EAGER: Distributed Learning in Expert Referral Networks
EAGER:专家推荐网络中的分布式学习
基本信息
- 批准号:1649225
- 负责人:
- 金额:$ 9万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2016
- 资助国家:美国
- 起止时间:2016-09-01 至 2017-10-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Whom do you ask when you don't know whom to ask? That may be considered a rhetorical question in some contexts, but it is the "raison d'être" for referral networks. If a person must address a problem, but lacks the knowledge of how to solve it, he or she asks someone who may either provide a solution, or may know someone else who might provide the solution. Referral networks are very useful for professional success, such as in consulting companies, health-care organizations (e.g. referral of patients to medical specialists) or interdisciplinary research endeavors. The advent of AI-based intelligent agents, who typically have narrow expertise, enables the creation of agent-based or mixed human-and-agent referral networks, but adds complexity to the referral process. In order to tame this complexity, the new research addresses learning to refer in a distributed setting. Each expert learns to better estimate the expertise of other experts in the network, whether human or AI-based agents, and thus overall network refers with increasing accuracy. The learning-to-refer methods are robust with respect to gradual expertise change (e.g. experts learn to perform better) or changes in the network (e.g. an experienced expert retires and/or one or more new but less experienced experts join).The research starts by modifying methods from reinforcement learning, such as the successful interval-estimation learning approach, extending them to the distributed referral-network setting. Preliminary results show that distributed interval threshold learning is effective in improving the accuracy of referrals with accrued experienced, and performs better than other approaches such as Q-learning or greedy selection of best-known expert. The research will address issues of robustness to changes in the referral network topology, benefits of informative priors and proactive skill advertisement by individual experts to their peers, and other related aspects relaxing the initial restrictive assumptions in order to address real referral-network scenarios. In addition to establishing this new line of distributed learning, this EAGER will generate data sets useful for further research in the area of expertise-network learning and make them available.
当你不知道该问谁的时候,你会问谁?在某些情况下,这可能被认为是一个反问句,但它是转介网络的“存在理由”。如果一个人必须解决一个问题,但缺乏如何解决它的知识,他或她会问可能提供解决方案的人,或者可能知道其他人可能提供解决方案的人。转介网络对于专业成功非常有用,例如在咨询公司、卫生保健组织(例如,将患者转介给医学专家)或跨学科研究工作中。基于人工智能的智能代理的出现,通常具有有限的专业知识,使人们能够创建基于代理或人与代理混合的转介网络,但增加了转介过程的复杂性。为了驯服这种复杂性,这项新的研究解决了在分布式环境中学习指代的问题。每个专家都学会更好地估计网络中其他专家的专业知识,无论是人类还是基于人工智能的代理,因此整个网络参考的准确性越来越高。学习-推荐方法对于逐渐的专业知识变化(例如,专家学习表现更好)或网络中的变化(例如,有经验的专家退休和/或一个或多个新的但经验较少的专家加入)是稳健的。研究开始于修改强化学习的方法,例如成功的区间估计学习方法,将其扩展到分布式推荐网络环境。初步结果表明,分布式区间阈值学习能有效地提高累积经验推荐的准确率,且优于Q学习或贪婪选择专家等其他方法。这项研究将解决转介网络拓扑结构变化的稳健性问题,个人专家向其同行发布信息丰富的前科和主动技能广告的好处,以及其他相关方面,放松最初的限制性假设,以应对真实的转介网络情景。除了建立这条新的分布式学习路线外,EIGER还将生成对专门知识领域的进一步研究有用的数据集--网络学习,并使其可用。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Jaime Carbonell其他文献
