Mark Bartlett

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James CussensSenior Lecturer in Computer Science, University of BristolVerified email at bristol.ac.uk
Dimitar KazakovAssoc. Prof. (Senior Lecturer), Dept. of Computer Science, University of York, York, UKVerified email at cs.york.ac.uk
Iain BateReal-Time Systems Group (RTSRG), University of YorkVerified email at york.ac.uk
Graziano CeddiaAssistant Professor - ERC Grantee, Centre for Development and Environment, Universität BernVerified email at unibe.ch
Nuala A SheehanProfessor of Statistical Genetics, University of LeicesterVerified email at le.ac.uk
Charles PerringsArizona State UniversityVerified email at asu.edu
Alan M FrischUniversity of YorkVerified email at york.ac.uk
Ian MiguelUniversity of St AndrewsVerified email at st-andrews.ac.uk
Youssef HamadiMicrosoft ResearchVerified email at microsoft.com
S. Armagan TarimProfessor of Business Analytics, University College CorkVerified email at ucc.ie
Rayner AlfredAssociate Professor of Computer Science, Universiti Malaysia SabahVerified email at ums.edu.my
Janne H. KorhonenIST AustriaVerified email at ist.ac.at
Matti JärvisaloAssociate Professor, University of HelsinkiVerified email at helsinki.fi
Josu CeberioProfessor Assistant at University of the Basque CountryVerified email at ehu.eus
John McCallHead of Research, National Subsea Centre, Robert Gordon UniversityVerified email at rgu.ac.uk
James NjugunaProfessor of Composite Materials & Structures at Robert Gordon University, AberdeenVerified email at rgu.ac.uk
Auwalu MohammedRobert Gordon UniversityVerified email at rgu.ac.uk

Mark Bartlett

School of Computing Science and Digital Media, Robert Gordon University

Verified email at rgu.ac.uk - Homepage

Artificial Intelligence Uncertainty Optimisation and Planning Probabilistic Graphical Models Monte Carlo Tree Search


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Advances in Bayesian network learning using integer programming M Bartlett, J Cussens Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI 13 …, 2013	116	2013
Integer linear programming for the Bayesian network structure learning problem M Bartlett, J Cussens Artificial Intelligence 244, 258-271, 2017	94	2017
Maximum likelihood pedigree reconstruction using integer linear programming J Cussens, M Bartlett, EM Jones, NA Sheehan Genetic epidemiology 37 (1), 69-83, 2013	47	2013
The temporal knapsack problem and its solution M Bartlett, AM Frisch, Y Hamadi, I Miguel, SA Tarim, C Unsworth International conference on integration of artificial intelligence (AI) and …, 2005	45	2005
Landscape gene flow, coexistence and threshold effect: the case of genetically modified herbicide tolerant oilseed rape (Brassica napus) MG Ceddia, M Bartlett, C Perrings Ecological Modelling 205 (1-2), 169-180, 2007	36	2007
Bayesian network structure learning with integer programming: Polytopes, facets and complexity J Cussens, M Järvisalo, JH Korhonen, M Bartlett Journal of Artificial Intelligence Research 58, 185-229, 2017	33	2017
Quantifying the effect of buffer zones, crop areas and spatial aggregation on the externalities of genetically modified crops at landscape level MG Ceddia, M Bartlett, C Perrings Agriculture, ecosystems & environment 129 (1-3), 65-72, 2009	27	2009
The role of environment structure in multi-agent simulations of language evolution M Bartlett, D Kazakov Proceedings of the Fourth Symposium on Adaptive Agents and Multi-Agent …, 2004	19	2004
On the regulation of spatial externalities: coexistence between GM and conventional crops in the EU and the ‘newcomer principle’ MG Ceddia, M Bartlett, C De Lucia, C Perrings Australian Journal of Agricultural and Resource Economics 55 (1), 126-143, 2011	18	2011
The origins of syntax: from navigation to language M Bartlett, D Kazakov Connection Science 17 (3-4), 271-288, 2005	16	2005
Implementing a biofuel economy in the EU: Lessons from the SUSTOIL project and future perspectives for next generation biofuels C De Lucia, M Bartlett Renewable and Sustainable Energy Reviews 29, 22-30, 2014	14	2014
Guaranteed loop bound identification from program traces for WCET M Bartlett, I Bate, D Kazakov 2009 15th IEEE Real-Time and Embedded Technology and Applications Symposium …, 2009	14	2009
Cooperative navigation and the faculty of language D Kazakov, M Bartlett Applied Artificial Intelligence 18 (9-10), 885-901, 2004	13	2004
Improved maximum likelihood reconstruction of complex multi-generational pedigrees NA Sheehan, M Bartlett, J Cussens Theoretical population biology 97, 11-19, 2014	12	2014
Hierarchical agglomerative clustering for cross-language information retrieval R Alfred, D Kazakov, M Bartlett, E Paskaleva Int J Transl 19 (1), 139-162, 2007	11	2007
Does good ESG lead to better financial performances by firms? Machine learning and logistic regression models of public enterprises in Europe C De Lucia, P Pazienza, M Bartlett Sustainability 12 (13), 5317, 2020	10	2020
A multi-agent simulation of the evolution of language D Kazakov, M Bartlett Proceedings of Information Society Conference IS, 39-41, 2002	10	2002
Hierarchical agglomerative clustering of English-Bulgarian parallel Corpora R Alfred, E Paskaleva, D Kazakov, M Bartlett RECENT ADVANCES IN NATURAL LANGUAGE PROCESSING, 24, 2007	7	2007
Accurate determination of loop iterations for worst-case execution time analysis M Bartlett, I Bate, D Kazakov IEEE Transactions on Computers 59 (11), 1520-1532, 2010	5	2010
Instruction cache prediction using Bayesian networks M Bartlett, I Bate, J Cussens ECAI 2010, 1099-1100, 2010	5	2010

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