CHRISTIAN WAGNER's Outputs (72)

Explain the world – Using causality to facilitate better rules for fuzzy systems (2024)
Journal Article
Zhang, T., Wagner, C., & Garibaldi, J. M. (2024). Explain the world – Using causality to facilitate better rules for fuzzy systems. IEEE Transactions on Fuzzy Systems, 1-14. https://doi.org/10.1109/TFUZZ.2024.3457962

The rules of a rule-based system provide explanations for its behaviour by revealing the relationships between the variables captured. However, ideally, we have AI systems which go beyond explainable AI (XAI), that is, systems which not only explain... Read More about Explain the world – Using causality to facilitate better rules for fuzzy systems.

Implementing responsible innovation: the role of the meso-level(s) between project and organisation (2024)
Journal Article
Stahl, B. C., Portillo, V., Wagner, H., Craigon, P. J., Darzentas, D., De Ossorno Garcia, S., Dowthwaite, L., Greenhalgh, C., Middleton, S. E., Nichele, E., Wagner, C., & Webb, H. (2024). Implementing responsible innovation: the role of the meso-level(s) between project and organisation. Journal of Responsible Innovation, 11(1), Article 2370934. https://doi.org/10.1080/23299460.2024.2370934

Much of academic discussion of responsible innovation (RI) has focused on RI integration into research projects. In addition, significant attention has also been paid to RI structures and policies at the research policy and institutional level. This... Read More about Implementing responsible innovation: the role of the meso-level(s) between project and organisation.

Gradient-based Fuzzy System Optimisation via Automatic Differentiation – FuzzyR as a Use Case (2024)
Preprint / Working Paper
Chen, C., Wagner, C., & Garibaldi, J. M. (2024). Gradient-based Fuzzy System Optimisation via Automatic Differentiation – FuzzyR as a Use Case

Since their introduction, fuzzy sets and systems have become an important area of research known for its versatility in modelling, knowledge representation and reasoning, and increasingly its potential within the context explainable AI. While the app... Read More about Gradient-based Fuzzy System Optimisation via Automatic Differentiation – FuzzyR as a Use Case.

Explaining time series classifiers through meaningful perturbation and optimisation (2023)
Journal Article
Meng, H., Wagner, C., & Triguero, I. (2023). Explaining time series classifiers through meaningful perturbation and optimisation. Information Sciences, 645, Article 119334. https://doi.org/10.1016/j.ins.2023.119334

Machine learning approaches have enabled increasingly powerful time series classifiers. While performance has improved drastically, the resulting classifiers generally suffer from poor explainability, limiting their applicability in critical areas. S... Read More about Explaining time series classifiers through meaningful perturbation and optimisation.

Assessing responsible innovation training (2023)
Journal Article
Stahl, B. C., Aicardi, C., Brooks, L., Craigon, P. J., Cunden, M., Burton, S. D., …Webb, H. (2023). Assessing responsible innovation training. Journal of Responsible Technology, 16, Article 100063. https://doi.org/10.1016/j.jrt.2023.100063

There is broad agreement that one important aspect of responsible innovation (RI) is to provide training on its principles and practices to current and future researchers and innovators, notably including doctoral students. Much less agreement can be... Read More about Assessing responsible innovation training.

Towards Handling Uncertainty-at-Source in AI – A Review and Next Steps for Interval Regression (2023)
Journal Article
Kabir, S., Wagner, C., & Ellerby, Z. (2023). Towards Handling Uncertainty-at-Source in AI – A Review and Next Steps for Interval Regression. IEEE Transactions on Artificial Intelligence, 5(1), 3-22. https://doi.org/10.1109/TAI.2023.3234930

Most of statistics and AI draw insights through modelling discord or variance between sources (i.e., inter-source) of information. Increasingly however, research is focusing on uncertainty arising at the level of individual measurements (i.e., within... Read More about Towards Handling Uncertainty-at-Source in AI – A Review and Next Steps for Interval Regression.

Tackling communication and analytical problems in environmental planning: Expert assessment of key definitions and their relationships (2022)
Journal Article
Wallace, K. J., Wagner, C., Pannell, D. J., Kim, M. K., & Rogers, A. A. (2022). Tackling communication and analytical problems in environmental planning: Expert assessment of key definitions and their relationships. Journal of Environmental Management, 317, Article 115352. https://doi.org/10.1016/j.jenvman.2022.115352

Inadequate definition of key terms and their relationships generates significant communication and analytical problems in environmental planning. In this work, we evaluate an ontological framework for environmental planning designed to combat these p... Read More about Tackling communication and analytical problems in environmental planning: Expert assessment of key definitions and their relationships.

