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Social learning enables multiple robots to share learned experiences while completing a task. The literature offers examples where robots trained with social learning reach a higher performance compared to their individual learning counterparts. No explanation has been advanced for that observation. In this research, we present experimental results suggesting that a lack of tuning of the parameters in social learning experiments could be the cause. In other words: the better the parameter settings are tuned, the less social learning can improve the system performance.
This paper introduces UniFIeD, a new data preprocessing method for time series. UniFIeD can cope with large intervals of missing data. A scalable test function generator, which allows the simulation of time series with different gap sizes, is presented additionally. An experimental study demonstrates that (i) UniFIeD shows a significant better performance than simple imputation methods and (ii) UniFIeD is able to handle situations, where advanced imputation methods fail. The results are independent from the underlying error measurements.
When using machine learning techniques for learning a function approximation from given data it is often a difficult task to select the right modeling technique.
In many real-world settings is no preliminary knowledge about the objective function available. Then it might be beneficial if the algorithm could learn all models by itself and select the model that suits best to the problem.
This approach is known as automated model selection. In this work we propose a
generalization of this approach.
It combines the predictions of several into one more accurate ensemble surrogate model. This approach is studied in a fundamental way, by first evaluating minimalistic ensembles of only two surrogate models in detail and then proceeding to ensembles with three and more surrogate models.
The results show to what extent combinations of models can perform better than single surrogate models and provides insights into the scalability and robustness of the approach. The study focuses on multi-modal functions topologies, which are important in surrogate-assisted global optimization.
We propose to apply typed Genetic Programming (GP) to the problem of finding surrogate-model ensembles for global optimization on compute-intensive target functions. In a model ensemble, base-models such as linear models, random forest models, or Kriging models, as well as pre- and post-processing methods, are combined. In theory, an optimal ensemble will join the strengths of its comprising base-models while avoiding their weaknesses, offering higher prediction accuracy and robustness. This study defines a grammar of model ensemble expressions and searches the set for optimal ensembles via GP. We performed an extensive experimental study based on 10 different objective functions and 2 sets of base-models. We arrive at promising results, as on unseen test data, our ensembles perform not significantly worse than the best base-model.
When researchers and practitioners in the field of
computational intelligence are confronted with real-world
problems, the question arises which method is the best to
apply. Nowadays, there are several, well established test
suites and well known artificial benchmark functions
available.
However, relevance and applicability of these methods to
real-world problems remains an open question in many
situations. Furthermore, the generalizability of these
methods cannot be taken for granted.
This paper describes a data-driven approach for the
generation of test instances, which is based on
real-world data. The test instance generation uses
data-preprocessing, feature extraction, modeling, and
parameterization. We apply this methodology on a classical
design of experiment real-world project and generate test
instances for benchmarking, e.g. design methods, surrogate
techniques, and optimization algorithms. While most
available results of methods applied on real-world
problems lack availability of the data for comparison,
our future goal is to create a toolbox covering multiple
data sets of real-world projects to provide a test
function generator to the research community.
This paper introduces CAAI, a novel cognitive architecture for artificial intelligence in cyber-physical production systems. The goal of the architecture is to reduce the implementation effort for the usage of artificial intelligence algorithms. The core of the CAAI is a cognitive module that processes declarative goals of the user, selects suitable models and algorithms, and creates a configuration for the execution of a processing pipeline on a big data platform. Constant observation and evaluation against performance criteria assess the performance of pipelines for many and varying use cases. Based on these evaluations, the pipelines are automatically adapted if necessary. The modular design with well-defined interfaces enables the reusability and extensibility of pipeline components. A big data platform implements this modular design supported by technologies such as Docker, Kubernetes, and Kafka for virtualization and orchestration of the individual components and their communication. The implementation of the architecture is evaluated using a real-world use case.
Die Arbeit beschreibt die Entwicklung und Verbreitung künstlicher Intelligenz (KI) und die damit verbundenen Herausforderungen und Chancen. Es wird hervorgehoben, dass trotz des offensichtlichen Nutzens von KI, Bedenken hinsichtlich unerwünschter Nebenwirkungen durch fehlerhafte oder missbräuchliche Anwendungen bestehen. Um diese Herausforderungen zu bewältigen, wird ein Ansatz vorgeschlagen, der als “konviviale künstliche Intelligenz” bezeichnet wird. Dieser Ansatz zielt auf ein harmonisches Zusammenspiel zwischen KI und Mensch ab und betont die Notwendigkeit einer menschenzentrierten Gestaltung bei der Entwicklung und Implementierung von KI-Modellen.
