Publications for Andrea Soltoggio
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Journal Articles
Ben-Iwhiwhu, E, Nath, S, Pilly, P, Kolouri, S,
Soltoggio, A (2023)
Lifelong reinforcement learning with modulating masks,
Transactions on Machine Learning Research, pp.1-23.
Baker, MM, New, A, Aguilar-Simon, M, Al-Halah, Z, Arnold, SMR, Ben-Iwhiwhu, E, Brna, AP, Brooks, E, Brown, RC, Daniels, Z, Daram, A, Delattre, F, Dellana, R, Eaton, E, Fu, H, Grauman, K, Hostetler, J, Iqbal, S, Kent, C, Ketz, N, Kolouri, S, Konidaris, G, Kudithipudi, D, Learned-Miller, E, Lee, S, Littman, ML, Madireddy, S, Mendez, JA, Nguyen, EQ, Piatko, C, Pilly, PK, Raghavan, A, Rahman, A, Ramakrishnan, SK, Ratzlaff, N,
Soltoggio, A, Stone, P, Sur, I, Tang, Z, Tiwari, S, Vedder, K, Wang, F, Xu, Z, Yanguas-Gil, A, Yedidsion, H, Yu, S, Vallabha, GK (2023)
A domain-agnostic approach for characterization of lifelong learning systems,
Neural Networks, 160, pp.274-296, ISSN: 0893-6080. DOI:
10.1016/j.neunet.2023.01.007.
Skarysz, A, Salman, D, Eddleston, M, Sykora, M, Hunsicker, E, Nailon, WH, Darnley, K, McLaren, DB, Thomas, P,
Soltoggio, A (2022)
Fast and automated biomarker detection in breath samples with machine learning,
PLoS ONE, 17(4), e0265399, DOI:
10.1371/journal.pone.0265399.
Ben-Iwhiwhu, E, Dick, J, Ketz, NA, Pilly, PK,
Soltoggio, A (2022)
Context meta-reinforcement learning via neuromodulation,
Neural Networks, 152, pp.70-79, ISSN: 0893-6080. DOI:
10.1016/j.neunet.2022.04.003.
Kudithipudi, D, Aguilar-Simon, M, Babb, J, Bazhenov, M, Blackiston, D, Bongard, J, Brna, AP, Raja, SC, Cheney, N, Clune, J, Daram, A, Fusi, S, Helfer, P, Kay, L, Ketz, N, Kira, Z, Kolouri, S, Krichmar, JL, Kriegman, S, Levin, M, Madireddy, S, Manicka, S, Marjaninejad, A, McNaughton, B, Miikkulainen, R, Navratilova, Z, Pandit, T, Parker, A, Pilly, PK, Risi, S, Sejnowski, TJ,
Soltoggio, A, Soures, N, Tolias, AS, Urbina-Meléndez, D, Valero-Cuevas, FJ, van de Ven, GM, Vogelstein, JT, Wang, F, Weiss, R, Yanguas-Gil, A, Zou, X, Siegelmann, H (2022)
Biological underpinnings for lifelong learning machines,
Nature Machine Intelligence, 4(3), pp.196-210, DOI:
10.1038/s42256-022-00452-0.
Ladosz, P, Ben-Iwhiwhu, E, Dick, J, Ketz, N, Kolouri, S, Krichmar, JL, Pilly, PK,
Soltoggio, A (2021)
Deep Reinforcement Learning With Modulated Hebbian Plus Q-Network Architecture,
IEEE Transactions on Neural Networks and Learning Systems, ISSN: 2162-237X. DOI:
10.1109/TNNLS.2021.3110281.
Fratczak, P, Goh, Y, Kinnell, P, Justham, L,
Soltoggio, A (2021)
Robot apology as a post-accident trust-recovery control strategy in industrial human-robot interaction,
International Journal of Industrial Ergonomics, 82, 103078, ISSN: 0169-8141. DOI:
10.1016/j.ergon.2020.103078.
Farley, S, Hodgkinson, JEA, Gordon, OM, Turner, J,
Soltoggio, A, Moriarty, PJ, Hunsicker, E (2020)
Improving the segmentation of scanning probe microscope images using convolutional neural networks,
Machine Learning: Science and Technology, 2(1), 015015, ISSN: 2632-2153. DOI:
10.1088/2632-2153/abc81c.