Constraint reformulations for set point optimization problems using fuzzy cognitive map models (2021)
Journal Article
Garzón Casado, A., Cano Marchal, P., Wagner, C., Gómez Ortega, J., & Gámez García, J. (2022). Constraint reformulations for set point optimization problems using fuzzy cognitive map models. Optimal Control Applications and Methods, 43(3), 711-721. https://doi.org/10.1002/oca.2846

The selection of optimal set points is an important problem in modern process control. Fuzzy cognitive maps (FCMs) allow to construct models of complex processes using expert knowledge, which is particularly useful in situations where measuring the v... Read More about Constraint reformulations for set point optimization problems using fuzzy cognitive map models.

A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems (2020)
Journal Article
D'Alterio, P., Garibaldi, J. M., John, R. I., & Wagner, C. (2021). A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems. IEEE Transactions on Fuzzy Systems, 29(11), 3323-3333. https://doi.org/10.1109/TFUZZ.2020.3018379

Constrained interval type-2 (CIT2) fuzzy sets have been introduced to preserve interpretability when moving from type-1 to interval type-2 (IT2) membership functions. Although they can be used to produce type-2 fuzzy systems with enhanced explainabil... Read More about A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems.

A Similarity Measure Based on Bidirectional Subsethood for Intervals (2020)
Journal Article
Kabir, S., Wagner, C., Havens, T. C., & Anderson, D. T. (2020). A Similarity Measure Based on Bidirectional Subsethood for Intervals. IEEE Transactions on Fuzzy Systems, 28(11), 2890-2904. https://doi.org/10.1109/tfuzz.2019.2945249

With a growing number of areas leveraging interval-valued data—including in the context of modelling human uncertainty (e.g., in Cyber Security), the capacity to accurately and systematically compare intervals for reasoning and computation is increas... Read More about A Similarity Measure Based on Bidirectional Subsethood for Intervals.

Towards a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems - A Participatory Design Approach (2020)
Journal Article
Soria, D., Razak, T. R., Garibaldi, J. M., Pourabdollah, A., & Wagner, C. (2021). Towards a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems - A Participatory Design Approach. IEEE Transactions on Fuzzy Systems, 29(5), 1160-1172. https://doi.org/10.1109/tfuzz.2020.2969901

Hierarchical fuzzy systems (HFSs) have been shown to have the potential to improve the interpretability of fuzzy logic systems (FLSs). However, challenges remain, such as: "How can we measure their interpretability?", "How can we make an informed ass... Read More about Towards a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems - A Participatory Design Approach.

ADONiS - Adaptive Online Non-Singleton Fuzzy Logic Systems (2019)
Journal Article
Pekaslan, D., Wagner, C., & Garibaldi, J. M. (2020). ADONiS - Adaptive Online Non-Singleton Fuzzy Logic Systems. IEEE Transactions on Fuzzy Systems, 28(10), 2302-2312. https://doi.org/10.1109/tfuzz.2019.2933787

Non-Singleton Fuzzy Logic Systems (NSFLSs) have the potential to capture and handle input noise within the design of input fuzzy sets. In this paper, we propose an online learning method which utilises a sequence of observations to continuously updat... Read More about ADONiS - Adaptive Online Non-Singleton Fuzzy Logic Systems.

Agent-Based Simulation Modelling for Reflecting on Consequences of Digital Mental Health (2019)
Preprint / Working Paper
Stroud, D., Wagner, C., & Siebers, P.-O. Agent-Based Simulation Modelling for Reflecting on Consequences of Digital Mental Health

The premise of this working paper is based around agent-based simulation models and how to go about creating them from given incomplete information. Agent-based simulations are stochastic simulations that revolve around groups of agents that each hav... Read More about Agent-Based Simulation Modelling for Reflecting on Consequences of Digital Mental Health.

Exploring subsethood to determine firing strength in non-singleton fuzzy logic systems (2018)
Presentation / Conference Contribution
Pekaslan, D., Garibaldi, J. M., & Wagner, C. (2018). Exploring subsethood to determine firing strength in non-singleton fuzzy logic systems.