Epileptiform electroencephalogram discharges increase seizure recurrence risk in patients with acute symptomatic seizure due to a structural brain lesion
- DOI:
10.1016/j.seizure.2024.02.001 - 发表时间:
2024-04-01 - 期刊:
- 影响因子:
- 作者:
Laia Grau-López;Belén Flores-Pina;Marta Jiménez;Jaime Carbonell;Jordi Ciurans;Eva Chies;Olga Fagundez;Alejandra Fumanal;Juan Luis Becerra - 通讯作者:
Juan Luis Becerra
Tissue-specific patterns of caspase-1 and cytokines in excisional wounds are altered by shock in rat skin and muscle
- DOI:
10.1016/j.jcrc.2012.10.038 - 发表时间:
2013-02-01 - 期刊:
- 影响因子:
- 作者:
Ravi Starzl;Dolores Wolfram;Ruben Zamora;Bahiyyah Jefferson;Derek Barclay;Chien Ho;Gerald Brandacher;Stefan Schneeberger;W.P. Andrew Lee;Jaime Carbonell;Yoram Vodovotz - 通讯作者:
Yoram Vodovotz
Activity Theory : Legacies , Standpoints , and Hopes : A discussion of Andy Blunden ’ s An Interdisciplinary Theory of Activity
活动理论:遗产、立场和希望:对安迪·布伦登的跨学科活动理论的讨论
- DOI:
- 发表时间:
2012 - 期刊:
- 影响因子:0
- 作者:
D. Rumbaugh;James E. King;Michael J Beran;David A. Washburn;K. Gould;Nate Kornell;D. J. Scaturo;Brian D. Haig;R. Schvaneveldt;Benjamin K. Barton;Thomas A. Ulrich;Peter Robinson;Matthew J. Schuelke;Eric Anthony Day;Henry W. Chase;E. Carayannis;Timothy M. Flemming;Michael C. Mitchelmore;Paul White;Erin M. Brodhagen;M. Gettinger;E. Usher;David B. Morris;Janna Wardman;J. R. Nelson;R. Low;P. Jin;Betty K. Tuller;Noël Nguyen;Fons Wijnhoven;Gerhard Weber;C. Rigg;K. Trehan;Michael L. Jones;Aytac Gogus;N. Seel;Som Naidu;Danny R. Bedgood;Christina M. Steiner;Birgit Marte;Jürgen Heller;Dietrich Albert;A. Podolskiy;Lorna Uden;Andrew J. Martin;C. Balkenius;B. Johansson;Karen L. Hollis;David A. Cook;J. Bloomberg;Otmar Bock;R. Clariana;Simon Hooper;Amy B. Adcock;R. Van Eck;Chin;Chung;M. Burtsev;J. S. Nairne;Marco Vasconcelos;Josefa N. S. Pandeirada;Liu Yang;Jaime Carbonell;M. Dornisch;G. Manaster;Katie Davis;Marcia L. Conner;Dolores Fidishun;Mark Tennant;J. Gurlitt;J. Fletcher;S. Cerri;G. Veletsianos;P. Wickman;Jason D. Baker;M. Gläser;Soumaya Chaffar;C. Frasson;Dirk Hermans;Heleen Vandromme;Els Joos;Leily Ziglari;Benjamin D. Nye;Barry G. Silverman;E. Marchione;M. Salgado;Mimi Bong;Joaquin A. Anguera;Jin Bo;R. D. Seidler;K. Cennamo;V. Munde;C. Vlaskamp;W. Ruijssenaars;Bea Maes;H. Nakken;John Biggs;C. Tang;Vicki S. Napper;Carolyn E. Schwartz;Zhanna Reznikova;Ben Seymour;W. Yoshida;Ray Dolan;M. Speekenbrink;C. Breitenstein;Stefan Knecht;M. Guarini;Royal Skousen;Steve Chandler;Wendelin M. Küpers;U. Goswami;P. Blenkiron;A. Antonietti;Robert Samuel Matthews;Charlotte Hua Liu;Geoffrey Hall;Mireille Bétrancourt;Sandra Berney;Cathrine Hasse;Nigel Stepp;Martin Volker Butz;Giovanni Pezzulo;Filipo Studzinski Perotto;S. Cooray;A. Bakala;K. Purandare;Anusha Wijeratne;Jeff