Dick, J, Ladosz, P, Ben-Iwhiwhu, E, Shimadzu, H, Kinnell, P, Pilly, PK, Kolouri, S,
Soltoggio, A (2020)
Detecting changes and avoiding catastrophic forgetting in dynamic partially observable environments,
Frontiers in Neurorobotics, 14, 578675, ISSN: 1662-5218. DOI:
10.3389/fnbot.2020.578675.
Jiang, L, Hu, Y, Xia, X, Liang, Q,
Soltoggio, A, Kabir, S (2020)
A multi-scale mapping approach based on a deep learning CNN model for reconstructing high-resolution urban DEMs,
Water, 12(5), 1369, DOI:
10.3390/w12051369.
Alkhalifah, Y, Phillips, I,
Soltoggio, A, Darnley, K, Nailon, WH, McLaren, D, Eddleston, M, Thomas, CLP, Salman, D (2019)
VOCCluster: Untargeted Metabolomics Feature Clustering Approach for Clinical Breath Gas Chromatography/Mass Spectrometry Data,
Analytical Chemistry, 92(4), pp.2937-2945, ISSN: 0003-2700. DOI:
10.1021/acs.analchem.9b03084.
Hu, Y,
Soltoggio, A, Lock, R, Carter, S (2018)
A Fully Convolutional Two-Stream Fusion Network for Interactive Image Segmentation,
Neural Networks, 109, pp.31-42, ISSN: 1879-2782. DOI:
10.1016/j.neunet.2018.10.009.
Soltoggio, A, Stanley, KO, Risi, S (2018)
Born to learn: the inspiration, progress, and future of evolved plastic artificial neural networks,
Neural Networks, 108, pp.48-67, ISSN: 0893-6080. DOI:
10.1016/j.neunet.2018.07.013.
Turner, J, Meng, Q, Schaefer, G, Whitbrook, A,
Soltoggio, A (2017)
Distributed task rescheduling with time constraints for the optimization of total task allocations in a multirobot system,
IEEE Transactions on Cybernetics, 48(9), pp.2583-2597, ISSN: 2168-2267. DOI:
10.1109/TCYB.2017.2743164.
Soltoggio, A and van der Velde, F (2015)
Editorial: Neural plasticity for rich and uncertain robotic information streams,
Frontiers in Neurorobotics, 9(12), ISSN: 1662-5218. DOI:
10.3389/fnbot.2015.00012.
Soltoggio, A (2014)
Short-term plasticity as cause-effect hypothesis testing in distal reward learning,
Biological Cybernetics, 109(1), pp.75-94, DOI:
10.1007/s00422-014-0628-0.
Soltoggio, A and Lemme, A (2013)
Movement primitives as a robotic tool to interpret trajectories through learning-by-doing,
International Journal of Automation and Computing, 10(5), pp.375-386, ISSN: 1476-8186. DOI:
10.1007/s11633-013-0734-9.
Soltoggio, A and Steil, J (2013)
Solving the distal reward problem with rare correlations,
Neural Computation, 25(4), pp.940-978, ISSN: 0899-7667. DOI:
10.1162/NECO_a_00419.
Soltoggio, A, Lemme, A, Reinhart, F, Steil, J (2013)
Rare neural correlations implement robotic conditioning with delayed rewards and disturbances,
Frontiers in Neurorobotics, 7(APR), DOI:
10.3389/fnbot.2013.00006.
Soltoggio, A and Stanley, KO (2012)
From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation,
Neural Networks, 34, pp.28-41, ISSN: 0893-6080. DOI:
10.1016/j.neunet.2012.06.005.
Soltoggio, A and Steil, J (2012)
How rich motor skills empower robots at last: Insights and progress of the AMARSi project,
Kuenstlich Intelligenz, 26(4), pp.407-410, DOI:
10.1007/s13218-012-0192-5.
