Bounty Hunting and Multi-Robot Task Allocation

Drew Wicke

Outline

● Introduction● Auction Based Task Allocation● Bounty Hunting based Task Allocation

○ Dynamic Tasks Results○ Task Abandonment Results

● Future Work○ Multi-robot task allocation○ Cooperative tasks○ Large scale

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Decentralized Dynamic Task Allocation

● Agents are mapped to tasks which they must complete● Agents decide collectively how to map the tasks● New tasks appear dynamically while agents are working on other

tasks

Real Life ApplicationsDisaster and Emergency TasksRobot SoccerPickup and Delivery problems

Common MethodsAuctionsMarketsContract Networks

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Dias, M. B., Zlot, R., Kalra, N., & Stentz, A. (2006). Market-based multirobot coordination: A survey and analysis. Proceedings of the IEEE, 94(7), 1257-1270.

● Single-Task Robots vs Multi-Task Robots● Single Robot Tasks vs Multi-Robot Tasks● Instantaneous Assignment vs Time

Extended

Taxonomy of Task Allocation

Gerkey, B. P., & Matarić, M. J. (2004). A formal analysis and taxonomy of task allocation in multi-robot systems. The International Journal of Robotics Research, 23(9), 939-954. 4

● Bid valuation (or fitness score) for each task

● Auctioneer determines winner after some clearing time

● Auctioneer monitors winners and reassigns if insufficient progress

Auctions (MURDOCH)

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Auctions are generally exclusive, assume infinite money supply and altruistic, non-strategic agents, require accurate valuation, and commonly assume sequential task arrival or some kind of task queue.This is an unwieldy and unreasonable set of constraints for many dynamic task allocation problems.

Auctions are a strange fit

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An alternative: Bounty Hunting!

Bounty hunter and Bail bondsman

Task Classes, and task commitment

Advantage: no bidding!

Disadvantage: Not exclusive

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Drew Wicke and David Freelan and Sean Luke (2015, May). Bounty Hunters and Multiagent Task Allocation. In Proceedings of the 2015 AAMAS (pp. 387-394).

Problem Description

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● Static Environment● Dynamic Agents - agents are removed● Dynamic Tasks - task distribution changes● Unreliable Collaborators - two slow agents are

added, pick tasks randomly.● Unexpectedly Bad Tasks - randomly selected

tasks become difficult for some agents

Evaluated by measuring the total bounty available.

Experiments

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● Bounty hunters learn which task classes to go after○ Simple - learns time to task and probability

of success○ Complex - learns time to task and

probability of success given that certain other agents have committed to the task.

● Bounty “Auction”○ Similar to an Auction, but incorporates the

Bounty

Bounty Hunters

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● Adaptive bounties converge to divvying tasks up despite not being exclusive

● Adaptive Bounties can successfully adapt to dynamically changing environments

● Adaptive Bounties can perform comparably to, and in some cases much better than, exclusive methods

● “Complex” is generally more effective

Results

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● Bounty hunters may abandon tasks, or Jumpship.

● Improves performance significantly in all but the Dynamic Agents and Dynamic Tasks experiments.

● Does statistically significantly the best in two new experiments.

Task Abandonment

Wicke, D., Wei, E., & Luke, S. Throwing in the Towel:Faithless Bounty Hunters as a Task Allocation Mechanism. Submitted AAMAS 2016.

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● Current assumptions○ Teleportation to home base after task

completion○ Home Base○ Simultaneous occupation of space○ Grid movement (manhattan distance)

● Instead○ Full pickup and delivery○ Obstacle avoidance○ Continuous space (cartesian distance)○ Incorporate cost and battery life

Multi-robot Bounty Hunting

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● Implement Bounty Hunting system in a multi-robot system using GMU’s FlockBots.

● Important to show that the system works in real life, not just in simulation.

Future Work

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Dias, M. B., Zlot, R., Kalra, N., & Stentz, A. (2006). Market-based multirobot coordination: A survey and analysis. Proceedings of the IEEE, 94(7), 1257-1270.

Gerkey, B. P., & Matarić, M. J. (2004). A formal analysis and taxonomy of task allocation in multi-robot systems. The International Journal of Robotics Research, 23(9), 939-954.

Gerkey, B. P., & Matari, M. J. (2002). Sold!: Auction methods for multirobot coordination. Robotics and Automation, IEEE Transactions on, 18(5), 758-768.

Wicke, D., Freelan, D., & Luke, S. (2015, May). Bounty Hunters and Multiagent Task Allocation. In Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems (pp. 387-394). International Foundation for Autonomous Agents and Multiagent Systems.

Wicke, D., Wei, E., & Luke, S. Throwing in the Towel:Faithless Bounty Hunters as a Task Allocation Mechanism. Submitted AAMAS 2016.

Bounty Hunting and Multi-Robot Task Allocation

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Bounty Hunting and Multi-Robot Task Allocation

Questions

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