C. Marshall;Soh;Andrew Byrne;J. Campbell;Umar Syed;Klaus Nielsen;R. Feltman;Andrew J. Elliot;N. Entwistle;Bhaskar DasGupta;Derong Liu;Henning Fernau;Yu;Janusz Wojtusiak;Damian Grace;John M. Keller;Michael J. Ford;Nathalie Muller Mirza;Michael Jackson;Dana LaCourse Munteanu;Jason Arndt;Eva L. Baker;Fabio Alivernini;F. Tonneau;J. Jozefowiez;D. Sagi;Y. Adini;M. Tsodyks;Melissa L. Allen;Friedrich T. Sommer;Vivienne B. Carr;Kristina Wieland;Leslie C. Novosel;D. Deshler;Daniel T. Pollitt;Carrie Mark;Belinda B. Mitchell;K. Wolf;Notger G. Müller;M. Haselgrove;L. Gregory Appelbaum;Joseph A. Harris;Ulrike Halsband;E. Davelaar;Andrew Finch;W. Timothy Coombs;Annie Lang;O. Podolskiy;Stephen Billett;Joseph Psotka;Åsa Hammar;J. Worthen;R. Reed Hunt;Margaret MacDougall;É. Le Bourg;Tiago V. Maia - 通讯作者:
Tiago V. Maia
Machine Learning: A Maturing Field
- DOI:
10.1023/a:1022665512030 - 发表时间:
1992-06-01 - 期刊:
- 影响因子:2.900
- 作者:
Jaime Carbonell - 通讯作者:
Jaime Carbonell
Management of lipid-lowering treatment in patients with ischemic stroke in Catalonia and Balearic Islands, Spain. Malic study phase 3. Preliminary results
- DOI:
10.1016/j.atherosclerosis.2024.118181 - 发表时间:
2024-08-01 - 期刊:
- 影响因子:
- 作者:
Carolina Guerrero;Dídac Llop;Marta Mauri;Mertixell Royuela;Eva Anoro;Jaime Carbonell;Oriol Barrachina;David Cánovas;Rosa Borrallo;Àngels Pedragosa Vall - 通讯作者:
Àngels Pedragosa Vall
Jaime Carbonell的其他文献
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{{ truncateString('Jaime Carbonell', 18)}}的其他基金
EAGER: TEACHER: A Pilot Study on Mining the Web for Customized Curriculum Planning
EAGER:老师:挖掘网络进行定制课程规划的试点研究
- 批准号:
1350364 - 财政年份:2013
- 资助金额:
$ 9万 - 项目类别:
Standard Grant
RI: Medium: Interactive Transfer Learning in Dynamic Environments
RI:媒介:动态环境中的交互式迁移学习
- 批准号:
1065251 - 财政年份:2011
- 资助金额:
$ 9万 - 项目类别:
Continuing Grant
LETRAS: A Learning-based Framework for Machine Translation of Low Resource Languages
LETRAS:基于学习的低资源语言机器翻译框架
- 批准号:
0534217 - 财政年份:2006
- 资助金额:
$ 9万 - 项目类别:
Continuing Grant
ITR/PE: AVENUE: Adaptable Voice Translation for Minority Languages
ITR/PE:AVENUE:针对少数民族语言的自适应语音翻译
- 批准号:
0121631 - 财政年份:2001
- 资助金额:
$ 9万 - 项目类别:
Continuing Grant
MLIAM: MUCHMORE: Multilingual Concept Hierarchies for Medical Information Organization and Retrieval
MLIAM:MUCHMORE:医疗信息组织和检索的多语言概念层次结构
- 批准号:
9982226 - 财政年份:2000
- 资助金额:
$ 9万 - 项目类别:
Continuing Grant
STIMULATE: Generalized Example-Based Machine Translation
STIMULATE:广义的基于示例的机器翻译
- 批准号:
9618941 - 财政年份:1997
- 资助金额:
$ 9万 - 项目类别:
Continuing Grant
Multitext Fusion, Tracking and Trend Detection
多文本融合、跟踪和趋势检测
- 批准号:
9314992 - 财政年份:1994
- 资助金额:
$ 9万 - 项目类别:
Continuing Grant
Learning by Abstraction and Analogy: Acquiring Planning Expertise in Complex Domains
通过抽象和类比学习:获得复杂领域的规划专业知识
- 批准号:
9022499 - 财政年份:1991
- 资助金额:
$ 9万 - 项目类别:
Continuing Grant
US Japan AI Syposium (Computer and Information Science)
美日人工智能研讨会(计算机与信息科学)
- 批准号:
8800097 - 财政年份:1987
- 资助金额:
$ 9万 - 项目类别:
Standard Grant
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