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2020 – today
- 2023
[i23]David M. Knigge, David W. Romero, Albert Gu, Efstratios Gavves, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn, Jan-Jakob Sonke:
Modelling Long Range Dependencies in N-D: From Task-Specific to a General Purpose CNN. CoRR abs/2301.10540 (2023)- 2022
- [j34]Eoin Martino Grua
, Martina De Sanctis, Ivano Malavolta, Mark Hoogendoorn, Patricia Lago:
An evaluation of the effectiveness of personalization and self-adaptation for e-Health apps. Inf. Softw. Technol. 146: 106841 (2022) - [j33]Floris den Hengst, Vincent François-Lavet, Mark Hoogendoorn, Frank van Harmelen:
Planning for potential: efficient safe reinforcement learning. Mach. Learn. 111(6): 2255-2274 (2022) - [c120]Leonardos Pantiskas, Kees Verstoep, Mark Hoogendoorn, Henri E. Bal:
Taking ROCKET on an Efficiency Mission: Multivariate Time Series Classification with LightWaveS. DCOSS 2022: 149-152 - [c119]David W. Romero, Robert-Jan Bruintjes, Jakub Mikolaj Tomczak, Erik J. Bekkers, Mark Hoogendoorn, Jan van Gemert:
FlexConv: Continuous Kernel Convolutions With Differentiable Kernel Sizes. ICLR 2022 - [c118]David W. Romero, Anna Kuzina, Erik J. Bekkers, Jakub Mikolaj Tomczak, Mark Hoogendoorn:
CKConv: Continuous Kernel Convolution For Sequential Data. ICLR 2022 - [c117]Leonardos Pantiskas, Kees Verstoep, Mark Hoogendoorn, Henri E. Bal:
An Empirical Evaluation of Multivariate Time Series Classification with Input Transformation across Different Dimensions. ICMLA 2022: 23-28 - [c116]Floris den Hengst, Vincent François-Lavet, Mark Hoogendoorn, Frank van Harmelen:
Reinforcement Learning with Option Machines. IJCAI 2022: 2909-2915 - [i22]Luis Pedro Silvestrin, Harry van Zanten, Mark Hoogendoorn, Ger Koole:
Transfer-Learning Across Datasets with Different Input Dimensions: An Algorithm and Analysis for the Linear Regression Case. CoRR abs/2202.05069 (2022) - [i21]Etienne van de Bijl, Jan Klein, Joris Pries, Sandjai Bhulai, Mark Hoogendoorn, Rob van der Mei:
The Dutch Draw: Constructing a Universal Baseline for Binary Prediction Models. CoRR abs/2203.13084 (2022) - [i20]Leonardos Pantiskas, Kees Verstoep, Mark Hoogendoorn, Henri E. Bal:
Taking ROCKET on an Efficiency Mission: Multivariate Time Series Classification with LightWaveS. CoRR abs/2204.01379 (2022) - [i19]Olivier Moulin, Vincent François-Lavet, Paul W. G. Elbers, Mark Hoogendoorn:
Improving adaptability to new environments and removing catastrophic forgetting in Reinforcement Learning by using an eco-system of agents. CoRR abs/2204.06550 (2022) - [i18]David W. Romero, David M. Knigge, Albert Gu, Erik J. Bekkers, Efstratios Gavves, Jakub M. Tomczak, Mark Hoogendoorn:
Towards a General Purpose CNN for Long Range Dependencies in ND. CoRR abs/2206.03398 (2022) - [i17]Leonardos Pantiskas, Kees Verstoep, Mark Hoogendoorn, Henri E. Bal:
An Empirical Evaluation of Multivariate Time Series Classification with Input Transformation across Different Dimensions. CoRR abs/2210.07713 (2022) - [i16]Jacob E. Kooi, Mark Hoogendoorn, Vincent François-Lavet:
