Rinde R. S. van Lon

University of Leuven, Louvain, Flanders, Belgium

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Publications (7)0 Total impact

  • R.R.S. van Lon · T. Holvoet
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    ABSTRACT: We consider logistics problems that are both dynamic and potentially large scale. A common way to create a scalable solution for logistics problems is to use multi-agent systems. In this paper we take two positions of a different nature: (1) evolutionary designed multi-agent systems are a promising approach to create scalable and performant solutions for logistics problems, (2) the multi-agent systems field does not prioritize evaluation enough, which hinders thorough scientific comparisons and prevents adoption in industry. We present arguments and refute common counterarguments for our position. Further, we discuss our present and upcoming efforts to realize our position.
    No preview · Conference Paper · Jan 2013
  • R.R.S. van Lon · T. Holvoet
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    ABSTRACT: Engineering collective adaptive systems (CAS) is a challenging task. Concurrent systems, esp. when being large-scale, are known to be hard to design as the overall system behavior non-linearly results from local behavior and interactions. They are also hard to engineer and debug, as time dependent errors are often hard to reproduce. Simulation tools and environments are often used to assist in this task. From our experience in developing and using simulators for decentralized systems (in traffic, logistics and smart power grid management), we learned that a simulation environment should comply to the following quality criteria. First, from a software engineering point of view, a simulation environment itself must be designed up to the highest software quality standards - modularity, separation of concerns, test-driven development, guaranteed state consistency, etc. are particularly important quality criteria to ensure correctness, extensibility and manageability of the software. Second, the simulation environment must provide convenient support for using and extending the simulation environment, ease the visualization of solutions, and - since its use in scientific process - offer direct support for evaluating CAS through the set-up of experiments. In this paper, we present RinSim, an open source simulator that explicitly addresses these quality criteria, and targets the large family of transportation and logistics applications. RinSim separates the definition of the problem domain from the solution, has a modular design, is being developed in a test-driven way, etc. RinSim has been used and extended in a variety of cases within our research group, and served as the core platform in our educational program on multi-agent software development.
    No preview · Conference Paper · Jan 2012
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    ABSTRACT: In dynamic dial-a-ride problems a fleet of vehicles need to handle transportation requests within time. We research how to create a decentralized multi-agent system that can solve the dynamic dial-a-ride problem. Normally multi-agent systems are hand designed for each specific application. In this paper we research the applicability of genetic programming to automatically program a multi-agent system that solves dial-a-ride problems. We evaluated the evolved system by running a number of simulations and compared it's performance to a selection hyper-heuristic. The results shows that genetic programming can be a viable alternative to hand constructing multi-agent systems.
    Full-text · Article · Jan 2012
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    Rinde R. S. van Lon · Pascal Wiggers · Léon J. M. Rothkrantz · Tom Holvoet
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    ABSTRACT: Complex systems are emergent, self-organizing and adaptive systems. They are pervasive in nature and usually hard to analyze or understand. Often they appear intelligent and show favorable properties such as resilience and anticipation. In this paper we describe a classifier model inspired by complex systems theory. Our model is a generalization of neural networks, boolean networks and genetic programming trees called computational networks. Designing computational networks by hand is infeasible when dealing with complex data. For designing our classifiers we developed an evolutionary design algorithm. Four extensions of this algorithm are presented. Each extension is inspired by natural evolution and theories from the evolutionary computing literature. The experiments show that our model can be evolutionary designed to act as a classifier. We show that our evolved classifiers are competitive compared to the classifiers in the Weka classifier collection. These experiments lead to the conclusion that using our evolutionary algorithm to design computational networks is a promising approach for the creation of classifiers. The benefits of the evolutionary extensions are inconclusive, for some datasets there is a significant performance increase while for other datasets the increase is very minimal.
    Full-text · Conference Paper · Jan 2011
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    Shaza Hanif · Rinde R. S. van Lon · Ning Gui · Tom Holvoet
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    ABSTRACT: Pickup and Delivery Problems (PDPs) have received significant research interest in the past decades. Their industrial relevance has stimulated the study of various types of solutions. Both centralized solutions, using discrete optimization techniques, as well as distributed, multi-agent system (MAS) solutions, have proven their merits. However, real PDP problems today are more and more characterized by (1) dynamism - in terms of tasks, service time, vehicle availability, infrastructure availability, and (2) their large scale - in terms of the geographical field of operation, the number of pickup and delivery tasks and vehicles. A combination of both characteristics brings unsolved challenges. Delegate MAS is a coordination mechanism that could prove to be valuable for constructing a decentralized solution for dynamic and large scale PDP problems. In this paper, we illustrate a solution based on delegate MAS for solving PDP. Our solution enables different agents to dynamically collect and disseminate local information and make decisions in a fully decentralized way. We applied our approach to a concrete case study. Experimental results indicate the suitability of the approach for dynamic and large scale PDP problems.
    Full-text · Conference Paper · Jan 2011
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    ABSTRACT: At TUDelft there is a project running on the assessment of the emotional state of car-drivers. Emotions are usual assessed by analysis of facial expressions and voice analysis. In this paper we report about assessment of emotions via EEG analysis. In a car simulation environment car-drivers, equipped with neurocaps, have to drive different tracks with and without billboards. Registration of billboards generates specific brain signals P300 and has an impact on the eye-blink rate. We report about the experimental design and results of analysis.
    No preview · Conference Paper · Jan 2010
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    R.R.S. Van Lon
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    ABSTRACT: This thesis argues that natural complex systems can provide an inspiring example for creating software which incorporates emergent, self-organizing and adaptive properties. The advantages of complex sys- tems are their natural resilience, redundancy and adaptivity. A generalization of neural networks and boolean networks called computational networks is presented as a model for complex systems. It is argued that this model satisfies the required properties for modeling complex systems. Furthermore, it is asserted that a computational network, being a network of mathematical functions, is appropriate for solving classification problems. For the design of computational networks an evolutionary design algorithm is constructed. Additionally, four extensions of this algorithm are presented. Each extension is inspired by natural evolution and theories from the evolutionary computing literature. An impor- tant component is a novel generative representation which can reuse substructures of computational networks. Experiments with this component have shown that it facilitates a higher level of complexity in the solution space, improving the computational network performance for more complex problems. Other components steer the evolutionary process towards a desired solution, either by introducing spe- cial stages during evolution, or by smoothing the fitness landscape. The experiments show that complex systems can be evolutionary designed to act as a classifier. The resulting computational network has a better performance on the Iris dataset compared to every classifier in the Weka classifier collection. Furthermore, an experiment was conducted using the TIMIT read speech dataset, the classifier was evo- lutionary designed using only 13 MFCC features, and a very small train set. Although the performance is not good enough to be of any practical use, the results are adequate given the limitations of the train data.
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