System to build the underlying models of SDSS,
• PD: Participatory Design to involve users in the design
process and to enhance the realism of the models,
• ML: Machine Learning algorithms to automate the
extraction of knowledge from stakeholders
• Experiments and results
• Conclusion and prospects
2

3. Disasters
• Natural disasters
• Earthquake
• Tsunami
• Flooding, etc. Natural disasters in Asia (1980 - 2010)
•
No of events: 3,341
Causing huge loss of human life and No of people killed: 1,144,006
property Average killed per year: 39,448
• Cities are especially vulnerable to No of people affected: 4,742,092,443
disasters: Average affected per
year:
163,520,429
•
Ecomomic Damage 673,457,207
Density of population, buildings and (US$ X 1,000):
Ecomomic Damage per
infrastructure year (US$ X 1,000):
23,222,662
http://www.preventionweb.net/ 3

4. Emergency response &
resource allocation
Loss
• Emergency response [CPC, 07]:
• Reducing life-threatening conditions Response effectiveness
• Providing life-sustaining aid
• Stopping additional damage to property
• Resource allocation (particularly important in
urban areas):
• Where and when do rescue resources need to be
allocated?
• How to organize and coordinate these allocations?
4

5. Spatial decision support
systems (SDSSs)
pointing operations, a wireless connection is immediately in real space.
• Decision support systems aim at:
• A multiagent-based simulation with a large number of
supporting decisions of stakeholdersin was performed
parallel with the experiment in real space. See-through
GPS
• head-mounted displays are not suitablesolve problems
training stakeholders to for presenting the
simulation of augmented experiments, since it is unsafe to
• mask the views of passengers. As described above, since we
Spatial DSSs involve location in
used mobile phones, small and low-resolution images of
three dimensional virtual spaces are difficult to understand.
decisions [CPC, 07], e.g.:
Instead of displaying visual simulations, the mobile phones
• design evacuation and rescue routes
in this
• allocate evacuees to shelters 2D Virtual Space Outdoor Real Space
Figure 4. Outdoor Experiment
• select optimal locations for rescue
teams
Digital City, from [Ishida et al., 07]
5

6. Literature of SDSSs for
emergency response
• DrillSim [Balasubramanian et al., 06], [Massaguer et al., 06],
• ALADDIN [Adams et al., 08], [Gianni et al., 08],
• DEFACTO [Marecki et al., 05], [Schurr et al., 05],
• Plan-C [Narzisi et al., 07],
• Digital City (JST CREST) [Ishida et al., 07], etc.
• Modeling and Simulation with ABM & GIS are core
techniques to:
•
Camera
model emergency situations
• design response solutions
In summary, an augmented experiment consists of 1)
to represent human 3D Virtual Space Indoor Real Space
Figure 3. Indoor Experiment
Digital City, from [Ishida et al., 07] 6

7. ALADDIN (Autonomous Learning Agents for
Decentralized Data and Information Networks)
[Adams et al., 08], [Gianni et al., 08]
• Evacuating a building on fire
• Improve situational awareness
• data collection
• data fusion
• Improve path planing and
coordination strategy
• auction methods
• coalition methods
• learning in games
7

8. DEFACTO (Demonstrating Effective Flexible Agent
Coordination of Teams through Omnipresence)
[Marecki et al., 05], [Schurr et al., 05]
• Fire evacuation
• Improve situational awareness (a) (b)
• 3D visualization
• human-interaction
• Focus on modeling (c) (d)
• detailed-level of situations
(e) (f)
8

9. Plan-C (Planning with Large Agent-
Networks against Catastrophes)
[Narzisi et al., 07]
• Emergency planning,
• Response planning as a
medical relief operations problem of multi-objectives
• use evolutionary algorithms optimization
9