Disentangled (Un)Controllable Features. CoRR abs/2211.00086 (2022) - [i15]Olivier Moulin, Vincent François-Lavet, Mark Hoogendoorn:
Improving generalization in reinforcement learning through forked agents. CoRR abs/2212.06451 (2022) - 2021
- [j32]Luca Roggeveen, Ali el Hassouni, Jonas Ahrendt, Tingjie Guo, Lucas M. Fleuren, Patrick Thoral, Armand R. J. Girbes, Mark Hoogendoorn, Paul W. G. Elbers:
Transatlantic transferability of a new reinforcement learning model for optimizing haemodynamic treatment for critically ill patients with sepsis. Artif. Intell. Medicine 112: 102003 (2021) - [j31]Jie Jiang, Qiuqiang Kong, Mark D. Plumbley, Nigel Gilbert, Mark Hoogendoorn, Diederik M. Roijers:
Deep Learning-Based Energy Disaggregation and On/Off Detection of Household Appliances. ACM Trans. Knowl. Discov. Data 15(3): 50:1-50:21 (2021) - [c115]Daniel Lutscher, Ali el Hassouni, Maarten Stol, Mark Hoogendoorn:
Mixing Consistent Deep Clustering. LOD 2021: 124-137 - [c114]Luis P. Silvestrin, Leonardos Pantiskas, Mark Hoogendoorn:
A Framework for Imbalanced Time-Series Forecasting. LOD 2021: 250-264 - [c113]Ali el Hassouni, Mark Hoogendoorn, Marketa Ciharova, Annet Kleiboer, Khadicha Amarti, Vesa Muhonen, Heleen Riper, A. E. Eiben:
pH-RL: A Personalization Architecture to Bring Reinforcement Learning to Health Practice. LOD 2021: 265-280 - [c112]Jan Klein, Sandjai Bhulai, Mark Hoogendoorn, Robert D. van der Mei:
Plusmine: Dynamic Active Learning with Semi-Supervised Learning for Automatic Classification. WI/IAT 2021: 146-153 - [p2]Eoin Martino Grua, Martina De Sanctis, Ivano Malavolta, Mark Hoogendoorn, Patricia Lago:
Social Sustainability in the e-Health Domain via Personalized and Self-Adaptive Mobile Apps. Software Sustainability 2021: 301-328 - [i14]David W. Romero, Anna Kuzina, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn:
CKConv: Continuous Kernel Convolution For Sequential Data. CoRR abs/2102.02611 (2021) - [i13]Ali el Hassouni, Mark Hoogendoorn, Marketa Ciharova, Annet Kleiboer, Khadicha Amarti, Vesa Muhonen, Heleen Riper, A. E. Eiben:
pH-RL: A personalization architecture to bring reinforcement learning to health practice. CoRR abs/2103.15908 (2021) - [i12]Luis P. Silvestrin, Leonardos Pantiskas, Mark Hoogendoorn:
A Framework for Imbalanced Time-series Forecasting. CoRR abs/2107.10709 (2021) - [i11]Jan Klein, Sandjai Bhulai, Mark Hoogendoorn, Rob van der Mei:
Jasmine: A New Active Learning Approach to Combat Cybercrime. CoRR abs/2108.06238 (2021) - [i10]David W. Romero, Robert-Jan Bruintjes, Jakub M. Tomczak, Erik J. Bekkers, Mark Hoogendoorn, Jan C. van Gemert:
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes. CoRR abs/2110.08059 (2021) - 2020
- [j30]Olga Pólchlopek, Nynke R. Koning, Frederike L. Büchner, Mathilde R. Crone, Mattijs E. Numans, Mark Hoogendoorn:
Quantitative and temporal approach to utilising electronic medical records from general practices in mental health prediction. Comput. Biol. Medicine 125: 103973 (2020) - [j29]Floris den Hengst, Eoin Martino Grua, Ali el Hassouni, Mark Hoogendoorn:
