Agents can individually devise plans and coordinate to achieve common goals. Methods exist to factor planning problems into separate tasks and distribute the plan synthesis process, while reducing the overall planning complexity. Merging distributedly generated plans becomes computationally costly when task plans are tightly coupled, and conflicts arise due to dependencies between plan actions. New plan merging algorithms allow factoring and solving large problems with a growing number of agents and tasks, but are yet to be demonstrated in physical real-world systems.   This Demo presents an architecture that deploys plan merging algorithms in a physical multi-robot setting and emulates a First Response Domain.

Scalable Multiple Robot Task Planning with Plan Merging and Conflict Resolution

Poster/Demo Session 4 

poster

AAMAS is the largest and most influential conference in the area of agents and multiagent systems, bringing together researchers and practitioners in all areas of agent technology and providing and internationally renowned high-profile forum for publishing and finding out about the latest developments in the field. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).  

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Gilberto Marcon Santos

1

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Scalable Multiple Robot Task Planning with Plan Merging and Conflict Resolution

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