Conferences
Nath, S, Peridis, C, Ben-Iwhiwhu, E, Liu, X, Dora, S, Liu, C, Kolouri, S,
Soltoggio, A (2023)
Sharing lifelong reinforcement learning knowledge via modulating masks. In
Second Conference on Lifelong Learning Agents (CoLLAs 2023); Proceedings of the Second Conference on Lifelong Learning Agents (CoLLAs 2023), Montreal, Canada.
Krichmar, JL, Ketz, NA, Pilly, PK,
Soltoggio, A (2022)
Flexible Path Planning in a Spiking Model of Replay and Vicarious Trial and Error. In
, pp.177-189, ISBN: 9783031167690. DOI:
10.1007/978-3-031-16770-6_15.
Fratczak, P, Goh, YM, Kinnell, P, Justham, L,
Soltoggio, A (2020)
Virtual Reality Study of Human Adaptability in Industrial Human-Robot Collaboration. In
2020 IEEE International Conference on Human-Machine Systems (ICHMS). DOI:
10.1109/ichms49158.2020.9209558.
Ben-Iwhiwhu, E, Ladosz, P, Dick, J, Chen, W-H, Pilly, P,
Soltoggio, A (2020)
Evolving inborn knowledge for fast adaptation in dynamic POMDP problems. In
Genetic and Evolutionary Computation Conference (GECCO 2020); GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, Electronic-only conference, pp.280-288, ISBN: 9781450371285. DOI:
10.1145/3377930.3390214.
Kolouri, S, Ketz, NA, Pilly, PK,
Soltoggio, A (2020)
Sliced Cramer synaptic consolidation for preserving deeply learned representations. In
International Conference On Learning Representations (ICLR 2020), Addis Abeba, Ethiopia.
Fratczak, P, Goh, YM, Kinnell, P,
Soltoggio, A, Justham, L (2019)
Understanding Human Behaviour in Industrial Human-Robot Interaction by Means of Virtual Reality. In
HTTF 2019: Halfway to the Future, Proceedings of the Halfway to the Future Symposium 2019. DOI:
10.1145/3363384.3363403.
Turner, J, Meng, Q, Schaefer, G,
Soltoggio, A (2018)
Fast consensus for fully distributed multi-agent task allocation. In
The 33rd ACM Symposium On Applied Computing, Pau, France,ISBN: 9781450351911. DOI:
10.1145/3167132.3167224.
Turner, J, Meng, Q, Schaefer, G,
Soltoggio, A (2018)
Distributed strategy adaptation with a prediction function in multi-agent task allocation. In
17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018), Stockholm, Sweden,ISBN: 9781450356497.
Mucha, A, Alkhalifah, Y, Darnley, K, Eddleston, M, Hu, Y, McLaren, DB, Nailon, WH, Salman, D, Sykora, M, Thomas, P,
Soltoggio, A (2018)
Convolutional neural networks for automated targeted analysis of gas chromatography-mass spectrometry data. In
International Joint Conference on Neural Networks, Rio de Janeiro, Brazil,ISBN: 9781509060146. DOI:
10.1109/IJCNN.2018.8489539.
Bahroun, Y and
Soltoggio, A (2017)
Online representation learning with single and multi-layer Hebbian networks for image classification. In
International Conference on Artificial Neural Networks, Alghero, Italy,ISBN: 9783319686110. DOI:
10.1007/978-3-319-68612-7.
Bahroun, Y, Hunsicker, E,
Soltoggio, A (2017)
Building efficient deep Hebbian networks for image classification tasks. In
International Conference on Artificial Neural Networks, Alghero, Italy,ISBN: 9783319686110.
Bahroun, Y, Hunsicker, E,
Soltoggio, A (2017)
Neural networks for efficient nonlinear online clustering. In
International Conference on Neural Information Processing, Guangzhou, China,ISBN: 9783319700861. DOI:
10.1007/978-3-319-70087-8_34.
Soltoggio, A, Blasing, B, Moscatelli, A, Schack, T (2015)
The Aikido inspiration to safety and efficiency: an investigation on forward roll impact forces. In
10th International Symposium on Computer Science in Sports, Loughborough, UK, pp.119-127, ISBN: 9783319245607. DOI:
10.1007/978-3-319-24560-7_15.
