Stefan Leijnen

Utrecht University of Applied Sciences · Artificial Intelligence

We present a method to detect differences in the semantics of the spontaneous language of persons with separate Primary Progressive Aphasia syndromes (PPA) using automated Information Control Unit derivation. The resulting semantic clusters are evaluated for their use in a predictive model to identify speakers with PPA. A prototype description is a...

The healthcare sector has been confronted with rapidly rising healthcare costs and a shortage of medical staff. At the same time, the field of Artificial Intelligence (AI) has emerged as a promising area of research, offering potential benefits for healthcare. Despite the potential of AI to support healthcare, its widespread implementation, especia...

In the past few years, the EU has shown a growing commitment to address the rapid transformations brought about by the latest Artificial Intelligence (AI) developments by increasing efforts in AI regulation. Nevertheless, despite the growing body of technical knowledge and progress, the governance of AI-intensive technologies remains dynamic and ch...

The field of AI and the noosphere share a common interest in intelligence. The central paradigm in the current AI field is that once intelligent systems become advanced enough, they will solve our problems and society will benefit from these intelligent technologies. However, many of the current problems we face, such as the climate crisis, have be...

This white paper is the result of a research project by Hogeschool Utrecht, Copenhagen Business School and the Dutch Association of Insurers in the period February-June 2023. The goal of the research project was to provide an overview of the practical implementation of artificial intelligence (AI) in fraud detection of non-life insurance claims in...

Artificial Intelligence (AI) offers organizations unprecedented opportunities. However, one of the risks of using AI is that its outcomes and inner workings are not intelligible. In industries where trust is critical, such as healthcare and finance, explainable AI (XAI) is a necessity. However, the implementation of XAI is not straightforward, as i...

With recent developments in artificial intelligence, it is possible to generate human motion using deep learning. In this paper, a transformer deep learning algorithm is investigated to generate improvisation dance motions for the Another Kind of Blue (AKOB) data set. AKOB is an innovative dance group, located in The Hague, Netherlands, with a spec...

Explainable artificial intelligence (xAI) is seen as a solution to making AI systems less of a “black box”. It is essential to ensure transparency, fairness, and accountability – which are especially paramount in the financial sector. The aim of this study was a preliminary investigation of the perspectives of supervisory authorities and regulated...

Explainable artificial intelligence (xAI) is seen as a solution to making AI systems less of a "black box". It is essential to ensure transparency, fairness, and accountability-which are especially paramount in the financial sector. The aim of this study was a preliminary investigation of the perspectives of supervisory authorities and regulated en...

The ethics guidelines put forward by the AI High Level Expert Group (AI-HLEG) present a list of seven key requirements that Human-centered, trustworthy AI systems should meet. These guidelines are useful for the evaluation of AI systems, but can be complemented by applied methods and tools for the development of trustworthy AI systems in practice....

An overview of neural network architectures is presented. Some of these architectures have been created in recent years, whereas others originate from many decades ago. Apart from providing a practical tool for comparing deep learning models, the Neural Network Zoo also uncovers a taxonomy of network architectures, their chronology, and traces back...

An overview of neural network architectures is presented. Some of these architectures have been created in recent years, whereas others originate from many decades ago. Apart from providing a practical tool for comparing deep learning models, the Neural Network Zoo also uncovers a taxonomy of network architectures, their chronology, and traces back...

The application of phylogenetic techniques to the documentation of cultural history can present a distorted picture due to horizontal transmission and blending. Moreover, the units of cultural transmission must be communicable concepts, rather than conveniently measurable attributes, and relatedness between elements of culture often resides at the...

Using a neural network simulation of a series of language training experiments with chimpanzees, the difference between indexical and symbolic interpretation is explored. From the results of the simulation follows a discussion about the systemic requirements for crossing the symbolic threshold and how the primacy of icons applies to computational m...

This paper investigates the effectiveness of creative versus uncreative leadership using EVOC, an agent-based model of cultural evolution. Each iteration, each agent in the artificial society invents a new action, or imitates a neighbor’s action. Only the leader’s actions can be imitated by all other agents, referred to as followers. Two measures o...

There are both benefits and drawbacks to creativity. In a social group it is not necessary for all members to be creative to benefit from creativity; some merely imitate or enjoy the fruits of others' creative efforts. What proportion should be creative? This paper contains a very preliminary investigation of this question carried out using a compu...

Simulations using a computer model the Evolution Of Culture (EVOC) indicate that the clustering of creative agents decreases the mean fitness of ideas in the short term (when imitators have not yet been exposed to them) but increases idea fitness in the long term (presumably because agents swap partial solutions). With the steep fitness function us...

The Dutch AIBO Team is a multi-institute team which competes in the 4-legged robot league of RoboCup since 2004. Our team combines serious research with serious fun: collaborative autonomous (intelligent) systems are applied in Soccer, our shared application domain. This team description paper briefly outlines our current activities for RoboCup 200...

At the end of 2003, several institutes in the Netherlands have joined forces and formed the Dutch AIBO Team. We are a group of researchers and students from the DECIS Lab and the universities of Amsterdam, Delft, Twente and Utrecht. Our goal is to stimulate research, teaching, and applications in the fields of artificial intelligence and collaborat...

The Autonomous Intelligent Robot (AIR) Laboratory consists of researchers and students aiming to develop goal-directed, adaptive, and autonomous behaviors in a wide variety of robots. Since the foundation of the Lab in 1997, we have done a number of different projects including (1) Developing self-localisation algorithms and behaviors for the Pione...

There are both benefits and drawbacks to creativity. In a social group it is not necessary for all members to be creative to benefit from creativity; some merely imitate or enjoy the fruits of others' creative efforts. What proportion should be creative? This paper outlines investigations of this ques-tion carried out using a computer model of cult...

Self-programming systems are capable of producing their own constraints. However, what a program produces is already implicitly present in its initial set of instructions. The capability for transforma-tional creativity turns out to be a crucial factor for self-programming. In order to create new constraints, room has to be made available first thr...

CONCLUSIONS AND FUTURE WORK DAWN – the Bénard machine shows it is possible to simulate and recognize the spontaneous generation of emergent phenomena in a stochastic neural network. Next steps for DAWN are: o Regularize forms with input patterns o Creating forms in multiple dimensions (i.e. temperature, connectivity, spike timing) and pit them agai...

Using advanced pathfinding algorithms and a decision sys-tem based on utility-models for targets, we create agents that can largely act autonomously in a dynamic crisis scenario. We will show that using a clustering technique and the use of a new developed pathcost estimation algorithm the process of decisionmaking is simplified. The information gi...

The Autonomous Intelligent Robot (AIR) Laboratory consists of researchers and students aiming to develop goal-directed, adaptive, and autonomous behaviors in a wide variety of robots. Since the foundation of the Lab in 1997, we have done a number of different projects including (1) Developing self-localisation algorithms and behaviors for the Pione...