10. Lack of flexibility and realism
Realism of
Project Application Main limitation Lack of (behavioral realism)
situations
DrillSim Fire evacuation Difficultly generalized Small scale
DEFACTO Fire evacuation Manual modeling 3D with OpenGL Learning from users’ solution
ALADDIN Fire evacuation Poor user-interface Simple GIS
ResQ Freiburg Search&Rescue Lack of reusability Simple GIS Interest on domain knowledge
Medical relief Limited configurability of
PLAN C GIS Interest on domain knowledge
operations agent behavior
Damas Rescue Search&Rescue Lack of flexibility Simple GIS Interest on domain knowledge
Digital City Large-scale evacuation Lack of solution support GIS Learning from users’ solution
• Lack of realism of emergency situations
• Environments are simply represented in small scale
• Lack of realism of rescue activities (i.e. agent behaviors)
• Small interest on domain knowledge to improve response effectiveness
10

11. Proposal
• Problem: Lack of realism of emergency situations
• Step 1: Using ABM&GIS (geospatial data of Hanoi and earthquake
loss estimation of IG-VAST) to build a realistic rescue model
• Problem: Lack of realism of rescue activities
• Step 2: Using Participatory Design to involve practitioners, experts
of emergency to improve agent behaviors
• Problem: The improvement of agent behaviors has to be made
manually and offline by modelers
• Step 3: Using Machine Learning to automate the acquisition of
experts’ knowledge
11

12. Step 1: Building a realistic
rescue model
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@"7)
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System of IG-VAST [Nguyen-Hong, 03]:
• Real GIS data of Hanoi
• Disaster impact data: building damage and
casualties
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)
)
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12
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13. Step 1: Building a realistic
rescue model
!"#$%&'%()*!!(++),"-.+)"-)/#(-0(+)1023)*-!4567)83(),"%%"109:)+!-((9+3"2)+3"1+)1
$"++0'%()1023);<)*9&%=+2)*-!456)(>2(9+0"9?)+")23(-()&-()!(-2&09%=)'(&#20,#%)2309:+)%(
@"7)
• Collect from Earthquake Loss Estimation
System of IG-VAST [Nguyen-Hong, 03]:
• Real GIS data of Hanoi
• Disaster impact data: building damage and
casualties
• Rescue agents: inspired from the agents
found in RobocupRescue simulations !"#$$%&'()*+,-*./01*234*(5*63738$6*9:;<6;%8&*;%*=36;%'*1;&)#;")*(5*>3%(;*?;)@
[www.robocuprescue.org] )
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+!3(@#%(7)E#2)02)0+)+".(2309:)23&2)-(&%%=)092(-(+2+).(?)+")13=)9"2F)
)
)
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09)23()59+202#2()",)4("$3=+0!+G)
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12
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14. Step 1: Building a realistic
rescue model
!"#$%&'%()*!!(++),"-.+)"-)/#(-0(+)1023)*-!4567)83(),"%%"109:)+!-((9+3"2)+3"1+)1
$"++0'%()1023);<)*9&%=+2)*-!456)(>2(9+0"9?)+")23(-()&-()!(-2&09%=)'(&#20,#%)2309:+)%(
@"7)
• Collect from Earthquake Loss Estimation
System of IG-VAST [Nguyen-Hong, 03]:
• Real GIS data of Hanoi
• Disaster impact data: building damage and
casualties
• Rescue agents: inspired from the agents
found in RobocupRescue simulations !"#$$%&'()*+,-*./01*234*(5*63738$6*9:;<6;%8&*;%*=36;%'*1;&)#;")*(5*>3%(;*?;)@
[www.robocuprescue.org] )
*%(>0+)&9@)A3#"9:)$-"$"+(@).()2")@")&%%)230+)1"-B),-".)C-&9!()@#-09:)23()=(&-7)5)1
!"9209#().=)+2#@0(+)09):("%":=)230+)=(&-?)&9@)02)10%%)@($(9@)"9)23()D"%#.()",).=)!%&+
+!3(@#%(7)E#2)02)0+)+".(2309:)23&2)-(&%%=)092(-(+2+).(?)+")13=)9"2F)
• GAMA (GIS and agent-based modeling )
)
platform [Amouroux et al., 07]) is used$%&&"'"(")*!%+!+,),-.!"/).-/&0"1!
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12
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15. Organization of rescue agents
• Rescue agents are
organized in multiple levels
• Agent decision models are
represented as sets of rules
• Agents coordinate by
exchanging messages
13

