Loughborough University
Leicestershire, UK
LE11 3TU
+44 (0)1509 263171
Loughborough University

Loughborough University Research Publications


Publications for Andrea Soltoggio

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Journal Articles

Hu, Y, Soltoggio, A, Lock, R, Carter, S (Accepted for publication) A Fully Convolutional Two-Stream Fusion Network for Interactive Image Segmentation, Neural Networks, ISSN: 1879-2782.

Soltoggio, A, Stanley, KO, Risi, S (Accepted for publication) Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks, Neural Networks, ISSN: 1879-2782.

Turner, J, Meng, Q, Schaefer, G, Whitbrook, A, Soltoggio, A (2017) Distributed Task Rescheduling With Time Constraints for the Optimisation of Total Task Allocations in a Multi-Robot System, IEEE Transactions on Cybernetics, pp.1-15, ISSN: 2168-2275. DOI: 10.1109/TCYB.2017.2743164.

Soltoggio, A and van der Velde, F (2015) Neural plasticity for rich and uncertain robotic information streams, Frontiers in Neurorobotics, 9(12), ISSN: 1662-5218. Full text: http://journal.frontiersin.org/article/10.3389/fnbot.2015.00012/full. 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, Full text: http://link.springer.com/article/10.1007/s00422-014-0628-0#page-1. 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, Lemme, A, Reinhart, F, Steil, J (2013) Rare neural correlations implement robotic conditioning with delayed rewards and disturbances, Frontiers in Neurorobotics, Frontiers in Neurorobotics, 6, pp.1-18, DOI: 10.3389/fnbot.2013.00006.

Soltoggio, A and Steil, JJ (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 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.

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.



Conferences

Skarysz, A, Alkhalifah, Y, Darnley, K, Eddleston, M, Hu, Y, McLaren, DB, Nailon, WH, Salman, D, Sykora, M, Thomas, CLP, 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.

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.

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.

Bahroun, Y, Hunsicker, E, Soltoggio, A (2017) Neural Networks for Efficient Nonlinear Online Clustering. In International Conference on Neural Information Processing, Guangzhou, China, pp.316-324.

Bahroun, Hunsicker, Soltoggio, A (2017) Building Efficient Deep Hebbian Networks for Image Classification Tasks. In 26th International Conference on Artificial Neural Networks, Proceedings, Alghero, Italy,ISBN: 978-3-319-68612-7. Full text: http://www.springer.com/us/book/9783319686110. DOI: 10.1007/978-3-319-68612-7.

Bahroun, Y and Soltoggio, A (2017) Online Representation Learning with Single and Multi-layer Hebbian Networks for Image Classification. In 26th International Conference on Artificial Neural Networks, Alghero, Italy,ISBN: 978-3-319-68612-7. Full text: http://www.springer.com/us/book/9783319686110. DOI: 10.1007/978-3-319-68600-4_41.

Soltoggio, A, Blaesing, 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: 978-3-319-24560-7. 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.

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.

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 International Conference on Development and Learning and Epigenetic Robotics (ICDL), 2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL). DOI: 10.1109/devlrn.2013.6652572.

Soltoggio, A, Lemme, A, Steil, JJ (2012) Using movement primitives in interpreting and decomposing complex trajectories in learning-by-doing. In 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO),ISBN: 9781467321259. DOI: 10.1109/robio.2012.6491169.

Jones, B, Soltoggio, A, Sendhoff, B, Yao, X (2011) Evolution of neural symmetry and its coupled alignment to body plan morphology. In the 13th annual conference, Proceedings of the 13th annual conference on Genetic and evolutionary computation - GECCO '11,ISBN: 9781450305570. DOI: 10.1145/2001576.2001609.

Soltoggio, A and Jones, B (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.

Soltoggio, A (2008) Phylogenetic Onset and Dynamics of Neuromodulation in Learning Neural Models. In Young Physiologists’ Symposium, Cambridge, UK, pp.1-1.

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),ISBN: 9780769532721. DOI: 10.1109/lab-rs.2008.22.

Soltoggio, A (2008) Neural Plasticity and Minimal Topologies for Reward-Based Learning. In 2008 8th International Conference on Hybrid Intelligent Systems (HIS), 2008 Eighth International Conference on Hybrid Intelligent Systems. DOI: 10.1109/his.2008.155.

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, Durr, P, Mattiussi, C, Floreano, D (2007) Evolving neuromodulatory topologies for reinforcement learning-like problems. In 2007 IEEE Congress on Evolutionary Computation,ISBN: 9781424413409. DOI: 10.1109/cec.2007.4424781.

Soltoggio, A (2006) A simple line search operator for ridged landscapes. In , GECCO 2006 - Genetic and Evolutionary Computation Conference, pp.503-504, ISBN: 1595931864.

Soltoggio, A (2005) An enhanced GA to improve the search process reliability in tuning of control systems. In the 2005 conference, Proceedings of the 2005 conference on Genetic and evolutionary computation - GECCO '05,ISBN: 1595930108. 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, (ed) (2016) Neural Plasticity for Rich and Uncertain Robotic Information Streams,ISBN: 978-2-88919-995-2. DOI: 10.3389/978-2-88919-995-2.

Soltoggio, A and van der Velde, F, (ed) (2016) Neural Plasticity for Rich and Uncertain Robotic Information Streams,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. Full text: http://link.springer.com/book/10.1007/978-3-319-24560-7. DOI: 10.1007/978-3-319-24560-7.

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. Full text: http://link.springer.com/book/10.1007/978-3-319-24560-7. DOI: 10.1007/978-3-319-24560-7.



Other

Soltoggio, A, Stanley, KO, Risi, S (2018) Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks, © 2018 Elsevier Ltd Biological neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifelong learning. The interplay of these elements leads to the emergence of biological intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) employ simulated evolution in-silico to breed plastic neural networks with the aim to autonomously design and create learning systems. EPANN experiments evolve networks that include both innate properties and the ability to change and learn in response to experiences in different environments and problem domains. EPANNs’ aims include autonomously creating learning systems, bootstrapping learning from scratch, recovering performance in unseen conditions, testing the computational advantages of particular neural components, and deriving hypotheses on the emergence of biological learning. Thus, EPANNs may include a large variety of different neuron types and dynamics, network architectures, plasticity rules, and other factors. While EPANNs have seen considerable progress over the last two decades, current scientific and technological advances in artificial neural networks are setting the conditions for radically new approaches and results. Exploiting the increased availability of computational resources and of simulation environments, the often challenging task of hand-designing learning neural networks could be replaced by more autonomous and creative processes. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and possible developments are presented. DOI: 10.1016/j.neunet.2018.07.013.



Posters

Soltoggio, A (2013) Short and long term plasticity as cause-effect hypothesis testing in robotic ambiguous scenarios, Bernstein Sparks Workshop. NeuroEnginneering the Brain: from Neuroscience to Robotics ..and back.



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.



Thesis/Dissertation

Soltoggio, A (2008) Evolutionary and Computational Advantages of Neuromodulated Plasticity.

Soltoggio, A (2004) Evolutionary Algorithms in the Design and Tuning of a Control System.



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