Data pre-processing is a key research topic in data mining because it plays a
crucial role in improving the accuracy of any data mining algorithm. In most
real world cases, a significant amount of the recorded data is found missing
due to most diverse errors. This loss of data is nearly always unavoidable.
Recovery of missing data plays a vital role in avoiding inaccurate data
mining decisions. Most multivariate imputation methods are not compatible
to univariate datasets and the traditional univariate imputation techniques
become highly biased as the missing data gap increases. With the current
technological advancements abundant data is being captured every second.
Hence, we intend to develop a new algorithm that enables maximum
utilization of the available big datasets for imputation. In this paper, we
present a Seasonal and Trend decomposition using Loess (STL) based
Seasonal Moving Window Algorithm, which is capable of handling patterns
with trend as well as cyclic characteristics. We show that the algorithm is
highly suitable for pre-processing of large datasets.
EventDetectR: An efficient Event Detection System (EDS) capable of detecting unexpected water quality conditions. This approach uses multiple algorithms to model the relationship between various multivariate water quality signals. Then the residuals of the models were utilized in constructing the event detection algorithm, which provides a continuous measure of the probability of an event at every time step. The proposed framework was tested for water contamination events with industrial data from automated water quality sensors. The results showed that the framework is reliable with better performance and is highly suitable for event detection.
Cyclone Dust Separators are devices often used to filter solid particles from flue gas. Such cyclones are supposed to filter as much solid particles from the carrying gas as possible. At the same time, they should only introduce a minimal pressure loss to the system. Hence, collection efficiency has to be maximized and pressure loss minimized. Both the collection efficiency and pressure loss are heavily influenced by the cyclones geometry. In this paper, we optimize seven geometrical parameters of an analytical cyclone model. Furthermore, noise variables are introduced to the model, representing the non-deterministic structure of the real-world problem. This is used to investigate robustness and sensitivity of solutions. Both the deterministic as well as the stochastic model are optimized with an SMS-EMOA. The SMS-EMOA is compared to a single objective optimization algorithm. For the harder, stochastic optimization problem, a surrogate-model-supported SMS-EMOA is compared against the model-free SMS-EMOA. The model supported approach yields better solutions with the same run-time budget.
Increasing computational power and the availability of 3D printers provide new tools for the combination of modeling and experimentation. Several simulation tools can be run independently and in parallel, e.g., long running computational fluid dynamics simulations can be accompanied by experiments with 3D printers. Furthermore, results from analytical and data-driven models can be incorporated. However, there are fundamental differences between these modeling approaches: some models, e.g., analytical models, use domain knowledge, whereas data-driven models do not require any information about the underlying processes.
At the same time, data-driven models require input and output data, but analytical models do not. Combining results from models with different input-output structures might improve and accelerate the optimization process. The optimization via multimodel simulation (OMMS) approach, which is able to combine results from these different models, is introduced in this paper.
Using cyclonic dust separators as a real-world simulation problem, the feasibility of this approach is demonstrated and a proof-of-concept is presented. Cyclones are popular devices used to filter dust from the emitted flue gases. They are applied as pre-filters in many industrial processes including energy production and grain processing facilities. Pros and cons of this multimodel optimization approach are discussed and experiences from experiments are presented.