Real world environments face a wide range of sources of noise and uncertainty. Thus, the ability to handle various uncertainties, including noise, becomes an indispensable element of automated decision making. Non-Singleton Fuzzy Logic Systems (NSFLS... Read More about Exploring subsethood to determine firing strength in non-singleton fuzzy logic systems.

A bidirectional subsethood based similarity measure for fuzzy sets (2018)
Presentation / Conference Contribution
Kabir, S., Wagner, C., Havens, T. C., & Anderson, D. T. (2018). A bidirectional subsethood based similarity measure for fuzzy sets. In N/A

Similarity measures are useful for reasoning about fuzzy sets. Hence, many classical set-theoretic similarity measures have been extended for comparing fuzzy sets. In previous work, a set-theoretic similarity measure considering the bidirectional sub... Read More about A bidirectional subsethood based similarity measure for fuzzy sets.

Input uncertainty sensitivity enhanced non-singleton fuzzy logic controllers for long-term navigation of quadrotor UAVs (2018)
Journal Article
Fu, C., Sarabakha, A., Kayacan, E., Wagner, C., John, R., & Garibaldi, J. M. (2018). Input uncertainty sensitivity enhanced non-singleton fuzzy logic controllers for long-term navigation of quadrotor UAVs. IEEE/ASME Transactions on Mechatronics, 23(2), 725-734. https://doi.org/10.1109/TMECH.2018.2810947

Input uncertainty, e.g., noise on the on-board camera and inertial measurement unit, in vision-based control of unmanned aerial vehicles (UAVs) is an inevitable problem. In order to handle input uncertainties as well as further analyze the interactio... Read More about Input uncertainty sensitivity enhanced non-singleton fuzzy logic controllers for long-term navigation of quadrotor UAVs.

The arithmetic recursive average as an instance of the recursive weighted power mean (2017)
Presentation / Conference Contribution
Wagner, C., Havens, T. C., & Anderson, D. T. (2017). The arithmetic recursive average as an instance of the recursive weighted power mean. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (1-6). https://doi.org/10.1109/FUZZ-IEEE.2017.8015507

The aggregation of multiple information sources has a long history and ranges from sensor fusion to the aggregation of individual algorithm outputs and human knowledge. A popular approach to achieve such aggregation is the fuzzy integral (FI) which i... Read More about The arithmetic recursive average as an instance of the recursive weighted power mean.

Modelling uncertainty in production processes using non-singleton fuzzification and fuzzy cognitive maps: a virgin olive oil case study (2016)
Presentation / Conference Contribution
Marchal, P. C., Wagner, C., Gámez, J. G., & Gómez, J. O. (in press). Modelling uncertainty in production processes using non-singleton fuzzification and fuzzy cognitive maps: a virgin olive oil case study.

Decision support systems (DSSs) are a convenient tool to aid plant operators in the selection of process set points. Inputs to these systems for variables that are not easily measured online often come from assessments made by experts, with an associ... Read More about Modelling uncertainty in production processes using non-singleton fuzzification and fuzzy cognitive maps: a virgin olive oil case study.

A comparative study on the control of Quadcopter UAVs by using singleton and non-singleton fuzzy logic controllers (2016)
Presentation / Conference Contribution
Fu, C., Sarabakha, A., Kayacan, E., Wagner, C., John, R., & Garibaldi, J. M. (in press). A comparative study on the control of Quadcopter UAVs by using singleton and non-singleton fuzzy logic controllers.

Fuzzy logic controllers (FLCs) have extensively been used for the autonomous control and guidance of unmanned aerial vehicles (UAVs) due to their capability of handling uncertainties and delivering adequate control without the need for a precise, mat... Read More about A comparative study on the control of Quadcopter UAVs by using singleton and non-singleton fuzzy logic controllers.

Modelling cyber-security experts' decision making processes using aggregation operators (2016)
Journal Article
Miller, S., Wagner, C., Aickelin, U., & Garibaldi, J. M. (2016). Modelling cyber-security experts' decision making processes using aggregation operators. Computers and Security, 62, 229-245. https://doi.org/10.1016/j.cose.2016.08.001

An important role carried out by cyber-security experts is the assessment of proposed computer systems, during their design stage. This task is fraught with difficulties and uncertainty, making the knowledge provided by human experts essential for su... Read More about Modelling cyber-security experts' decision making processes using aggregation operators.