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Dangers in Multiagent Rescue using DEFACTO
Janusz MareckiNathan Schurr, Milind Tambe, University of Southern California
Paul ScerriCarnegie Mellon University
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Dangers in Multiagent Rescue using DEFACTO
Dangers in Multiagent Rescue
Autonomous Multiagent Rescue– Problem: Which house to
rescue first?– Human expertise &
responsibility
Human supervisor
– Problem: Human overwhelmed with tasks
Mixed decision making = DANGER
? ?
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Dangers in Multiagent Rescue using DEFACTO
Outline
Motivation and Domain DEFACTO System Adjustable Autonomy Strategies Predicted results Experimental results & Dangers Summary
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Dangers in Multiagent Rescue using DEFACTO
Motivation
Large scale disasters
Incident commander
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Dangers in Multiagent Rescue using DEFACTO
Domain timeline
Currently:– Thorough testing of DEFACTO system
Short term goal:– Los Angeles Fire Department Training Tool
Long term goal:– Automated First Responders
under human supervision
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Dangers in Multiagent Rescue using DEFACTO
Outline
Motivation and Domain DEFACTO System Adjustable Autonomy Strategies Predicted results Experimental results & Dangers Summary
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Dangers in Multiagent Rescue using DEFACTO
DEFACTO System Architecture
Demonstrating
Effective
Flexible
Agent
Coordination
Through
Omnipresence
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Dangers in Multiagent Rescue using DEFACTO
DEFACTO System Architecture
Demonstrating
Effective
Flexible
Agent
Coordination
Through
Omnipresence
Robocup Rescue Simulation Environment 7 different simulators (fire, traffic, civilians etc.) Different maps (USC, Kobe)
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Dangers in Multiagent Rescue using DEFACTO
DEFACTO System Architecture
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Dangers in Multiagent Rescue using DEFACTO
DAFACTO Movie
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Dangers in Multiagent Rescue using DEFACTO
DEFACTO System Architecture
Simulator
FireBrigade FireBrigade
Machinetta Agent Machinetta AgentMachinetta
Agent
Machinetta: Multiagent platform, Abstracted Theories of Teamwork (Scerri et al AAMAS 03)
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Dangers in Multiagent Rescue using DEFACTO
Outline
Motivation and Domain DEFACTO System Adjustable Autonomy Strategies Predicted results Experimental results & Dangers Summary
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Dangers in Multiagent Rescue using DEFACTO
Adjustable autonomy strategies
Agents dynamically adjust own level of autonomy– Agents act autonomously, but also...– Give up autonomy, transferring control to humans
When to transfer decision-making control – Whenever human has superior expertise– Yet, do not overload human with tasks!– Previous: Individual agent-human interaction
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Dangers in Multiagent Rescue using DEFACTO
Team level Adjustable Autonomy
AT Team level A strategy H Human strategy for all tasks AH Individual A strategy followed by
the H strategy ATH Team level A strategy followed
by the H strategy B The maximum number of agents the
human is able to control EQH The quality of human decisions
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Dangers in Multiagent Rescue using DEFACTO
Outline
Motivation and Domain DEFACTO System Adjustable Autonomy Strategies Predicted results Experimental results & Dangers Summary
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Dangers in Multiagent Rescue using DEFACTO
Calculating predictions
Strategy value equations Domain specific
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Dangers in Multiagent Rescue using DEFACTO
Predicted results
Low B, Low EQh
0
20
40
60
80
2 3 4 5 6 7 8 9 10Number of agents
Str
ateg
y va
lue
A H ATH
Low B, High EQh
0
20
40
60
80
2 3 4 5 6 7 8 9 10Number of agents
Str
ateg
y va
lue
A H ATH
Low B, Low EQh Low B, High EQh
Although higher expected quality of human decisions yields better results, low limit of human controllable agents hampers the overall score
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Dangers in Multiagent Rescue using DEFACTO
Predicted results - ctnd
Low B, High EQh
0
20
40
60
80
2 3 4 5 6 7 8 9 10Number of agents
Str
ateg
y va
lue
A H ATH
Low B, High EQh
0
20
40
60
80
2 3 4 5 6 7 8 9 10Number of agents
Str
ateg
y va
lue
A H ATH
High B, Low EQh High B, High EQh
High limit of human controllable agents makes the human involving strategies effective also for larger teams, beating the fully autonomous A strategy
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Dangers in Multiagent Rescue using DEFACTO
Outline
Motivation and Domain DEFACTO System Adjustable Autonomy Strategies Predicted results Experimental results & Dangers Summary
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Dangers in Multiagent Rescue using DEFACTO
Experimental setup
3 Subjects Allocation Viewer Same Map for each scenario
– Building size and location– Initial position of fires
4, 6, and 10 agents A, H, AH, ATH Strategies Averaged over 3 runs
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Dangers in Multiagent Rescue using DEFACTO
Experimental results
Subject C
0
50
100
150
200
250
300
3 5 7 9 11Number of Fire Engines
Bu
ildin
gs
Sav
ed
A H AH ATH
Subject B
0
50
100
150
200
250
300
3 5 7 9 11Number of Fire Engines
Bu
ildin
gs
Sav
ed
A H AH ATH
Subject A
0
50
100
150
200
250
300
3 5 7 9 11Number of Fire Engines
Bu
ildin
gs
Sav
ed
A H AH ATH
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Dangers in Multiagent Rescue using DEFACTO
Conclusions from results
No strategy dominates through all the experiments in all cases As the number of agents increase, for strategy A the slope of
improvement is greater than the slope of improvement for H. This correlates with our prediction that humans are not as good at exploiting additional agents resources, whereas agents are able to better exploit increasing numbers of available teammates
If the difference for 4 agents between strategy A and H for a particular commander is small enough, as is the case with subjects A and C, then as we grow to larger numbers of agents, A will dominate AH, ATH and H
ATH was constructed to help out at large # of agents in the team. However, what we see instead is that ATH does better at smaller # of agents over H, in a very surprising result. At higher # of agents, ATH does worse for subject A than A.
Dip at 6 agents?
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Dangers in Multiagent Rescue using DEFACTO
Discrepancy for 6 agents?
At 6 agents case, mixed strategies involving humans and agents (AH and ATH) performed worse than for 4 agents case
At 6 agents case, H strategy improved over the 4 agents case
At 6 agents case, AT strategy improved over the 4 agents case
Hypothesis: Human-Agent conflicts in resource allocation caused the problem
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Dangers in Multiagent Rescue using DEFACTO
Task allocation overload danger
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Dangers in Multiagent Rescue using DEFACTO
Summary
Rigid transfer of control strategies are outperformed by flexible dominant strategy selection
Having human in the loop does not necessary lead to increased performance
Having humans and agents doing resource allocation simultaneously is susceptible to excessive reallocations which decreases overall performance
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Dangers in Multiagent Rescue using DEFACTO
Future application
Automated First Responders using DEFACTO
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Dangers in Multiagent Rescue using DEFACTO
Thank you!
Email: [email protected] Teamcore web site: http://teamcore.usc.edu Thanks
– CREATE Center– Fred Pighin, Pratik Patil, Nikhil Kasinadhuni and
J.P. Lewis