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Nicholas R. Jennings
Researcher at Imperial College London
Publications - 823
Citations - 65994
Nicholas R. Jennings is an academic researcher from Imperial College London. The author has contributed to research in topics: Multi-agent system & Autonomous agent. The author has an hindex of 116, co-authored 807 publications receiving 64112 citations. Previous affiliations of Nicholas R. Jennings include University of Warsaw & University of Southampton.
Papers
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Proceedings Article
The ActiveCrowdToolkit: an open-source tool for benchmarking active learning algorithms for crowdsourcing research
TL;DR: An open-source toolkit that allows the easy comparison of the performance of active learning methods over a series of datasets by combining a judgement aggregation model, task selection method and worker selection method is presented.
Book ChapterDOI
Market Engineering: A Research Agenda
TL;DR: In this paper, the authors set out the need for a coherent and encompassing agenda in this area and highlighted the key constituent building blocks, including legal frameworks, economic mechanisms, management science models, and information and communication technologies.
Journal ArticleDOI
Agents and markets
TL;DR: A study on the interactions between autonomous agents and markets is presented, including cooperation, coordination, and negotiation between agents that are becoming active participants in marketplaces.
Design and Implementation of ARCHON's Coordination Module
Nicholas R. Jennings,J. A. Pople +1 more
TL;DR: This paper describes the design and implementation of a domain-independent reusable coordination module, at the heart of the ARCHON architecture, based upon the philosophy of providing a corpus of extensible generic knowledge about cooperation and situation assessment.
Proceedings ArticleDOI
Consensus acceleration in multiagent systems with the Chebyshev semi-iterative method
TL;DR: This work considers the fundamental problem of reaching consensus in multiagent systems and derives two novel acceleration methods based on the Chebyshev semi-iterative method that maximizes the worst-case convergence speed and an asynchronous version that approximates the output of the synchronous algorithm.