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Waves: a model of collective learning

Published: 23 August 2017 Publication History

Abstract

Collective learning considers how agents, in a community sharing a learning purpose, may benefit from exchanging hypotheses and observations to learn efficiently as a community as well as individuals. The community forms a communication network and each agent has access to observations. We address the question of a protocol, i.e. a set of agent's behaviours, which guarantees the hypotheses retained by the agents take into account all the observations in the community. We present and investigate the protocol WAVES which displays such a guarantee in a turn-based scenario: at the beginning of each turn, agents collect new observations and interact until they all reach this consistency guarantee. We investigate and experiment WAVES on various network topologies and various experimental parameters. We present results on learning efficiency, in terms of computation and communication costs, as well as results on learning quality, in terms of predictive accuracy for a given number of observations collected by the community.

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  • (2018)Better Collective Learning with Consistency GuaranteesPRIMA 2018: Principles and Practice of Multi-Agent Systems10.1007/978-3-030-03098-8_55(671-679)Online publication date: 24-Oct-2018

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cover image ACM Conferences
WI '17: Proceedings of the International Conference on Web Intelligence
August 2017
1284 pages
ISBN:9781450349512
DOI:10.1145/3106426
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 23 August 2017

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WI '17 Paper Acceptance Rate 118 of 178 submissions, 66%;
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  • (2018)Better Collective Learning with Consistency GuaranteesPRIMA 2018: Principles and Practice of Multi-Agent Systems10.1007/978-3-030-03098-8_55(671-679)Online publication date: 24-Oct-2018

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