Using agent-based models and machine learning to enhance spatial decision support systems
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Transcript of Using agent-based models and machine learning to enhance spatial decision support systems
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 UMMISCOCo-supervisor: M. Alain BOUCHER, Prof. AUF, MSI/IRD UMI 209 UMMISCOReviewers: M. Nicolas BREDECHE, MdC HDR, LRI, Université Paris-Sud M. Bernard PAVARD, Prof., IRIT, Université Paul Sabatier, ToulouseExaminators: 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
PhD thesis of University Pierre and Marie Curie, Paris, France
1
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 prospects2
Disasters
• Natural disasters
• Earthquake
• Tsunami
• Flooding, etc.
• Causing huge loss of human life and property
• Cities are especially vulnerable to disasters:
• Density of population, buildings and infrastructure
No of events: 3,341
No of people killed: 1,144,006
Average killed per year: 39,448
No of people affected: 4,742,092,443
Average affected per year:
163,520,429
Ecomomic Damage (US$ X 1,000):
673,457,207
Ecomomic Damage per year (US$ X 1,000):
23,222,662
Natural disasters in Asia (1980 - 2010)
http://www.preventionweb.net/3
Emergency response & resource allocation
4
• Emergency response [CPC, 07]:
• Reducing life-threatening conditions
• 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?
Loss
Response effectiveness
Spatial decision support systems (SDSSs)
• Decision support systems aim at:
• supporting decisions of stakeholders
• training stakeholders to solve problems
• Spatial DSSs involve location in decisions [CPC, 07], e.g.:
• design evacuation and rescue routes
• allocate evacuees to shelters
• select optimal locations for rescue teams
5
pointing operations, a wireless connection is immediately
A multiagent-based simulation with a large number of was performed in
parallel with the experiment in real space. See-through head-mounted displays are not suitable for presenting the simulation of augmented experiments, since it is unsafe to mask the views of passengers. As described above, since we used mobile phones, small and low-resolution images of three dimensional virtual spaces are difficult to understand. Instead of displaying visual simulations, the mobile phones
in this
in real space.
Figure 4. Outdoor Experiment
GPS
Outdoor Real Space2D Virtual Space
GPS
Outdoor Real Space2D Virtual Space
Digital City, from [Ishida et al., 07]
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:
• model emergency situations
• design response solutions
6
In summary, an augmented experiment consists of 1) to represent human
Figure 3. Indoor Experiment
Indoor Real Space3D Virtual Space
Camera
Indoor Real Space3D Virtual Space
Camera
Digital City, from [Ishida et al., 07]
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
DEFACTO (Demonstrating Effective Flexible Agent Coordination of Teams through Omnipresence)
[Marecki et al., 05], [Schurr et al., 05]
• Fire evacuation
• Improve situational awareness
• 3D visualization
• human-interaction
• Focus on modeling
• detailed-level of situations
8
(a) (b)
(c) (d)
(e) (f)
Plan-C (Planning with Large Agent-Networks against Catastrophes)
[Narzisi et al., 07]
• Emergency planning, medical relief operations
• use evolutionary algorithms
• Response planning as a problem of multi-objectives optimization
9
Project Application Main limitationRealism of situations Lack of (behavioral realism)
DrillSim
DEFACTO
ALADDIN
ResQ Freiburg
PLAN C
Damas Rescue
Digital City
Fire evacuation Difficultly generalized Small scale
Fire evacuation Manual modeling 3D with OpenGL Learning from users’ solution
Fire evacuation Poor user-interface Simple GIS
Search&Rescue Lack of reusability Simple GIS Interest on domain knowledge
Medical relief operations
Limited configurability of agent behavior
GIS Interest on domain knowledge
Search&Rescue Lack of flexibility Simple GIS Interest on domain knowledge
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
Lack of flexibility and realism
Proposal
11
• 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
Step 1: Building a realistic rescue model
12
