Document Type

Conference Proceeding

Publication Date

10-2009

Abstract

Coalition formation algorithms are generally not applicable to real-world robotic collectives since they lack mechanisms to handle uncertainty. Those mechanisms that do address uncertainty either deflect it by soliciting information from others or apply reinforcement learning to select an agent type from within a set. This paper presents a coalition formation mechanism that directly addresses uncertainty while allowing the agent types to fall outside of a known set. The agent types are captured through a novel agent modeling technique that handles uncertainty through a belief-based evaluation mechanism. This technique allows for uncertainty in environmental data, agent type, coalition value, and agent cost. An investigation of both the effects of adding agents on processing time and of model quality on the convergence rate of initial agent models (and thereby coalition quality) is provided. This approach handles uncertainty on a larger scale than previous work and provides a mechanism readily applied to a dynamic collective of real-world robots. Abstract © IEEE.

Comments

© 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

AFIT Scholar furnishes the accepted version of this conference paper. The published version of record is available from IEEE via subscription at the DOI link on this record page.

DOI

10.1109/IROS.2009.5354319

Source Publication

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009

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