Reinforcement learning for personalization: A systematic literature review. Data Sci. 3(2): 107-147 (2020) - [c111]David W. Romero, Mark Hoogendoorn:
Co-Attentive Equivariant Neural Networks: Focusing Equivariance On Transformations Co-Occurring in Data. ICLR 2020 - [c110]David W. Romero, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn:
Attentive Group Equivariant Convolutional Networks. ICML 2020: 8188-8199 - [c109]Seyed Amin Tabatabaei, Jan Klein, Mark Hoogendoorn:
Estimating the F1 Score for Learning from Positive and Unlabeled Examples. LOD (1) 2020: 150-161 - [c108]Ali el Hassouni, Mark Hoogendoorn, A. E. Eiben, Vesa Muhonen:
Structural and Functional Representativity of GANs for Data Generation in Sequential Decision Making. LOD (1) 2020: 458-471 - [i9]Xixi Lu, Seyed Amin Tabatabaei, Mark Hoogendoorn, Hajo A. Reijers:
Trace Clustering on Very Large Event Data in Healthcare Using Frequent Sequence Patterns. CoRR abs/2001.03411 (2020) - [i8]David W. Romero, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn:
Attentive Group Equivariant Convolutional Networks. CoRR abs/2002.03830 (2020) - [i7]David W. Romero, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn:
Wavelet Networks: Scale Equivariant Learning From Raw Waveforms. CoRR abs/2006.05259 (2020) - [i6]Daniel Lutscher, Ali el Hassouni, Maarten Stol, Mark Hoogendoorn:
Mixing Consistent Deep Clustering. CoRR abs/2011.01977 (2020)
2010 – 2019
- 2019
- [c107]Xixi Lu, Seyed Amin Tabatabaei, Mark Hoogendoorn, Hajo A. Reijers:
Trace Clustering on Very Large Event Data in Healthcare Using Frequent Sequence Patterns. BPM 2019: 198-215 - [c106]Seyed Amin Tabatabaei, Xixi Lu, Mark Hoogendoorn, Hajo A. Reijers:
Identifying Patient Groups based on Frequent Patterns of Patient Samples. HealthCom 2019: 1-6 - [c105]Caleb Mensah, Jan Klein, Sandjai Bhulai, Mark Hoogendoorn, Rob van der Mei:
Detecting Fraudulent Bookings of Online Travel Agencies with Unsupervised Machine Learning. IEA/AIE 2019: 334-346 - [c104]Luis P. Silvestrin, Mark Hoogendoorn, Ger Koole:
A Comparative Study of State-of-the-Art Machine Learning Algorithms for Predictive Maintenance. SSCI 2019: 760-767 - [c103]Alessandro Zonta, Selmar K. Smit, Mark Hoogendoorn, A. E. Eiben:
Generation of Human-Like Movements Based on Environmental Features. SSCI 2019: 3079-3086 - [c102]Mark Hoogendoorn, Ward van Breda, Jeroen Ruwaard:
GP-HD: Using Genetic Programming to Generate Dynamical Systems Models for Health Care. WI 2019: 1-8 - [c101]Floris den Hengst, Mark Hoogendoorn, Frank van Harmelen, Joost Bosman:
Reinforcement Learning for Personalized Dialogue Management. WI 2019: 59-67 - [c100]Ali el Hassouni, Mark Hoogendoorn, A. E. Eiben, Martijn van Otterlo, Vesa Muhonen:
End-to-end Personalization of Digital Health Interventions using Raw Sensor Data with Deep Reinforcement Learning. WI 2019: 258-264 - [c99]Eoin Martino Grua, Mark Hoogendoorn, Ivano Malavolta, Patricia Lago, A. E. Eiben:
CluStream-GT: Online Clustering for Personalization in the Health Domain. WI 2019: 270-275 - [i5]Seyed Amin Tabatabaei, Xixi Lu, Mark Hoogendoorn, Hajo A. Reijers:
