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Hart, E., Sim, K., Kamimura, K., Meredieu, C., Guyon, D., & Gardiner, B. (2019). Use of machine learning techniques to model wind damage to forests. Agricultural and forest meteorology, 265, 16-29. https://doi.org/10.1016/j.agrformet.2018.10.022
This paper tested the ability of machine learning techniques, namely artificial neural networks and random forests, to predict the individual trees within a forest most at r...
Martí, L., Segredo, E., Sánchez-Pi, N., & Hart, E. (in press). Selection methods and diversity preservation in many-objective evolutionary algorithms. Data Technologies and Applications, ISSN 2514-9288
Purpose – One of the main components of multi-objective, and therefore, many-objective evolutionary algorithms is the selection mechanism. It is responsible for performing two...
Capodieci, N., Hart, E., & Cabri, G. (2016). Artificial Immunology for Collective Adaptive Systems Design and Implementation. ACM transactions on autonomous and adaptive systems, 11(2), (1-25). doi:10.1145/2897372. ISSN 1556-4665
Distributed autonomous systems consisting of large numbers of components with no central control point need to be able to dynamically adapt their control mechanisms to deal wi...
Hale, M. F., Buchanan, E., Winfield, A. F., Timmis, J., Hart, E., Eiben, A. E., …Tyrrell, A. M. (2019). The ARE Robot Fabricator: How to (Re)produce Robots that Can Evolve in the Real World. In ALIFE 2019: The 2019 Conference on Artificial Life, 95-102. https://doi.org/10.1162/isal_a_00147
The long term vision of the Autonomous Robot Evolution (ARE) project is to create an ecosystem of both virtual and physical robots with evolving brains and bodies. One of the ...
Akram, R. N., Markantonakis, K., Mayes, K., Habachi, O., Sauveron, D., Steyven, A., & Chaumette, S. (2017). Security, privacy and safety evaluation of dynamic and static fleets of drones. In 2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC),https://doi.org/10.1109/dasc.2017.8101984
Interconnected everyday objects, either via public or private networks, are gradually becoming reality in modern life -- often referred to as the Internet of Things (IoT) or C...
Steyven, A., Hart, E., & Paechter, B. (2017). An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm robotics. In GECCO '17 Proceedings of the Genetic and Evolutionary, (155-162). https://doi.org/10.1145/3071178.3071232
A robotic swarm that is required to operate for long periods in a potentially unknown environment can use both evolution and individual learning methods in order to adapt. How...
Steyven, A., Hart, E., & Paechter, B. (2016). Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm. In Parallel Problem Solving from Nature – PPSN XIV; Lecture Notes in Computer Science, 921-931. https://doi.org/10.1007/978-3-319-45823-6_86
It is well known that in open-ended evolution, the nature of the environment plays in key role in directing evolution. However, in Evolutionary Robotics, it is often unclear e...
Gkatzia, D., Rieser, V., Bartie, P., & Mackaness, W. (2015). From the Virtual to the RealWorld: Referring to Objects in Real-World Spatial Scenes. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 1936-1942. https://doi.org/10.18653/v1/d15-1224
Predicting the success of referring expressions (RE) is vital for real world applications such as navigation systems. Traditionally, research has focused on studying Referring...
Gkatzia, D., Cercas Curry, A., Rieser, V., & Lemon, O. (2015). A Game-Based Setup for Data Collection and Task-Based Evaluation of Uncertain Information Presentation. In Proceedings of the 15th European Workshop on Natural Language Generation, 112-113. https://doi.org/10.18653/v1/w15-4720
Decision-making is often dependent on uncertain data, e.g. data associated with confidence scores, such as probabilities. A concrete example of such data is weather data. We w...
Gkatzia, D., & Mahamood, S. (2015). A Snapshot of NLG Evaluation Practices 2005 - 2014. https://doi.org/10.18653/v1/w15-4708
In this paper we present a snapshot of endto-end NLG system evaluations as presented in conference and journal papers1 over the last ten years in order to better understand th...
Cercas Curry, A., Gkatzia, D., & Rieser, V. (2015). Generating and Evaluating Landmark-Based Navigation Instructions in Virtual Environments. In Proceedings of the 15th European Workshop on Natural Language Generation, 90-94. https://doi.org/10.18653/v1/w15-4715
Referring to landmarks has been identified to lead to improved navigation instructions. However, a previous corpus study suggests that human “wizards” also choose to refer to ...
Steyven, A., Hart, E., & Paechter, B. (2015). The Cost of Communication: Environmental Pressure and Survivability in mEDEA. In Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference - GECCO Companion '15, 1239-1240. doi:10.1145/2739482.2768489
We augment the mEDEA algorithm to explicitly account for
the costs of communication between robots. Experimental
results show that adding a costs for communication exerts
Hart, E., Steyven, A., & Paechter, B. (2015). Improving survivability in environment-driven distributed evolutionary algorithms through explicit relative fitness and fitness proportionate communication. In Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO '15, 169-176. doi:10.1145/2739480.2754688
Ensuring the integrity of a robot swarm in terms of maintaining
a stable population of functioning robots over long
periods of time is a mandatory prerequisite for building mo...
Gkatzia, D., Hastie, H., & Lemon, O. (2014). Comparing Multi-label Classification with Reinforcement Learning for Summarisation of Time-series Data. In Proceedings of the Conference Volume 1: Long Papers, 1231-1240. https://doi.org/10.3115/v1/p14-1116
We present a novel approach for automatic report generation from time-series data, in the context of student feedback generation. Our proposed methodology treats content selec...
Capodieci, N., Hart, E., & Cabri, G. (2014). Artificial Immune System driven evolution in Swarm Chemistry. In Proceedings of IEEE SASO 2014, 40-49. https://doi.org/10.1109/SASO.2014.16
Morphogenetic engineering represents an interesting field in which models, frameworks and algorithms can be tested in order to study how self-* properties and emergent behavio...
Swarm robotics is a special case within the general field of robotics. The distributed nature makes it...
Prof. Emma Hart, Dr Andreas Steyven and Prof. Ben Paechter have been nominated for a prestigious best paper award at the GECCO 18, Kyoto, Japan for new work in evolving a diverse team of swarm robot...
14 July 2018