16. Behaviors of agents dedicated
to resource allocation
• Agent “center” assigns rescue agencies to damaged districts
• Agencies allocate rescuers to damaged wards
Hanoi City Ba-Dinh District
14

17. This model is a foundation
to build the targeted SDSS
15

18. Restrictions of the current
model and proposal
• Restrictions:
• The agent behaviors are not realistic enough
• The simulated rescue activities are not performant
16

19. Restrictions of the current
model and proposal
• Restrictions:
• The agent behaviors are not realistic enough
• The simulated rescue activities are not performant
• Next step of the proposal:
• Make stakeholders (experts) play the role of agents
to control the rescue activities
• Acquire the knowledge of stakeholders to improve
the behavior of agents
16

20. Step 2: improving agents’ behavior
by Participatory Design
)
MADFAM, from [Nguyen-Duc & Drogoul, 07]
Design process of agent-based participatory
simulations, from [Guyot & Honiden, 06] Digital City, from [Ishida et al., 07] 17

21. Applying participatory design
to the rescue model
18

22. A first experiment
• Involving 27 master students of the IFI in a half-day
Number of
• They play simulations to improve the behaviors of
ambulances (i.e. reducing the “number of deaths”)
Improvement
students
• Students: 0 16/27
• execute separately from 5 to 8 playing sessions
2 4/27
• follow the same progression of 4 scenarios
• take 5 minutes of discussion between two playing sessions 3 1/27
• attend a final 30 minutes of debriefing session 4 2/27
• Results:
5 2/27
• 11 students showed real improvements
• they reached the maximal improvement in the first scenario 6 1/27
• No student reached the optimal result (=8) for all four 7 1/27
scenarios
19

23. Requirements
• User-interface must be friendly and interactive
• Scenarios
• must be understandable, realistic, rich, varied
• sound progression from simple to complex ones
• Experimental protocol with well-design
questionnaires (for debriefing sessions)
20

24. Limitations of the current
participatory design process
• A effective model requires:
• a large number of playing
sessions
• the analysis of a large base of
user trace
• Limitations:
• Manual analysis of modelers
takes a lot of time
• Offline change of model lacks an from [Nguyen-Duc & Drogoul, 07]
immediate feedback
21

25. Step 3: automating the acquisition
of experts’ knowledge by ML
I will save victimX,
he’s very close.
• Machine learning
• Automatically extract the behaviors of
users
• Online and interactive learning No, I prefer victimY he’s
in a more critical state
• Immediately improve the behaviors of
agents
• Let agents intelligently negotiate with users
• Help agents learn more quickly the users’
Ok, so the gravity is more
decision-making important than the distance
22

26. Requirements of an online and
interactive learning
• Being effective under constraints of time and
resources
• Being supervised (by the user)
• Being incremental
• Providing visualizable and understandable
"outcomes"
• SVM, KNN, Neural Network, HMM are not suitable
• Decision Tree, Bayesian Network are more suitable
• Supported by an interactive interface and a
language
• to allow negotiations between users and agents
23

27. Learning the behavior of
agents
• Layered learning of Robocup-Soccer [Stone, 98]
• Real-time Belief Space Search (RTBSS) of Damas-Rescue [Paquet, 06]
Visualizable &
Method Effective Supervised Incremental Interactive
Understandable
RTBSS v v x v x
Layered v v x v x
• Limitations of these methods:
• Outcomes are difficultly visualized in a understandable way
• Lack of interaction with stakeholders (i.e. learning without human supervisors)
• Need of large training sets of examples
24

28. My choice: combining decision
tree and utility function
• Binary decision tree [Payne &
Meisel, 77], [Cerny et al., 79]
• Additive utility function
[Keeney & Raiffa, 76]
• to treat categorial data • to treat numerical data
• to solve classification problems • to solve regression problems
• to filter alternatives • to represent preferences
Decision model of agents
An utility function to choose a target district for hospitals
An utility function to choose a target district for police offices
An utility function to choose a target district for fire-stations
Hospital has an UF to choose a target ward for ambulances
Police office has an UF to choose a target ward for police forces
Fire-station has an UF to choose a target ward for firefighters
Each ambulance, firefighter, police force has:
- A decision tree to choose target type
- For each type, an utility function to choose a precise target
25