Identifying Patient Groups based on Frequent Patterns of Patient Samples. CoRR abs/1904.01863 (2019) - [i4]Mark Hoogendoorn, Ward van Breda, Jeroen Ruwaard:
GP-HD: Using Genetic Programming to Generate Dynamical Systems Models for Health Care. CoRR abs/1904.05815 (2019) - [i3]Floris den Hengst, Mark Hoogendoorn, Frank van Harmelen, Joost Bosman:
Reinforcement Learning for Personalized Dialogue Management. CoRR abs/1908.00286 (2019) - [i2]David W. Romero, Mark Hoogendoorn:
Co-Attentive Equivariant Neural Networks: Focusing Equivariance On Transformations Co-Occurring In Data. CoRR abs/1911.07849 (2019) - 2018
- [b1]Mark Hoogendoorn, Burkhardt Funk:
Machine Learning for the Quantified Self - On the Art of Learning from Sensory Data. Cognitive Systems Monographs 35, Springer 2018, ISBN 978-3-319-66307-4, pp. 1-221 - [c98]Jie Jiang, Mark Hoogendoorn, Qiuqiang Kong, Diederik M. Roijers, Nigel Gilbert:
Predicting Appliance Usage Status In Home Like Environments. DSP 2018: 1-5 - [c97]Seyed Amin Tabatabaei, Mark Hoogendoorn, Aart van Halteren:
Narrowing Reinforcement Learning: Overcoming the Cold Start Problem for Personalized Health Interventions. PRIMA 2018: 312-327 - [c96]Ali el Hassouni, Mark Hoogendoorn, Martijn van Otterlo, Eduardo Barbaro:
Personalization of Health Interventions Using Cluster-Based Reinforcement Learning. PRIMA 2018: 467-475 - [c95]Ali el Hassouni, Mark Hoogendoorn, Vesa Muhonen:
Using Generative Adversarial Networks to Develop a Realistic Human Behavior Simulator. PRIMA 2018: 476-483 - [c94]Eoin Martino Grua, Mark Hoogendoorn:
Exploring Clustering Techniques for Effective Reinforcement Learning based Personalization for Health and Wellbeing. SSCI 2018: 813-820 - [c93]Jan Klein, Sandjai Bhulai, Mark Hoogendoorn, Rob van der Mei, Raymond Hinfelaar:
Detecting Network Intrusion beyond 1999: Applying Machine Learning Techniques to a Partially Labeled Cybersecurity Dataset. WI 2018: 784-787 - [i1]Ali el Hassouni, Mark Hoogendoorn, Martijn van Otterlo, Eduardo Barbaro:
Personalization of Health Interventions using Cluster-Based Reinforcement Learning. CoRR abs/1804.03592 (2018) - 2017
- [j28]Ward van Breda, Mark Hoogendoorn, A. E. Eiben, Matthias Berking:
Assessment of temporal predictive models for health care using a formal method. Comput. Biol. Medicine 87: 347-357 (2017) - [j27]Mark Hoogendoorn, Thomas Berger, Ava Schulz, Timo Stolz, Peter Szolovits:
Predicting Social Anxiety Treatment Outcome Based on Therapeutic Email Conversations. IEEE J. Biomed. Health Informatics 21(5): 1449-1459 (2017) - [c92]Ryan Amirkhan, Mark Hoogendoorn, Mattijs E. Numans, Leon M. G. Moons:
Using recurrent neural networks to predict colorectal cancer among patients. SSCI 2017: 1-8 - 2016
- [j26]Mark Hoogendoorn, Peter Szolovits, Leon M. G. Moons, Mattijs E. Numans:
Utilizing uncoded consultation notes from electronic medical records for predictive modeling of colorectal cancer. Artif. Intell. Medicine 69: 53-61 (2016) - [j25]Reinier Kop, Mark Hoogendoorn, Annette ten Teije, Frederike L. Büchner, Pauline Slottje, Leon M. G. Moons, Mattijs E. Numans:
Predictive modeling of colorectal cancer using a dedicated pre-processing pipeline on routine electronic medical records. Comput. Biol. Medicine 76: 30-38 (2016) - [c91]Mark Hoogendoorn, Ali el Hassouni, Kwongyen Mok, Marzyeh Ghassemi, Peter Szolovits:
Prediction using patient comparison vs. modeling: A case study for mortality prediction. EMBC 2016: 2464-2467 - [c90]Ward R. J. van Breda, Mark Hoogendoorn, A. E. Eiben, Gerhard Andersson, Heleen Riper, Jeroen Ruwaard, Kristofer Vernmark:
A feature representation learning method for temporal datasets. SSCI 2016: 1-8 - 2015
- [j24]Tibor Bosse, Mark Hoogendoorn:
Special issue on advances in applied artificial intelligence. Appl. Intell. 42(1): 1-2 (2015) - [j23]Richard Koopmanschap, Mark Hoogendoorn, Jan Joris Roessingh:
Tailoring a cognitive model for situation awareness using machine learning. Appl. Intell. 42(1): 36-48 (2015) - [j22]Giorgos Karafotias, Mark Hoogendoorn, Ágoston E. Eiben:
Parameter Control in Evolutionary Algorithms: Trends and Challenges. IEEE Trans. Evol. Comput. 19(2): 167-187 (2015) - [c89]Reinier Kop, Mark Hoogendoorn, Leon M. G. Moons, Mattijs E. Numans, Annette ten Teije:
On the Advantage of Using Dedicated Data Mining Techniques to Predict Colorectal Cancer. AIME 2015: 133-142 - [c88]Ward R. J. van Breda, Mark Hoogendoorn, A. E. Eiben, Matthias Berking:
An Evaluation Framework for the Comparison of Fine-Grained Predictive Models in Health Care. AIME 2015: 148-152 - [c87]Giorgos Karafotias, Mark Hoogendoorn, A. E. Eiben:
Evaluating Reward Definitions for Parameter Control. EvoApplications 2015: 667-680 - [c86]Reinier Kop, Armon Toubman, Mark Hoogendoorn, Jan Joris Roessingh:
Evolutionary Dynamic Scripting: Adaptation of Expert Rule Bases for Serious Games. IEA/AIE 2015: 53-62 - [c85]Shu Gao, Mark Hoogendoorn:
Using Evolutionary Algorithms to Personalize Controllers in Ambient Intelligence. ISAmI 2015: 1-11 - 2014
- [j21]Mark Hoogendoorn, Syed Waqar Jaffry, Peter-Paul van Maanen, Jan Treur:
Design and validation of a relative trust model. Knowl. Based Syst. 57: 81-94 (2014) - [c84]Mark Hoogendoorn, Leon M. G. Moons, Mattijs E. Numans, Robert-Jan Sips:
Utilizing Data Mining for Predictive Modeling of Colorectal Cancer Using Electronic Medical Records. Brain Informatics and Health 2014: 132-141 - [c83]Giorgos Karafotias, Mark Hoogendoorn, Berend Weel:
Comparing generic parameter controllers for EAs. FOCI 2014: 46-53 - [c82]Giorgos Karafotias, Ágoston E. Eiben, Mark Hoogendoorn:
Generic parameter control with reinforcement learning. GECCO 2014: 1319-1326 - [c81]Diti Oudendag, Mark Hoogendoorn, Roel Jongeneel:
Agent-Based Modeling of Farming Behavior: A Case Study for Milk Quota Abolishment. IEA/AIE (1) 2014: 11-20 - [c80]Xander Wilcke, Mark Hoogendoorn, Jan Joris Roessingh:
Co-evolutionary Learning for Cognitive Computer Generated Entities. IEA/AIE (2) 2014: 120-129 - [c79]Reinier Kop, Mark Hoogendoorn, Michel C. A. Klein:
A Personalized Support Agent for Depressed Patients: Forecasting Patient Behavior Using a Mood and Coping Model. WI-IAT (3) 2014: 302-309 - 2013
