Skip to main content

Research Repository

Advanced Search

The ARE Robot Fabricator: How to (Re)produce Robots that Can Evolve in the Real World (2019)
Conference Proceeding
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 major challenges for such a vision is the need to construct many unique ind... Read More about The ARE Robot Fabricator: How to (Re)produce Robots that Can Evolve in the Real World.

Use of machine learning techniques to model wind damage to forests (2018)
Journal Article
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 risk of damage in storms. Models based on these techniques were developed i... Read More about Use of machine learning techniques to model wind damage to forests.

Selection methods and diversity preservation in many-objective evolutionary algorithms (2018)
Journal Article
Martí, L., Segredo, E., Sánchez-Pi, N., & Hart, E. (2018). Selection methods and diversity preservation in many-objective evolutionary algorithms. Data Technologies and Applications, https://doi.org/10.1108/dta-01-2018-0009

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 main tasks simultaneously. First, it has to promote convergence by selecti... Read More about Selection methods and diversity preservation in many-objective evolutionary algorithms.

Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity Algorithm (2018)
Conference Proceeding
Hart, E., Steyven, A. S. W., & Paechter, B. (2018). Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity Algorithm. In GECCO '18 Proceedings of the Genetic and Evolutionary Computation Conference (101-108). https://doi.org/10.1145/3205455.3205481

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 range of studies that includes insect groups, human groups and swarm robot... Read More about Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity Algorithm.

Security, privacy and safety evaluation of dynamic and static fleets of drones (2017)
Conference Proceeding
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 Cyber-Physical Systems (CPS). One stand-out example are those systems based... Read More about Security, privacy and safety evaluation of dynamic and static fleets of drones.

An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm robotics (2017)
Conference Proceeding
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 Computation Conference. , (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. However, the role played by the environment in influencing the effectiveness o... Read More about An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm robotics.

Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm (2016)
Conference Proceeding
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 exactly how parameterisation of a given environment might influence the emer... Read More about Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm.

Artificial Immunology for Collective Adaptive Systems Design and Implementation (2016)
Journal Article
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. https://doi.org/10.1145/2897372

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 with an unpredictable and changing environment. Existing frameworks for engin... Read More about Artificial Immunology for Collective Adaptive Systems Design and Implementation.

Improving survivability in environment-driven distributed evolutionary algorithms through explicit relative fitness and fitness proportionate communication. (2015)
Conference Proceeding
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). https://doi.org/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 more complex systems that achieve user-defined tasks. mEDEA is an environ... Read More about Improving survivability in environment-driven distributed evolutionary algorithms through explicit relative fitness and fitness proportionate communication..

From the Virtual to the RealWorld: Referring to Objects in Real-World Spatial Scenes (2015)
Conference Proceeding
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 Expression Generation (REG) in virtual, controlled environments. In this p... Read More about From the Virtual to the RealWorld: Referring to Objects in Real-World Spatial Scenes.

Generating and Evaluating Landmark-Based Navigation Instructions in Virtual Environments (2015)
Conference Proceeding
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 street names and generate user-centric instructions. In this paper, we cond... Read More about Generating and Evaluating Landmark-Based Navigation Instructions in Virtual Environments.

A Snapshot of NLG Evaluation Practices 2005 - 2014 (2015)
Conference Proceeding
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 the nature and type of evaluations that have been undertaken. We find that re... Read More about A Snapshot of NLG Evaluation Practices 2005 - 2014.

A Game-Based Setup for Data Collection and Task-Based Evaluation of Uncertain Information Presentation (2015)
Conference Proceeding
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 will demo a game-based setup for exploring the effectiveness of different ap... Read More about A Game-Based Setup for Data Collection and Task-Based Evaluation of Uncertain Information Presentation.

The Cost of Communication: Environmental Pressure and Survivability in mEDEA (2015)
Conference Proceeding
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). https://doi.org/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 environmental pressure to implicitly select for genomes that maintain high... Read More about The Cost of Communication: Environmental Pressure and Survivability in mEDEA.

Comparing Multi-label Classification with Reinforcement Learning for Summarisation of Time-series Data (2014)
Conference Proceeding
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 selection as a multi-label (ML) classification problem, which takes as input ti... Read More about Comparing Multi-label Classification with Reinforcement Learning for Summarisation of Time-series Data.

Idiotypic networks for evolutionary controllers in virtual creatures. (2014)
Conference Proceeding
Capodieci, N., Hart, E., & Cabri, G. (2014). Idiotypic networks for evolutionary controllers in virtual creatures. In H. Sayama, J. Rieffel, S. Risi, R. Doursat, & H. Lipson (Eds.), Artificial Life 14: Proceedings of ALife, 14th International Conference on the Synthesis and Simulation of Living Systems. , (192-199). https://doi.org/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, respectively the ability to avoid obstacles and to minimise spent energy. We describe... Read More about Idiotypic networks for evolutionary controllers in virtual creatures..

Artificial Immune System driven evolution in Swarm Chemistry. (2014)
Conference Proceeding
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 behaviours can arise in potentially complex and distributed systems. In this field... Read More about Artificial Immune System driven evolution in Swarm Chemistry..

An immune network approach for self-adaptive ensembles of autonomic components: a case study in swarm robotics. (2013)
Conference Proceeding
Capodieci, N., Hart, E., & Cabri, G. (2013). An immune network approach for self-adaptive ensembles of autonomic components: a case study in swarm robotics. In P. Liò, O. Miglino, G. Nicosia, S. Nolfi, & M. Pavone (Eds.), Advances in Artifical Life, Proceedings of ECAL 2013. , (864-871). https://doi.org/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 coordination pattern during the runtime execution of a given task. Building on previo... Read More about An immune network approach for self-adaptive ensembles of autonomic components: a case study in swarm robotics..

Designing self-aware adaptive systems: from autonomic computing to cognitive immune networks. (2013)
Conference Proceeding
Capodieci, N., Hart, E., & Cabri, G. (2013). Designing self-aware adaptive systems: from autonomic computing to cognitive immune networks. In Proceedings of SASO Workshops 2013. https://doi.org/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 ensemble should cooperate to achieve system or human goals, and systems are e... Read More about Designing self-aware adaptive systems: from autonomic computing to cognitive immune networks..

Boosting the Immune System (2008)
Conference Proceeding
McEwan, C., Hart, E., & Paechter, B. (2008). 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 dynamical systems, with little overlap between. Although the balance is la... Read More about Boosting the Immune System.