Download - Information Sharing for Distributed Planning

AAMAS 2010 - Doctoral Symposium 1

Information Sharing forDistributed 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

• 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


Iterated Distributed Planning

1. Factor the problem, enumerate interactions2. Compute independent plans & potential interactions3. Exchange messages about interactions4. Use exchanged information, improve local model




?


A Tale of Two Distributed Planners

Distributed Prioritized Planning (DPP) L-TREMOR


Distributed Prioritized Planning


Multiagent Path Planning

Start

Goal


Multiagent Path Planning


Prioritized Planning

• Assign priorities to agents based on path length

[van den Berg, et al 2005]


Prioritized Planning

• Plan from highest priority to lowest priority• Use previous agents as dynamic obstacles

[van den Berg, et al 2005]


Distributed Prioritized Planning

Parallelizable& Equivalent


Large-Scale Path Solutions


DPP Results

Fewer Sequential Plans


DPP Results

Longer Planning TimeFewer Sequential Plans


• 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?


Summary of DPP

• Observable, certain world• Only one type of interaction: collision

• Far fewer sequential planning iterations• Incremental planning may reduce execution time


L-TREMOR


A Simple Rescue Domain

Rescue Agent

Cleaner Agent

Narrow Corridor

Victim

Unsafe Cell

Clearable Debris


A Simple (Large) Rescue Domain


Distributed POMDP with Coordination Locales (DPCL)

• Often, interactions between agents are sparse

Only fits one agent Passable if

cleaned

[Varakantham, et al 2009]



• Define coordination locales (CLs) where POMDP model functions are not independent:


<S, A, Ω, P, R, O> (states) (actions) (obs.) (transition)(reward)(obs. fn)





S1, A1 S2, A2

SglobalR1, P1, O1 R2, P2, O2

Outside CL:(typical)





S1, A1 S2, A2

Sglobal

R12, P12, O12

Inside CL:(interaction)


TREMOR

Role Allocation Policy Solution Interaction Detection Coordination

TREMOR

Branch & Bound MDP

Independent EVA[3] solvers

Joint policy evaluation

Reward shapingof independent

models



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


Preliminary Results – Joint Utility

N = 6 N = 10N = 100

(structurally similar to N=10)


Preliminary Results – Timing


Preliminary Results – Model Accuracy

R = 0.804


Current Issues

• Oscillations in solutions

• Discovery of relevant locales

?


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


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


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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