Aniko Ekárt

Professor of Computer Science | Aston University

Birmingham, UNITED KINGDOM

Dr Ekárt researches artificial intelligence techniques.

Biography

Aniko Ekárt is a Professor in Computer Science at Aston University, specialising in Artificial Intelligence. She is also Director of Research Degree Programmes in the College of Engineering and Physical Sciences.

Aniko joined Aston as a lecturer in Computer Science in January 2006. During her time at Aston, she served as Associate Dean for Postgraduate Programmes between Aug 2017 - May 2020 and Head of Computer Science in the period Aug 2016 - May 2017.

Previously, she held research positions at the Institute for Computer Science and Control, Hungarian Academy of Sciences in Budapest, Hungary, while at the same time lecturing in Artificial Intelligence at various levels for the Eötvös Loránd University and the Dennis Gábor University in Budapest.

Between 2001 and 2003 Aniko was a lecturer in Computer Science at the University of Birmingham, where she lectured in Artificial Intelligence and Human Computer Interaction.

Her research interests are in the broad area of Artificial Intelligence Techniques. More specifically, she is interested in computational intelligence, theory and applications of genetic programming and evolutionary computation. Dr Ekárt is also involved in evolutionary art, where the personal aesthetic preference is modelled and followed by evolutionary methods. Additionally, she is interested in data mining for engineering, design and health areas. More recently, she has started contributing to social learning and trust in AI.

Areas of Expertise (4)

Artificial Intelligence Techniques

Evolutionary Computation

Genetic Programming

Neuroevolution

Education (3)

Eötvös Loránd University: Ph.D., Informatics 2001

Technical University of Cluj-Napoca: M.Sc., Next Generation of Computers 1996

Technical University of Cluj-Napoca: M.Sc., Computer Science 1995

Affiliations (1)

British Computer Society : Member

Links (3)

Media Appearances (1)

Collaboration to bring machine-learning and AI into social housing maintenance

The Engineer online

2023-04-03

The Aston University team will be led by Aniko Ekárt, Professor of Artificial Intelligence. “It is a privilege to be involved in the creation of this system, which will select the best contractor for each job based on their skill set, availability and location and be reactive to changing priorities of jobs," she said in a statement.

Articles (3)

Is Beauty in the Age of the Beholder?

12th International Conference, EvoMUSART

2023 Symmetry is a universal concept, its unique importance has made it a topic of research across many different fields. It is often considered as a constant where higher levels of symmetry are preferred in the judgement of faces and even the initial state of the universe is thought to have been in pure symmetry. The same is true in the judgement of auto-generated art, with symmetry often used alongside complexity to generate aesthetically pleasing images; however, these are two of many different aspects contributing to aesthetic judgement, each one of these aspects is also influenced by other aspects, for example, art expertise. These intricacies cause multiple problems such as making it difficult to describe aesthetic preferences and to auto-generate artwork using a high number of these aspects. In this paper, a gamified approach is presented which is used to elicit the preferences of symmetry levels for individuals and further understand how symmetry can be utilised within the context of automatically generating artwork. The gamified approach is implemented within an experiment with participants aged between 13 and 60, providing evidence that symmetry should be kept consistent within an evolutionary art context.

A GENTLER Approach to Urban Traffic Modelling and Prediction

IEEE Congress on Evolutionary Computation

2022 Intelligent Transportation aims to usher in a new and improved version of motorised traffic, one that is stream-lined, safe and at the heart of the net-zero agenda. Designing and building the urban infrastructure necessary to turn that vision into a reality relies on complex decision making, which often hinges on estimating the dynamics of future traffic through areas of the road network that are yet to be built. Traffic models capable of yielding such estimations, robustly and reliably, are valuable technological tools that urban planners can utilise to inform their decisions. To that end, we propose a novel algorithm that employs Genetic Programming and Transfer Learning to produce traffic models which accurately predict vehicle flow through a given junction based on readings collected from sur-rounding areas. We enhance the algorithm with a randomisation mechanism and run a comprehensive experimental study on a segment of the city of Darmstadt's road network, in order to investigate the effects of the exploration-exploitation interplay on the generated models' prediction accuracy.

Guest Editorial: Special Issue on Evolutionary Computation for Games

IEEE Transactions on Games

2023 The eight papers in this special section focus on applications of evolutionary computation to games to demonstrate several ways in which evolution can push boundaries and explore new areas of what is possible in the realm of games research, with a focus on game-playing, automatic agent parameter tuning, automatic game testing, and procedural content generation.