dangers in multiagent rescue using defacto janusz marecki nathan schurr, milind tambe, university of...

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

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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Outline

Motivation and Domain DEFACTO System Adjustable Autonomy Strategies Predicted results Experimental results & Dangers Summary

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Motivation

Large scale disasters

Incident commander

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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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Outline


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DEFACTO System Architecture

Demonstrating

Effective

Flexible

Agent

Coordination

Through

Omnipresence

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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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DAFACTO Movie

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Simulator

FireBrigade FireBrigade

Machinetta Agent Machinetta AgentMachinetta

Agent

Machinetta: Multiagent platform, Abstracted Theories of Teamwork (Scerri et al AAMAS 03)

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Outline


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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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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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Outline


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Calculating predictions

Strategy value equations Domain specific

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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


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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Predicted results - ctnd

Low B, High EQh

0

20

40

60

80


Str

ateg

y va

lue

A H ATH

Low B, High EQh

0

20

40

60

80


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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Outline


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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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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


Bu

ildin

gs

Sav

ed

A H AH ATH

Subject A

0

50

100

150

200

250

300


Bu

ildin

gs

Sav

ed

A H AH ATH

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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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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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Task allocation overload danger

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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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Future application

Automated First Responders using DEFACTO

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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

dangers in multiagent rescue using defacto janusz marecki nathan schurr, milind tambe, university of...

Documents

multiagent platform

human supervision dangers

strategyh human strategy

better results

h strategyath team level

low eqhlow b

level of autonomyagents

overall score low b