- [j20]Tibor Bosse, Mark Hoogendoorn, Michel C. A. Klein, Jan Treur, C. Natalie van der Wal, Arlette van Wissen:
Modelling collective decision making in groups and crowds: Integrating social contagion and interacting emotions, beliefs and intentions. Auton. Agents Multi Agent Syst. 27(1): 52-84 (2013) - [j19]Pietro Cipresso, Mark Hoogendoorn, Michel C. A. Klein, Aleksandar Matic:
Special Issue "Technology for Mental Health". EAI Endorsed Trans. Ambient Syst. 1(2): e1 (2013) - [j18]Mark Hoogendoorn, Robbert-Jan Merk:
Utilizing theory of mind for action selection applied in the domain of fighter pilot training. Appl. Intell. 39(4): 749-760 (2013) - [j17]Mark Hoogendoorn, Michel C. A. Klein, Zulfiqar Ali Memon, Jan Treur:
Formal specification and analysis of intelligent agents for model-based medicine usage management. Comput. Biol. Medicine 43(5): 444-457 (2013) - [j16]Tibor Bosse, Fiemke Both, Rob Duell, Mark Hoogendoorn, Michel C. A. Klein, Rianne van Lambalgen, Andy van der Mee, Rogier Oorburg, Alexei Sharpanskykh, Jan Treur, Michael de Vos:
An ambient agent system assisting humans in complex tasks by analysis of a human's state and performance. Int. J. Intell. Inf. Database Syst. 7(1): 3-33 (2013) - [j15]Mark Hoogendoorn, S. Waqar Jaffry, Peter-Paul van Maanen, Jan Treur:
Modelling biased human trust dynamics. Web Intell. Agent Syst. 11(1): 21-40 (2013) - [c78]Giorgos Karafotias, Mark Hoogendoorn, A. E. Eiben:
Why parameter control mechanisms should be benchmarked against random variation. IEEE Congress on Evolutionary Computation 2013: 349-355 - [c77]Ágoston E. Eiben, Nicolas Bredèche, Mark Hoogendoorn, Jürgen Stradner, Jon Timmis, Andy M. Tyrrell, Alan F. T. Winfield:
The Triangle of Life. ECAL 2013: 1056-1063 - [c76]Giorgos Karafotias, Mark Hoogendoorn, A. E. Eiben:
Parameter control: strategy or luck? GECCO (Companion) 2013: 215-216 - [c75]Richard Koopmanschap, Mark Hoogendoorn, Jan Joris Roessingh:
Learning Parameters for a Cognitive Model on Situation Awareness. IEA/AIE 2013: 22-32 - [c74]Mark Hoogendoorn:
Predicting Human Behavior in Crowds: Cognitive Modeling versus Neural Networks. IEA/AIE 2013: 73-82 - [e1]Moonis Ali, Tibor Bosse, Koen V. Hindriks, Mark Hoogendoorn, Catholijn M. Jonker, Jan Treur:
Recent Trends in Applied Artificial Intelligence, 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Amsterdam, The Netherlands, June 17-21, 2013. Proceedings. Lecture Notes in Computer Science 7906, Springer 2013, ISBN 978-3-642-38576-6 [contents] - 2012
- [j14]Fiemke Both, Mark Hoogendoorn, Andy van der Mee, Jan Treur, Michael de Vos:
An intelligent agent model with awareness of workflow progress. Appl. Intell. 36(2): 498-510 (2012) - [j13]Mark Hoogendoorn, S. Waqar Jaffry, Jan Treur:
Cognitive and neural modeling of dynamics of trust in competitive trustees. Cogn. Syst. Res. 14(1): 60-83 (2012) - [j12]Tibor Bosse, Mark Hoogendoorn, Zulfiqar Ali Memon, Jan Treur, Muhammad Umair:
A computational model for dynamics of desiring and feeling. Cogn. Syst. Res. 19-20: 39-61 (2012) - [j11]Tibor Bosse, Fiemke Both, Charlotte Gerritsen, Mark Hoogendoorn, Jan Treur:
