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Information Sharing for Distributed Planning. Prasanna Velagapudi. Large Heterogeneous Teams. 100s to 1000s of robots, agents, people Complex, collaborative tasks Dynamic, uncertain environment Joint planning intractable. Scaling Team Planning. - PowerPoint PPT Presentation
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AAMAS 2010 - Doctoral Symposium 1
Information Sharing forDistributed Planning
Prasanna Velagapudi
AAMAS 2010 - Doctoral Symposium 2
Large Heterogeneous Teams
• 100s to 1000s of robots, agents, people
• Complex, collaborative tasks
• Dynamic, uncertain environment
• Joint planning intractable
AAMAS 2010 - Doctoral Symposium 3
Scaling Team Planning
• Independent planners: can’t account for teammates• Existing work: needs specific structure or doesn’t
scale to these sizes– DPC, Prioritized Planning– JESP, Factored MDP, ND-POMDP
AAMAS 2010 - Doctoral Symposium 4
Iterated Distributed Planning
1. Factor the problem, enumerate interactions2. Compute independent plans & potential interactions3. Exchange messages about interactions4. Use exchanged information, improve local model
AAMAS 2010 - Doctoral Symposium 5
Iterated Distributed Planning
1. Factor the problem, enumerate interactions2. Compute independent plans & potential interactions3. Exchange messages about interactions4. Use exchanged information, improve local model
?
AAMAS 2010 - Doctoral Symposium 6
Iterated Distributed Planning
1. Factor the problem, enumerate interactions2. Compute independent plans & potential interactions3. Exchange messages about interactions4. Use exchanged information, improve local model
?
AAMAS 2010 - Doctoral Symposium 7
Iterated Distributed Planning
1. Factor the problem, enumerate interactions2. Compute independent plans & potential interactions3. Exchange messages about interactions4. Use exchanged information, improve local model
AAMAS 2010 - Doctoral Symposium 8
A Tale of Two Distributed Planners
Distributed Prioritized Planning (DPP) L-TREMOR
AAMAS 2010 - Doctoral Symposium 9
Distributed Prioritized Planning
AAMAS 2010 - Doctoral Symposium 10
Multiagent Path Planning
Start
Goal
AAMAS 2010 - Doctoral Symposium 11
Multiagent Path Planning
AAMAS 2010 - Doctoral Symposium 12
Prioritized Planning
• Assign priorities to agents based on path length
[van den Berg, et al 2005]
AAMAS 2010 - Doctoral Symposium 13
Prioritized Planning
• Plan from highest priority to lowest priority• Use previous agents as dynamic obstacles
[van den Berg, et al 2005]
AAMAS 2010 - Doctoral Symposium 14
Distributed Prioritized Planning
Parallelizable& Equivalent
AAMAS 2010 - Doctoral Symposium 15
Large-Scale Path Solutions
AAMAS 2010 - Doctoral Symposium 16
Large-Scale Path Solutions
AAMAS 2010 - Doctoral Symposium 17
DPP Results
Fewer Sequential Plans
AAMAS 2010 - Doctoral Symposium 18
DPP Results
Longer Planning TimeFewer Sequential Plans
AAMAS 2010 - Doctoral Symposium 19
• Prioritized Planning
• DPP
Why does this happen?
ABCD
ABCD
Longest planning agents might replan multiple times
Individual agent planning times varied by >2 orders of magnitude
Solution 2: Incremental Planning
Solution 1: Prioritize by plan time?
AAMAS 2010 - Doctoral Symposium 20
Summary of DPP
• Observable, certain world• Only one type of interaction: collision
• Far fewer sequential planning iterations• Incremental planning may reduce execution time
AAMAS 2010 - Doctoral Symposium 21
L-TREMOR
AAMAS 2010 - Doctoral Symposium 22
A Simple Rescue Domain
Rescue Agent
Cleaner Agent
Narrow Corridor
Victim
Unsafe Cell
Clearable Debris
AAMAS 2010 - Doctoral Symposium 23
A Simple (Large) Rescue Domain
AAMAS 2010 - Doctoral Symposium 24
Distributed POMDP with Coordination Locales (DPCL)
• Often, interactions between agents are sparse
Only fits one agent Passable if
cleaned
[Varakantham, et al 2009]
AAMAS 2010 - Doctoral Symposium 25
Distributed POMDP with Coordination Locales (DPCL)
• Define coordination locales (CLs) where POMDP model functions are not independent:
[Varakantham, et al 2009]
<S, A, Ω, P, R, O> (states) (actions) (obs.) (transition)(reward)(obs. fn)
AAMAS 2010 - Doctoral Symposium 26
Distributed POMDP with Coordination Locales (DPCL)
• Define coordination locales (CLs) where POMDP model functions are not independent:
[Varakantham, et al 2009]
S1, A1 S2, A2
SglobalR1, P1, O1 R2, P2, O2
Outside CL:(typical)
AAMAS 2010 - Doctoral Symposium 27
Distributed POMDP with Coordination Locales (DPCL)
• Define coordination locales (CLs) where POMDP model functions are not independent:
[Varakantham, et al 2009]
S1, A1 S2, A2
Sglobal
R12, P12, O12
Inside CL:(interaction)
AAMAS 2010 - Doctoral Symposium 28
TREMOR
Role Allocation Policy Solution Interaction Detection Coordination
TREMOR
Branch & Bound MDP
Independent EVA[3] solvers
Joint policy evaluation
Reward shapingof independent
models
[Varakantham, et al 2009]
AAMAS 2010 - Doctoral Symposium 29
L-TREMOR
Role Allocation Policy Solution Interaction Detection Coordination
TREMOR
Branch & Bound MDP
Independent EVA[3] solvers
Joint policy evaluation Reward shaping
of independentmodels
L-TREMOR
DecentralizedAuction
Sampling & message passing
Distributed & Parallelizable
AAMAS 2010 - Doctoral Symposium 30
Preliminary Results – Joint Utility
N = 6 N = 10N = 100
(structurally similar to N=10)
AAMAS 2010 - Doctoral Symposium 31
Preliminary Results – Timing
AAMAS 2010 - Doctoral Symposium 32
Preliminary Results – Model Accuracy
R = 0.804
AAMAS 2010 - Doctoral Symposium 33
Current Issues
• Oscillations in solutions
• Discovery of relevant locales
?
AAMAS 2010 - Doctoral Symposium 34
Summary of L-TREMOR
• Partially-observable, uncertain world• Multiple types of interactions• Role-allocation of tasks
• Improvement over independent planning• Handles large problems• Next steps: improving convergence
AAMAS 2010 - Doctoral Symposium 35
Conclusions
• Two approaches to distributed planning– DPP: approaching centralized performance– L-TREMOR: exceeding joint tractability
• Analogous strategies for distributing planning– Both iterate independent planners– Both exchange messages about states, actions
AAMAS 2010 - Doctoral Symposium 36
Future Work
• Generalized framework for distributed planning through iterative message exchange
• Reduce necessary communication• Better search over task allocations• Scaling to larger team sizes
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