• Collect from Earthquake Loss Estimation System of IG-VAST [Nguyen-Hong, 03]:
• Real GIS data of Hanoi
• Disaster impact data: building damage and casualties
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Step 1: Building a realistic rescue model
12
• 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 [www.robocuprescue.org]
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Step 1: Building a realistic rescue model
12
• 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 [www.robocuprescue.org]
• GAMA (GIS and agent-based modeling platform [Amouroux et al., 07]) is used to build model
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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
14
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
This model is a foundation to build the targeted SDSS
15
Restrictions of the current model and proposal
• Restrictions:
• The agent behaviors are not realistic enough
• The simulated rescue activities are not performant
16
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
Step 2: improving agents’ behavior by Participatory Design
17
Design process of agent-based participatory simulations, from [Guyot & Honiden, 06]
)
MADFAM, from [Nguyen-Duc & Drogoul, 07]
Digital City, from [Ishida et al., 07]
Applying participatory design to the rescue model
18
A first experiment
• Involving 27 master students of the IFI in a half-day
• They play simulations to improve the behaviors of ambulances (i.e. reducing the “number of deaths”)
• Students:
• execute separately from 5 to 8 playing sessions
• follow the same progression of 4 scenarios
• take 5 minutes of discussion between two playing sessions
• attend a final 30 minutes of debriefing session
• Results:
• 11 students showed real improvements
• they reached the maximal improvement in the first scenario
• No student reached the optimal result (=8) for all four scenarios
ImprovementNumber of
students
0 16/27
2 4/27
3 1/27
4 2/27
5 2/27
6 1/27
7 1/2719
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
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 immediate feedback
21
from [Nguyen-Duc & Drogoul, 07]
Step 3: automating the acquisition of experts’ knowledge by ML
• Machine learning
• Automatically extract the behaviors of users
• Online and interactive learning
• Immediately improve the behaviors of agents
• Let agents intelligently negotiate with users
• Help agents learn more quickly the users’ decision-making
I will save victimX, he’s very close.
No, I prefer victimY he’s in a more critical state
Ok, so the gravity is more important than the distance
22
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 agents23
Learning the behavior of agents
• 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
• Layered learning of Robocup-Soccer [Stone, 98]
• Real-time Belief Space Search (RTBSS) of Damas-Rescue [Paquet, 06]
Method Effective SupervisedVisualizable &
Understandable Incremental Interactive
RTBSS
Layered
v v x v x
v v x v x
My choice: combining decision tree and utility function
• Binary decision tree [Payne & Meisel, 77], [Cerny et al., 79]
• to treat categorial data
• to solve classification problems
• to filter alternatives
• Additive utility function [Keeney & Raiffa, 76]
• to treat numerical data
• to solve regression problems
• to represent preferences
25
Decision model of agents
An utility function to choose a target district for hospitalsAn utility function to choose a target district for police officesAn utility function to choose a target district for fire-stations
Hospital has an UF to choose a target ward for ambulancesPolice office has an UF to choose a target ward for police forcesFire-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
Behavior of an ambulance
• Ambulance have two questions:
• Go to an onsite victim for first-aid or take the carried victims to hospital?
• If the type is determined, which precise target will be chosen?
• Decision model of ambulance contains:
• One decision tree to choose a target type (victim or hospital)
• Two utility functions to choose a target of a specific type
26
F(Vk) = ∑ wi * Ck
i
The vic(m Vmax will be selected if: Vmax = ArgMax{F(Vk)}
Criteria to choose a victim Min/Max
Name