Methods for model-based reasoning within agent-based Ambient Intelligence applications. Knowl. Based Syst. 27: 190-210 (2012) - [c73]Fiemke Both, Mark Hoogendoorn, Michel C. A. Klein:
Validation of a Model for Coping and Mood for Virtual Agents. IAT 2012: 382-389 - [c72]Mark Hoogendoorn, Robbert-Jan Merk:
Action Selection Using Theory of Mind: A Case Study in the Domain of Fighter Pilot Training. IEA/AIE 2012: 521-533 - [c71]Berend Weel, Mark Hoogendoorn, A. E. Eiben:
On-Line Evolution of Controllers for Aggregating Swarm Robots in Changing Environments. PPSN (2) 2012: 245-254 - [p1]Mark Hoogendoorn, Pieter Huibers, Rianne van Lambalgen, Jan Joris Roessingh:
A Model of Team Decision Making Using Situation Awareness. Modern Advances in Intelligent Systems and Tools 2012: 113-120 - 2011
- [j10]Mark Hoogendoorn, Catholijn M. Jonker, Jan Treur:
A generic architecture for redesign of organizations triggered by changing environmental circumstances. Comput. Math. Organ. Theory 17(2): 119-151 (2011) - [j9]Tibor Bosse, Fiemke Both, Mark Hoogendoorn, S. Waqar Jaffry, Rianne van Lambalgen, Rogier Oorburg, Alexei Sharpanskykh, Jan Treur, Michael de Vos:
Design and Validation of a Model for a Human's Functional State and Performance. Int. J. Model. Simul. Sci. Comput. 2(4) (2011) - [j8]Tibor Bosse, Mark Hoogendoorn, Michel C. A. Klein, Jan Treur:
An ambient agent model for monitoring and analysing dynamics of complex human behaviour. J. Ambient Intell. Smart Environ. 3(4): 283-303 (2011) - [j7]Mark Hoogendoorn, Jan Treur, C. Natalie van der Wal, Arlette van Wissen:
Agent-Based Modelling of the Emergence of Collective States Based on Contagion of Individual States in Groups. Trans. Comput. Collect. Intell. 3: 152-179 (2011) - [j6]Tibor Bosse, Charlotte Gerritsen, Mark Hoogendoorn, S. Waqar Jaffry, Jan Treur:
Agent-based vs. population-based simulation of displacement of crime: A comparative study. Web Intell. Agent Syst. 9(2): 147-160 (2011) - [c70]Fiemke Both, Mark Hoogendoorn, Rianne van Lambalgen, Rogier Oorburg, Michael de Vos:
Performance Measures to Enable Agent-Based Support in Demanding Circumstances. HCI (20) 2011: 578-587 - [c69]Ard C. M. Al, Mark Hoogendoorn:
Moving Target Search Using Theory of Mind. IAT 2011: 66-71 - [c68]Mark Hoogendoorn, S. Waqar Jaffry, Peter-Paul van Maanen, Jan Treur:
Modeling and Validation of Biased Human Trust. IAT 2011: 256-263 - [c67]Mark Hoogendoorn, Bas W. Knopper, Andy van der Mee:
An Agent-Based Architecture for Model-Based Diagnosis Using Observation Cost. IAT 2011: 415-420 - [c66]Fiemke Both, Mark Hoogendoorn:
Utilization of a Virtual Patient Model to Enable Tailored Therapy for Depressed Patients. ICONIP (3) 2011: 700-710 - [c65]Tibor Bosse, Mark Hoogendoorn, Michel C. A. Klein, Jan Treur, C. Natalie van der Wal:
Agent-Based Analysis of Patterns in Crowd Behaviour Involving Contagion of Mental States. IEA/AIE (2) 2011: 566-577 - [c64]Mark Hoogendoorn, S. Waqar Jaffry, Peter-Paul van Maanen:
Validation and Verification of Agent Models for Trust: Independent Compared to Relative Trust. IFIPTM 2011: 35-50 - [c63]