Fontana, A,
Soltoggio, A, Wrobel, B (2014)
POET: an evo-devo method to optimize the weights of a large artificial neural networks. In
Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFE XIV). Cambridge, MA: MIT Press, 2014, New York, USA, pp.1-8, DOI:
10.7551/978-0-262-32621-6-ch073.
Pugh, JK,
Soltoggio, A, Stanley, KO (2014)
Real-time hebbian learning from autoencoder features for control tasks. In
Fourteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFE XIV), NYC, USA. DOI:
10.7551/978-0-262-32621-6-ch034.
Fontana, A,
Soltoggio, A, Wróbel, B (2014) POET: An evo-devo method to optimize the weights of large artificial neural networks. In
, Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014, pp.447-454, ISBN: 9780262326216.
Soltoggio, A, Reinhart, F, Lemme, A, Steil, J (2013)
Learning the rules of a game: neural conditioning in human-robot interaction with delayed rewards. In
2013 IEEE 3rd Joint International Conference on Development and Learning and Epigenetic Robotics, ICDL 2013 - Electronic Conference Proceedings,, DOI:
10.1109/DevLrn.2013.6652572.
Soltoggio, A, Lemme, A, Steil, J (2012)
Using movement primitives in interpreting and decomposing complex trajectories in learning-by-doing. In
2012 IEEE International Conference on Robotics and Biomimetics, ROBIO 2012 - Conference Digest, pp.1427-1433, ISBN: 978-1-4673-2125-9. DOI:
10.1109/ROBIO.2012.6491169.
Jones, BH,
Soltoggio, A, Sendhoff, B, Yao, X (2011)
Evolution of neural symmetry and its coupled alignment to body plan morphology. In
Genetic and Evolutionary Computation Conference, GECCO'11, pp.235-242, ISBN: 9781450305570. DOI:
10.1145/2001576.2001609.
Soltoggio, A and Jones, BH (2009)
Novelty of behaviour as a basis for the neuro-evolution of operant reward learning. In
Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009, pp.169-176, ISBN: 9781605583259. DOI:
10.1145/1569901.1569925.
Dürr, P, Mattiussi, C,
Soltoggio, A, Floreano, D (2008)
Evolvability of Neuromodulated Learning for Robots. In
2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems (LAB-RS). DOI:
10.1109/lab-rs.2008.22.
Soltoggio, A (2008) Phylogenetic Onset and Dynamics of Neuromodulation in Learning Neural Models. In
Young Physiologists’ Symposium, Cambridge, UK, pp.1-1.
Soltoggio, A, Bullinaria, JA, Mattiussi, C, Dürr, P, Floreano, D (2008)
Evolutionary advantages of neuromodulated plasticity in dynamic, reward-based scenarios. In
Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems, Winchester, UK, pp.569-576, ISBN: 978-0-262-28719-7.
Soltoggio, A (2008)
Neural plasticity and minimal topologies for reward-based learning. In
Proceedings - 8th International Conference on Hybrid Intelligent Systems, HIS 2008, pp.637-642, ISBN: 9780769533261. DOI:
10.1109/HIS.2008.155.
Soltoggio, A, Durr, P, Mattiussi, C, Floreano, D (2007)
Evolving neuromodulatory topologies for reinforcement learning-like problems. In
2007 IEEE Congress on Evolutionary Computation. DOI:
10.1109/cec.2007.4424781.
Soltoggio, A (2006)
A simple line search operator for ridged landscapes. In
GECCO06: Genetic and Evolutionary Computation Conference, Proceedings of the 8th annual conference on Genetic and evolutionary computation. DOI:
10.1145/1143997.1144089.
Soltoggio, A (2005)
An enhanced GA to improve the search process reliability in tuning of control systems. In
GECCO05: Genetic and Evolutionary Computation Conference, Proceedings of the 7th annual conference on Genetic and evolutionary computation. DOI:
10.1145/1068009.1068365.
Soltoggio, A (2004) GP and GA in the design of a constrained control system with disturbance rejection. In
, IEEE International Symposium on Intelligent Control - Proceedings, pp.477-482.