29. Behavior of an ambulance
Can carry
more
•
No Yes
Ambulance have two questions:
•
Serious victim
Go to an onsite victim for first-aid or
Hospital
carried
take the carried victims to hospital? No Yes
• If the type is determined, which Victim Hospital
precise target will be chosen?
Criteria to choose a victim Min/ Name
• Decision model of ambulance Distance (from ambulance to victim)
Max
(-) C1
contains: Gravity (of victim) (+) C2
•
Distance (from victim) to closest other victim (-) C3
One decision tree to choose a target Number of victims nearby (+) C4
type (victim or hospital) Max gravity of victims nearby (+) C5
• Two utility functions to choose a
F(Vk)
=
∑
wi
*
Cki
target of a specific type
The
vic(m
Vmax
will
be
selected
if:
Vmax
=
ArgMax{F(Vk)}
26

30. Learning decision tree
Can carry I will go to V1 because:
more
No Yes
I can carry more victim
and V1 is close to me
Hospital Victim
27

31. Learning decision tree
Can carry I will go to V1 because:
more
No Yes
I can carry more victim
and V1 is close to me
Hospital Victim
User change decision
You must go to H1 because
Alternatives: {V1, V2, V3, V4, H1, H2}
Decision: H1
you carry a victim in critical state
and H1 has free beds
Reasoning for change
Boolean
function: SeriousVictimCarried
Numerical
criteria: High(freeBedNumber)
27

32. Learning decision tree
Can carry I will go to V1 because:
more
No Yes
I can carry more victim
and V1 is close to me
Hospital
Serious victim
carried
No Yes
Victim Hospital
User change decision
You must go to H1 because
• find the leaf-node corresponding
Alternatives: {V1, V2, V3, V4, H1, H2}
Decision: H1
you carry a victim in critical state
to current context and H1 has free beds
Reasoning for change
• replace the leaf-node by a Boolean
subtree function: SeriousVictimCarried
Numerical
• boolean condition of sub-tree is criteria: High(freeBedNumber)
defined by users
27

33. Learning utility function
Ambulance1 choose a target
I will go to V1 because:
Alternatives: {V1, V2, V3, V4, H1, H2}
s/he is close to me
F(Vk)= -1* distance Decision: {V1}
Reasoning for decision
Boolean
CanCarryMore
function:
Numerical
criteria: Low(distance)
28

34. Learning utility function
Ambulance1 choose a target
I will go to V1 because:
Alternatives: {V1, V2, V3, V4, H1, H2}
s/he is close to me
F(Vk)= -1* distance Decision: {V1}
Reasoning for decision
Boolean
CanCarryMore
function:
Numerical
criteria: Low(distance)
You must go to V2 because:
s/he’s in a more critical state
28

35. Learning utility function
Ambulance1 choose a target
I will go to V1 because:
Alternatives: {V1, V2, V3, V4, H1, H2}
s/he is close to me
F(Vk)= -1* distance Decision: {V1}
Reasoning for decision
gravity Boolean
function:
CanCarryMore
Numerical
criteria: Low(distance)
• Add new numerical criteria
(identified by user) to the
function You must go to V2 because:
s/he’s in a more critical state
28

36. Learning utility function
Ambulance1 choose a target
I will go to V1 because:
Alternatives: {V1, V2, V3, V4, H1, H2}
s/he is close to me
F(Vk)= -0.4*
-1* distance Decision: {V1}
Reasoning for decision
+0.6* gravity Boolean
function:
CanCarryMore
Numerical
criteria: Low(distance)
• Add new numerical criteria
(identified by user) to the
function You must go to V2 because:
s/he’s in a more critical state
• Update criteria’ weights by
solving “inequalities
system” (Simplex method for
linear programming
[Vanderbei, 08])
28

37. Experiments
• Test with an "Oracle" to validate:
• Learning decision tree
• Learning utility function
• Real-life test involves PhD students of MSI
• Ten scenarios to improve the behaviors of ambulances
• Improvement means the reduction in “number of deaths”
• Evaluation by the best result with all participants
29