Distance (from ambulance to victim) (-) C1
Gravity (of victim) (+) C2
Distance (from victim) to closest other victim (-) C3
Number of victims nearby (+) C4
Max gravity of victims nearby (+) C5
Hospital
Can carry more
No Yes
Victim
Serious victim carried
No
Hospital
Yes
27
Learning decision tree
Victim
I will go to V1 because:I can carry more victimand V1 is close to me
Hospital
Can carry more
No Yes
User change decision
{V1, V2, V3, V4, H1, H2}Alternatives:
Decision:
Reasoning for change
High(freeBedNumber)
SeriousVictimCarried
Numericalcriteria:
Boolean function:
H1
You must go to H1 becauseyou carry a victim in critical state
and H1 has free beds
27
Learning decision tree
Victim
I will go to V1 because:I can carry more victimand V1 is close to me
Hospital
Can carry more
No Yes
User change decision
{V1, V2, V3, V4, H1, H2}Alternatives:
Decision:
Reasoning for change
High(freeBedNumber)
SeriousVictimCarried
Numericalcriteria:
Boolean function:
H1
You must go to H1 becauseyou carry a victim in critical state
and H1 has free beds
27
Learning decision tree
Victim
I will go to V1 because:I can carry more victimand V1 is close to me
No
• find the leaf-node corresponding to current context
• replace the leaf-node by a subtree
• boolean condition of sub-tree is defined by users
Hospital
Can carry more
No Yes
Serious victim carried
Hospital
Yes
Ambulance1 choose a target
{V1, V2, V3, V4, H1, H2}Alternatives:
{V1}Decision:
Reasoning for decision
Numericalcriteria: Low(distance)
Boolean function: CanCarryMore
Learning utility function
28
I will go to V1 because:s/he is close to me
F(Vk)= distance-1*
Ambulance1 choose a target
{V1, V2, V3, V4, H1, H2}Alternatives:
{V1}Decision:
Reasoning for decision
Numericalcriteria: Low(distance)
Boolean function: CanCarryMore
Learning utility function
28
I will go to V1 because:s/he is close to me
You must go to V2 because:s/he’s in a more critical state
F(Vk)= distance-1*
Ambulance1 choose a target
{V1, V2, V3, V4, H1, H2}Alternatives:
{V1}Decision:
Reasoning for decision
Numericalcriteria: Low(distance)
Boolean function: CanCarryMore
Learning utility function
28
I will go to V1 because:s/he is close to me
You must go to V2 because:s/he’s in a more critical state
F(Vk)= distance-1*
gravity
• Add new numerical criteria (identified by user) to the function
Ambulance1 choose a target
{V1, V2, V3, V4, H1, H2}Alternatives:
{V1}Decision:
Reasoning for decision
Numericalcriteria: Low(distance)
Boolean function: CanCarryMore
Learning utility function
• Update criteria’ weights by solving “inequalities system” (Simplex method for linear programming [Vanderbei, 08])
28
I will go to V1 because:s/he is close to me
You must go to V2 because:s/he’s in a more critical state
F(Vk)= distance-1*-0.4*
gravity+0.6*
• Add new numerical criteria (identified by user) to the function
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
Validation of learning decision tree
Tree of the Oracle
30
Victim Hospital
Tree learnt by ambulance
Victim carried
YesNo
Have onsite victim
Victim carried
Wait
Yes
No
Hospital
Yes
Victim carried
No
Victim
No Yes
Can not carry more
No
Hospital
Yes
Serious victim carried
YesNo
Victim Hospital
Validation of learning decision tree
Tree of the Oracle
30
Victim
Hospital
Tree learnt by ambulance
Situation1 Victim carried
YesNo
No
Have not onsite victim
Wait
Yes
Have onsite victim
Victim carried
Wait
Yes
No
Hospital
Yes
Victim carried
No
Victim
No Yes
Can not carry more
No
Hospital
Yes
Serious victim carried
YesNo
Victim Hospital
Validation of learning decision tree
Tree of the Oracle
30
Victim HospitalVictim
Tree learnt by ambulance
Situation 2Victim carried
YesNo
No
Have not onsite victim
Wait
Yes
Have onsite victim
YesNo
Have onsite victim
Victim carried
Wait
Yes
No
Hospital
Yes
Victim carried
No
Victim
No Yes
Can not carry more
No
Hospital
Yes
Serious victim carried
YesNo
Victim Hospital
Validation of learning decision tree
Tree of the Oracle
30
Victim Hospital
Victim
Tree learnt by ambulanceSituation 3
Victim carried
YesNo
No
Have not onsite victim
Wait
Yes
Have onsite victim
YesNo
No
Serious victim carried
Yes
Hospital
Have onsite victim
Victim carried
Wait
Yes
No
Hospital
Yes
Victim carried
No
Victim
No Yes
Can not carry more