Soltoggio, A (2004)
A Comparison of Genetic Programming and Genetic Algorithms in the Design of a Robust, Saturated Control System. In
, pp.174-185, ISBN: 9783540223436. DOI:
10.1007/978-3-540-24855-2_16.
Books
Soltoggio, A and van der Velde, F (2016)
Neural plasticity for rich and uncertain robotic information streams, © Copyright 2007-2016 Frontiers Media SA, ISBN: 978-2-88919-995-2. DOI:
10.3389/978-2-88919-995-2.
Chung, P,
Soltoggio, A, Dawson, C, Meng, Q, Pain, M (ed) (2015)
Proceedings of the 10th International Symposium on Computer Science in Sports (ISCSS), Springer, ISBN: 978-3-319-24558-4. DOI:
10.1007/978-3-319-24560-7.
Chapters
Bahroun, Y, Hunsicker, E,
Soltoggio, A (2017)
Building Efficient Deep Hebbian Networks for Image Classification Tasks. In
Artificial Neural Networks and Machine Learning – ICANN 2017, Springer International Publishing, pp.364-372, ISBN: 9783319685991. DOI:
10.1007/978-3-319-68600-4_42.
Bahroun, Y and
Soltoggio, A (2017)
Online Representation Learning with Single and Multi-layer Hebbian Networks for Image Classification. In
Artificial Neural Networks and Machine Learning – ICANN 2017, Springer International Publishing, pp.354-363, ISBN: 9783319685991. DOI:
10.1007/978-3-319-68600-4_41.
Digital/Visual Products
Soltoggio, A and Lemme, A (2013)
Movement primitives as a robotic tool to interpret trajectories through learning-by-doing.
Soltoggio, A (2013)
From Modulated Hebbian Plasticity to Simple Behavior Learning through Noise and Weight Saturation.
Soltoggio, A, Lemme, Andre, Reinhart, Felix, (2013)
Neural learning with robot: copying with delayed rewards and disturbances.
Soltoggio, A, Lemme, Andre, Steil, Jochen, (2012)
Iterative Decomposition of Complex Trajectories.
Reports
Soltoggio, A, Steil, Jochen, Kappel, David, Pecevski, Dejan, Rueckert, Elmar, Maass, Wolfgang, (2014) Technical report on Meta-learning Approaches - Adaptive Modular Architectures for Rich Motor Skills (ICT-248311), European Union.
Soltoggio, A (2003) A Case Study of a Genetically Evolved Control System, Norwegian University of Science and Technology.
Software
Soltoggio, A, Ladosz, P, Ben-Iwhiwhu, E, Dick, J (2020)
Deep Reinforcement Learning with Modulated Hebbian plus Q Network Architecture, GitHub.
Hu, Y,
Soltoggio, A, Lock, R, Carter, S (2018)
A Fully Convolutional Two-Stream Fusion Network for Interactive Image Segmentation, GitHub.
Soltoggio, A (2014)
Short-term plasticity as cause-effect hypothesis testing in distal reward learning, Andrea Soltoggio.
Soltoggio, A and Lemme, A, (2012)
Using movement primitives in interpreting and decomposing complex trajectories in learning-by-doing, Research Institute for Cognition and Robotics.
Soltoggio, A (2012)
From Modulated Hebbian Plasticity to Simple Behavior Learning through Noise and Weight Saturation - Matlab code, Andrea Soltoggio.
Soltoggio, A and Steil, Jochen, (2012)
Solving the distal reward problem with rare correlations, Andrea Soltoggio.
Soltoggio, A (Accepted for publication) Matlab code: From Modulated Hebbian Plasticity to Simple Behavior Learning through Noise and Weight Saturation.
Datasets
Ben-Iwhiwhu, E, Dick, J, Ketz, NA, Pilly, PK,
Soltoggio, A (2023)
Supplementary information files for Context meta-reinforcement learning via neuromodulation, DOI:
10.17028/rd.lboro.23592483.
Turner, J, Meng, Q, Schaefer, G, Whitbrook, A,
Soltoggio, A (2018)
Supplementary files for article Distributed Task Rescheduling With Time Constraints for the Optimization of Total Task Allocations in a Multirobot System, DOI:
10.17028/rd.lboro.5993038.