38. Validation of learning
decision tree
Have onsite victim Victim carried
No Yes No Yes
Victim Hospital
Victim carried Victim carried
No Yes No Yes
Wait Hospital Victim Can not
carry more
No Yes
Tree of the Oracle Tree learnt by ambulance
Serious victim
Hospital
carried
No Yes
Victim Hospital
30

39. Validation of learning
decision tree
Situation1
Have onsite victim Victim carried
No Yes No Yes
Have not onsite Hospital
Victim carried Victim carried
victim
No Yes No Yes No Yes
Wait Hospital Victim Can not Victim Wait
carry more
No Yes
Tree of the Oracle Tree learnt by ambulance
Serious victim
Hospital
carried
No Yes
Victim Hospital
30

40. Validation of learning
decision tree
Have onsite victim Victim carried
No Yes Situation 2 No Yes
Have not onsite Have onsite victim
Victim carried Victim carried
victim
No Yes No Yes No Yes No Yes
Victim
Wait Hospital Victim Can not Victim Wait Hospital
carry more
No Yes
Tree of the Oracle Tree learnt by ambulance
Serious victim
Hospital
carried
No Yes
Victim Hospital
30

41. Validation of learning
decision tree
Have onsite victim Victim carried
No Yes No Yes
Have not onsite Have onsite victim
Victim carried Victim carried
victim
No Yes No Yes No Yes No Yes
Can not Serious victim
Wait Hospital Victim Victim Wait Hospital
carry more carried
Tree of the Oracle
No Yes
Situation 3 Tree learnt by ambulance
No Yes
Serious victim
Victim Hospital
Hospital
carried
No Yes
Victim Hospital
30

42. Validation of learning
decision tree
Have onsite victim Victim carried
No Yes No Yes
Have not onsite Have onsite victim
Victim carried Victim carried
victim
No Yes No Yes No Yes No Yes
Can not Serious victim
Wait Hospital Victim Victim Wait Hospital
carry more carried
No Yes No Yes
Tree of the Oracle Tree learnt by ambulance
Serious victim Can not Hospital
Hospital carry more
carried
No Yes No Yes
Victim Hospital
Victim Hospital
• The same set of rules generated from the two trees
30

43. Validation of learning
utility function
Difference
Error in the utility function of agents
ai
Difference(kmin) = ∑| – kmin* | wi
with kmin= ArgMin{Difference(k)}
First ambulance
Second ambulance
Where: ai are coefficients of the
function of Oracle: Fo(Vk) = ∑ ai * Cki
wi are coefficients of the function of
agent: Fa(Vk) = ∑ wi * Cki
Time (in simulation steps)
• The function of agent converges towards UF of the Oracle
31

44. Real-life test with users
Victim carried
No Yes
Victim Hospital
F(Vk)= -1* distance
F(Hk)= -1* distance
32

45. Real-life test with users
Victim carried
Scenario1 No Yes
Reduce 2 deaths Have onsite victim
Hospital
No Yes
F(Vk)= -1*
-0.4* distance
Wait Victim
0.6* gravity
F(Hk)= -1* distance
32

46. Real-life test with users
Victim carried
Scenario 2 No Yes
Reduce 1 death Have onsite victim Have onsite victim
No Yes No Yes
F(Vk)= -1*
-0.2* distance
-0.4*
Victim Can not
Wait Hospital
carry more
0.7*
0.6* gravity No Yes
-0.1* distance to closest other victim Victim Hospital
F(Hk)= -1* distance
32

47. Real-life test with users
Victim carried
Scenario 3 No Yes
Reduce 3 deaths Have onsite victim Have onsite victim
No Yes No Yes
F(Vk)= -0.1* distance
-1*
-0.2*
-0.4*
Victim Can not
Wait Hospital
carry more
0.7*
0.5*
0.6* gravity No Yes
-0.1* distance to closest other victim Have
Hospital
reachable
0.3* number of victims nearby No
victims
Yes
Hospital Victim
F(Hk)= -1* distance
32

