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Hart, E., Sim, K., Kamimura, K., Meredieu, C., Guyon, D., & Gardiner, B. A. (2019). Use of Machine Learning Techniques to Model Wind Damage to Forests. Agricultural and forest meteorology, 265, 16-29. doi: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...
Hart, E., Steyven, A. S. W., & Paechter, B. (in press). Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity Algorithm. In Proceedings of GECCO 2018
The presence of functionality diversity within a group has been demonstrated to lead to greater robustness, higher performance and increased problem-solving ability in a broad...
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),doi: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. doi: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. (2015). Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm. In Parallel Problem Solving from Nature – PPSN XIV; Lecture Notes in Computer Science, 921-931. doi: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...
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...
Capodieci, N., Hart, E. & Cabri, G. (2013). Idiotypic networks for evolutionary controllers in virtual creatures. In Sayama, H., Rieffel, J., Risi, S., Doursat, R. & Lipson, H. (Eds.). Artificial Life 14: Proceedings of ALife, 14th International Conference on the Synthesis and Simulation of Living Systems, 192-199. doi:10.7551/978-0-262-32621-6-ch032
We propose a novel method for evolving adaptive locomotive strategies for virtual limbless creatures that addresses both functional and non-functional requirements, respective...
Capodieci, N., Hart, E. & Cabri, G. (2013). Artificial Immune System driven evolution in Swarm Chemistry. In Proceedings of IEEE SASO 2014, 40-49. doi: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...
Capodieci, N., Hart, E. & Cabri, G. (2012). Designing self-aware adaptive systems: from autonomic computing to cognitive immune networks. In Proceedings of SASO Workshops 2013doi:10.1109/SASOW.2013.17
An autonomic system is composed of ensembles of heterogeneous autonomic components in which large sets of components are dynamically added and removed. Nodes within such an en...
Capodieci, N., Hart, E. & Cabri, G. (2012). An immune network approach for self-adaptive ensembles of autonomic components: a case study in swarm robotics. In Liò, P., Miglino, O., Nicosia, G., Nolfi, S. & Pavone, M. (Eds.). Advances in Artifical Life, Proceedings of ECAL 2013, 864-871. doi:10.7551/978-0-262-31709-2-ch127
We describe an immune inspired approach to achieve self-expression within an ensemble, i.e. enabling an ensemble of autonomic components to dynamically change their coordinati...
McEwan, C., Hart, E., & Paechter, B. (2007). Boosting the Immune System. In Artificial Immune Systems, 316-327. doi:10.1007/978-3-540-85072-4_28
Much of contemporary research in Artificial Immune Systems (AIS) has partitioned into either algorithmic machine learning and optimisation, or modelling biologically plausible...
Hart, E., Ross, P., Webb, A., & Lawson, A. (2003). A role for immunology in 'next generation' robots. In J. Timmis, P. Bentley, & E. Hart (Eds.), Artificial Immune Systems. ICARIS 2003, 46-56. doi:10.1007/978-3-540-45192-1_5
Much of current robot research is about learning tasks in which the task to be achieved is pre-specified, a suitable technology for the task is chosen and the learning process...
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
Core44, Room C44, Merchiston Campus
7 June 2018
15:00, Core44, Room C44, Merchiston Campus
20 March 2018
Core44, Room C44 ,Merchiston Campus
28 September 2017
Core44, room 44, Merchiston Campus
7 June 2017