No
Hospital
Yes
Serious victim carried
YesNo
Victim Hospital
Validation of learning decision tree
Tree of the Oracle
30
Victim Hospital
Victim
Tree learnt by ambulance
• The same set of rules generated from the two trees
Victim carried
YesNo
No
Have not onsite victim
Wait
Yes
Have onsite victim
YesNo
No
No
Serious victim carried
Yes
Hospital
Have onsite victim
Victim carried
Wait
Yes
No
Hospital
Yes
Victim carried
No
Victim
No Yes
Can not carry more
No
Hospital
Yes
Serious victim carried
YesNo
Victim Hospital
Can not carry more
Yes
Hospital
Validation of learning utility function
• The function of agent converges towards UF of the Oracle
31
Time (in simulation steps)
Diff
eren
ce
First ambulance
Second ambulance
Difference(kmin) = ∑| ai – kmin* wi | with kmin= ArgMin{Difference(k)}
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
Error in the utility function of agents
32
F(Vk)= distance-1*
F(Hk)= distance-1*
Victim Hospital
Real-life test with usersVictim carried
YesNo
32
F(Vk)= distance-1*
gravity0.6*
F(Hk)= distance-1*
Yes
Victim
Hospital
Scenario1
Reduce 2 deaths
-0.4*
Real-life test with usersVictim carried
YesNo
Have onsite victim
Wait
No
32
F(Vk)= distance-1*
gravity0.6*
distance to closest other victim-0.1*
F(Hk)= distance-1*
Yes
Victim
No
Hospital
Victim
Scenario 2
Reduce 1 death
-0.4*
0.7*
-0.2*
Real-life test with usersVictim carried
YesNo
Have onsite victim
Wait
No
Have onsite victim
Yes
Can not carry more
Yes
Hospital
No
32
F(Vk)= distance-1*
gravity0.6*
distance to closest other victim-0.1*
number of victims nearby0.3*
F(Hk)= distance-1*
Yes
Victim
No
Hospital
Yes
Victim
Scenario 3
Have reachable
victimsNo
Hospital
Reduce 3 deaths
-0.4*
0.7*
-0.2*
-0.1*
0.5*
-0.1*
Real-life test with usersVictim carried
YesNo
Have onsite victim
Wait
No
Have onsite victim
Yes
Can not carry more
Yes
Hospital
No
32
F(Vk)= distance-1*
gravity0.6*
distance to closest other victim-0.1*
number of victims nearby
distance to closest ambulance
0.3*
0.07*
F(Hk)= distance-1*
number of free beds0.1*
Yes
Victim
No
Hospital
Yes
Yes
Victim
Have reachable
victimsNo
Hospital Have reachable savable victims
Hospital
No
Scenario 4
Reduce 2 deaths
-0.4*
0.7*
-0.2*
-0.1*
0.5*
-0.1*
0.13*
-0.03*
0.67*
-0.9*
Real-life test with usersVictim carried
YesNo
Have onsite victim
Wait
No
Have onsite victim
Yes
Can not carry more
Yes
Hospital
No
The final decision model of ambulances
Criteria to choose a victim Min/Max
Weight
Gravity (of victim) (+) 0.5459
Number of victims nearby (+) 0.1345
Distance (from ambulance to victim) (-) 0.1034
Distance (from victim) to closest other ambulance (+) 0.0725
Max gravity of victims nearby (+) 0.0665
Distance (from victim) to closest other victim (-) 0.0635
Distance (from victim) to closest hospital (-) 0.0137
Criteria to choose a hospital Min/Max
Weight
Distance (from ambulance to hospital) (-) 0.4106
Number of free beds (of hospital) (+) 0.2477
Number of victims nearby (+) 0.1267
Distance (from hospital) to closest ambulance (+) 0.0975
Max gravity of victims nearby (+) 0.0674
Distance (from hospital) to closest other victim (-) 0.0365
Distance (from hospital) to closest other hospital (-) 0.013633
Victim carried
Have onsite victim
Wait
Yes
No
Victim
Yes
Have onsite victim
No
Hospital
No Yes
Can not carry more
YesNo
HospitalHave
reachable victims
YesNo
Hospital Have reachable savable victims
Hospital
YesNo
Have reachable savable victims with
safe path
YesNo
Hospital
Have serious reachable savable victims with safe
path
YesNo
Victim (serious, reachable, savable,
safe path)Hospital
Serious victim carried
YesNo
Victim (reachable, savable, safe path)
Results for all ten scenarios
ScenarioScenario
ParametersParametersParametersParametersImprovement
(in reducing the
number of deaths)
Hospital
number
Ambulance
number
Victim
number
Ambulance
capacity
Improvement
(in reducing the
number of deaths)
1
2
3
4
5
6
7
8
9
10
1 1 6 1 2
1 1 8 2 1
1 1 18 3 3
2 2 33 3 2
2 4 42 4 4
2 4 54 5 3
5 15 67 6 6
5 15 86 8 8
6 24 128 10 7
6 24 242 10 12
34
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
35
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)
• ...
36
Thanks and Questions?
37
• 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