Multi-Agent Systems with Reciprocal Interaction Laws

In this thesis, we investigate a special class of multi-agent systems, which we call reciprocal multi-agent (RMA) systems. The evolution of agents in a RMA system is governed by interactions between pairs of agents. Each interaction is reciprocal, and the magnitude of attraction/repulsion depends only on distances between agents. We investigate the class of RMA systems from four perspectives, these are two basic properties of the dynamical system, one formula for computing the Morse indices/co-indices of critical formations, and one formation control model as a variation of the class of RMA systems. An important aspect about RMA systems is that there is an equivariant potential function associated with each RMA system so that the equations of motion of agents are actually a gradient flow. The two basic properties about this class of gradient systems we will investigate are about the convergence of the gradient flow, and about the question whether the associated potential function is generically an equivariant Morse function. We develop systematic approaches for studying these two problems, and establish important results. A RMA system often has multiple critical formations and in general, these are hard to locate. So in this thesis, we consider a special class of RMA systems whereby there is a geometric characterization for each critical formation. A formula associated with the characterization is developed for computing the Morse index/co-index of each critical formation. This formula has a potential impact on the design and control of RMA systems. In this thesis, we also consider a formation control model whereby the control of formation is achieved by varying interactions between selected pairs of agents. This model can be interpreted in different ways in terms of patterns of information flow, and we establish results about the controllability of this control system for both centralized and decentralized problems.Engineering and Applied Science

Crowdsourcing for smart engagement apps in an urban context : an explorative study

This paper elaborates on the first results of an ongoing living lab project on ‘smart’ city engagement and offers a theoretical, methodological and empirical contribution to the field of user-driven innovation by describing a crowdsourcing experiment conducted in collaboration with the city of Ghent (Flanders). Our presented living lab approach has a double goal. First, it wants to empower citizens by systematically transforming the relationship(s) between citizens and between citizens (as service users) and local city-related governmental institutes (as service providers) by offering smart city applications. Second, it has the ambition to go beyond reactively studying information systems as change agents and wants to pro-actively improve engineering systems that can contribute to the desired changes in city engagement. Supporting citizens as self-actuating sensors to open up more innovative ways of collecting data is an important boundary of the research within a living lab context. We aim for user-driven innovation by involving citizens in the co-production of new electronic public services. Therefore we choose to go through a co-design process (Sanders & Stappers, 2008) with citizens defining the smart engagement applications that most probably will be developed and implemented in a living lab setting. Today, various innovation companies and organizations envision a central role for the user when looking for innovations. The attention for participation of the user is growing since the 80’s, although that the meaning of the concept ‘participation’ is not stable. Different people have used ‘participation’ in a wide variety of different situations and the widespread use of the term has tended to mean that ‘participation’ is used to refer to a wide variety of different situations by different people (Pateman, 1972). Therefore some point to participation as an empty signifier (Carpentier, 2007). The history and origin (and radicalism) of the concept as related to power issues is fading away under the diversity of its different meanings. Recently different participative methods were developed and are used to learn about users and their needs. Some known user-centered methods within industry are working with living labs (Niitamo, Kulkki, Eriksson, & Hribernik, 2006) and crowdsourcing (Hudson-Smith, Batty, Crooks, & Milton, 2009). Although participative methods were initially mainly focused on handing over the power to the user, currently much more attention is given to usability of applications and market forecasting when in the context of user involvement or co-creation. The analysis of power relations is fading slowly away. In our research the notion of participation is used in two ways: as a political phrase, referring to users who are gaining more power and impact on societal changes, and as a practical phrase referring to the forecasting of the success of urban smart engagement apps. This paper is structured in four parts. The first part of the paper introduces the concepts of engagement and ‘smartness’. The second part of the paper introduces crowdsourcing and also elaborates on the related concepts of ‘Web 2.0”, ‘collective intelligence’ and ‘wisdom of crowds’. The third part of the paper describes our methodology, introduces the online crowdsourcing enabler ‘mijndigitaalideevoorgent’, and presents the first, preliminary results of our crowdscourcing experiment. The fourth and last part of the paper formulates a conclusion and discussion of the results

2018 SDSU Data Science Symposium Program

Table of Contents: Letter from SDSU PresidentLetter from SDSU Department of Mathematics and Statistics Dept. HeadSponsorsGeneral InformationKeynote SpeakersInvited SpeakersSunday ScheduleWorkshop InformationMonday ScheduleAbstracts| Invited SpeakersAbstracts | Oral PresentationsPoster PresentationCommittee and Volunteer

Novel sampling techniques for reservoir history matching optimisation and uncertainty quantification in flow prediction

Modern reservoir management has an increasing focus on accurately predicting the likely range of field recoveries. A variety of assisted history matching techniques has been developed across the research community concerned with this topic. These techniques are based on obtaining multiple models that closely reproduce the historical flow behaviour of a reservoir. The set of resulted history matched models is then used to quantify uncertainty in predicting the future performance of the reservoir and providing economic evaluations for different field development strategies. The key step in this workflow is to employ algorithms that sample the parameter space in an efficient but appropriate manner. The algorithm choice has an impact on how fast a model is obtained and how well the model fits the production data. The sampling techniques that have been developed to date include, among others, gradient based methods, evolutionary algorithms, and ensemble Kalman filter (EnKF). This thesis has investigated and further developed the following sampling and inference techniques: Particle Swarm Optimisation (PSO), Hamiltonian Monte Carlo, and Population Markov Chain Monte Carlo. The inspected techniques have the capability of navigating the parameter space and producing history matched models that can be used to quantify the uncertainty in the forecasts in a faster and more reliable way. The analysis of these techniques, compared with Neighbourhood Algorithm (NA), has shown how the different techniques affect the predicted recovery from petroleum systems and the benefits of the developed methods over the NA. The history matching problem is multi-objective in nature, with the production data possibly consisting of multiple types, coming from different wells, and collected at different times. Multiple objectives can be constructed from these data and explicitly be optimised in the multi-objective scheme. The thesis has extended the PSO to handle multi-objective history matching problems in which a number of possible conflicting objectives must be satisfied simultaneously. The benefits and efficiency of innovative multi-objective particle swarm scheme (MOPSO) are demonstrated for synthetic reservoirs. It is demonstrated that the MOPSO procedure can provide a substantial improvement in finding a diverse set of good fitting models with a fewer number of very costly forward simulations runs than the standard single objective case, depending on how the objectives are constructed. The thesis has also shown how to tackle a large number of unknown parameters through the coupling of high performance global optimisation algorithms, such as PSO, with model reduction techniques such as kernel principal component analysis (PCA), for parameterising spatially correlated random fields. The results of the PSO-PCA coupling applied to a recent SPE benchmark history matching problem have demonstrated that the approach is indeed applicable for practical problems. A comparison of PSO with the EnKF data assimilation method has been carried out and has concluded that both methods have obtained comparable results on the example case. This point reinforces the need for using a range of assisted history matching algorithms for more confidence in predictions