48. Real-life test with users
Victim carried
Scenario 4 No Yes
Reduce 2 deaths Have onsite victim Have onsite victim
No Yes No Yes
F(Vk)= -0.1* distance
-1*
-0.2*
-0.4*
Victim Can not
Wait Hospital
carry more
0.67*
0.7*
0.5*
0.6* gravity No Yes
-0.1*
-0.03* distance to closest other victim Have
Hospital
reachable
0.13* number of victims nearby
0.3* victims
No Yes
0.07* distance to closest ambulance Hospital Have reachable
savable victims
No Yes
F(Hk)= -0.9* distance
-1*
0.1* number of free beds
Victim
Hospital 32

49. Victim carried
The final decision
No Yes
model of ambulances
Have onsite victim Have onsite victim
No Yes No Yes
Can not
Wait Victim Hospital
carry more
No Yes
Criteria to choose a victim Min/ Weight
Max Have
Hospital
reachable
Gravity (of victim) (+) 0.5459 No
victims
Yes
Number of victims nearby (+) 0.1345
Hospital
Distance (from ambulance to victim) (-) 0.1034 Have reachable
savable victims
Distance (from victim) to closest other ambulance (+) 0.0725 No Yes
Max gravity of victims nearby (+) 0.0665
Have reachable
Distance (from victim) to closest other victim (-) 0.0635 Hospital savable victims with
safe path
Distance (from victim) to closest hospital (-) 0.0137 No Yes
Criteria to choose a hospital Min/ Weight
Max Hospital Serious victim
carried
Distance (from ambulance to hospital) (-) 0.4106 No Yes
Number of free beds (of hospital) (+) 0.2477
Number of victims nearby (+) 0.1267 Have serious
Victim (reachable, reachable savable
Distance (from hospital) to closest ambulance (+) 0.0975 savable, safe path) victims with safe
path
Max gravity of victims nearby (+) 0.0674 No Yes
Distance (from hospital) to closest other victim (-) 0.0365 Victim (serious,
Hospital reachable, savable,
Distance (from hospital) to closest other hospital (-) 0.0136 safe path)
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50. Results for all ten scenarios
Parameters
Improvement
Scenario Hospital Ambulance Victim Ambulance (in reducing the
number number number capacity number of deaths)
1 1 1 6 1 2
2 1 1 8 2 1
3 1 1 18 3 3
4 2 2 33 3 2
5 2 4 42 4 4
6 2 4 54 5 3
7 5 15 67 6 6
8 5 15 86 8 8
9 6 24 128 10 7
10 6 24 242 10 12
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51. Conclusions
• Concerning the design of a SDSS, my proposal:
• automatically acquire part of the stakeholders’ knowledge
• enhance the realism and the effectiveness of system
• reduce the number of tests and focus on a few prototypes
• The outcomes of this PhD thesis can be easily generalized to
support the modeling of different socio-environmental systems:
• My proposal of PD augmented with ML can be used in any applicative context
• I designed the interactive interface, such that it can be reused in any context of
decision-making
• I designed the combination of DT and UF in order to be adaptable to model any
agent behaviors
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52. Prospects
• Improving user/agent interaction with a more friendly interface and a
more natural language
• Currently, learning process requires a lot of efforts from the users when playing with the
agents
• Improving learning algorithm to support fault-tolerance
• Currently, learning algorithm requires a high-level consistency in decisions of users
• Designing experiments with real practitioners and experts of emergency
• 2006: meeting with the Population Committee of Vietnam
• 2007: meeting with the Vietnam Search and Rescue Committee (VINASARCOM)
• ...
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53. Thanks and Questions?
• Step 1: Using ABM&GIS (geospatial data of Badinh and earthquake
loss estimation of IG-VAST) to build a realistic rescue model
• to solve the lack of realism of emergency situations
• Step 2: Using Participatory Design to improve agent behaviors
• to solve the lack of realism of rescue activities
• Step 3: Using online interactive learning (DT and UF) to automate
the acquisition of experts’ knowledge
• to tackle the manual, offline improvement of agent behaviors, which is done by
modelers
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