Dieser Schlussbericht beschreibt die im Projekt „CI-basierte mehrkriterielle Optimierungsverfahren für Anwendungen in der Industrie“ (CIMO) im Zeitraum von November 2011 bis einschließlich Oktober 2014 erzielten Ergebnisse. Für aufwändige Optimierungsprobleme aus der Industrie wurden geeignete Lösungsverfahren entwickelt. Der Schwerpunkt lag hierbei auf Methoden aus den Bereichen Computational Intelligence (CI) und Surrogatmodellierung. Diese bieten die Möglichkeit, wichtige Herausforderung von aufwändigen, komplexen Optimierungsproblemen zu lösen. Die entwickelten Methoden können verschiedene konfliktäre Zielgrößen berücksichtigen, verschiedene Hierarchieebenen des Problems in die Optimierung integrieren, Nebenbedingungen beachten, vektorielle aber auch strukturierte Daten verarbeiten (kombinatorische Optimierung) sowie die Notwendigkeit teurer/zeitaufwändiger Zielfunktionsberechnungen reduzieren. Die entwickelten Methoden wurden schwerpunktmäßig auf einer Problemstellung aus der Kraftwerkstechnik angewendet, nämlich der Optimierung der Geometrie eines Fliehkraftabscheiders (auch: Zyklon), der Staubanteile aus Abgasen filtert. Das Optimierungsproblem, das diese FIiehkraftabscheider aufwerfen, führt zu konfliktären Zielsetzungen (z.B. Druckverlust, Abscheidegrad). Zyklone können unter anderem über aufwändige Computational Fluid Dynamics (CFD) Simulationen berechnet werden, es stehen aber auch einfache analytische Gleichungen als Schätzung zu Verfügung. Die Verknüpfung von beidem zeigt hier beispielhaft wie Hierarchieebenen eines Optimierungsproblems mit den Methoden des Projektes verbunden werden können. Neben dieser Schwerpunktanwendung konnte auch gezeigt werden, dass die Methoden in vielen weiteren Bereichen Erfolgreich zur Anwendung kommen können: Biogaserzeugung, Wasserwirtschaft, Stahlindustrie. Die besondere Herausforderung der behandelten Probleme und Methoden bietet viele wichtige Forschungsmöglichkeiten für zukünftige Projekte, die derzeit durch die Projektpartner vorbereitet werden.
There is a strong need for sound statistical analysis of simulation and optimization algorithms. Based on this analysis, improved parameter settings can be determined. This will be referred to as tuning. Model-based investigations are common approaches in simulation and optimization. The sequential parameter optimization toolbox (SPOT), which is implemented as a package for the statistical programming language R, provides sophisticated means for tuning and understanding simulation and optimization algorithms. The toolbox includes methods for tuning based on classical regression and analysis of variance techniques; tree-based models such as classification and regressions trees (CART) and random forest; Gaussian process models (Kriging), and combinations of different meta-modeling approaches. This article exemplifies how an existing optimization algorithm, namely simulated annealing, can be tuned using the SPOT framework.
The use of surrogate models is a standard method to deal with complex, realworld
optimization problems. The first surrogate models were applied to continuous
optimization problems. In recent years, surrogate models gained importance
for discrete optimization problems. This article, which consists of three
parts, takes care of this development. The first part presents a survey of modelbased
methods, focusing on continuous optimization. It introduces a taxonomy,
which is useful as a guideline for selecting adequate model-based optimization
tools. The second part provides details for the case of discrete optimization
problems. Here, six strategies for dealing with discrete data structures are introduced.
A new approach for combining surrogate information via stacking
is proposed in the third part. The implementation of this approach will be
available in the open source R package SPOT2. The article concludes with a
discussion of recent developments and challenges in both application domains.
Cyclone separators are popular devices used to filter dust from the emitted flue gases. They are applied as pre-filters in many industrial processes including energy production and grain processing facilities.
Increasing computational power and the availability of 3D printers provide new tools for the combination of modeling and experimentation, which necessary for constructing efficient cyclones. Several simulation tools can be run in parallel, e.g., long running CFD simulations can be accompanied by experiments with 3D printers. Furthermore, results from analytical and data-driven models can be incorporated. There are fundamental differences between these modeling approaches: some models, e.g., analytical models, use domain knowledge, whereas data-driven models do not require any information about the underlying processes.
At the same time, data-driven models require input and output data, whereas analytical models do not. Combining results from models with different input-output structure is of great interest. This combination inspired the development of a new methodology. An optimization via multimodel simulation approach, which combines results from different models, is introduced.
Using cyclonic dust separators (cyclones) as a real-world simulation problem, the feasibility of this approach is demonstrated. Pros and cons of this approach are discussed and experiences from the experiments are presented.
Furthermore, technical problems, which are related to 3D-printing approaches, are discussed.
Die steigende Komplexität der Produktionssysteme, insbesondere im Maschinenbau, führt zu einer Belastung für Automatisierer und Anlagenbauer. Um dieser Belastung entgegenzuwirken, bietet Industrie 4.0 mit Cyber-physischen Systemen und intelligenten Automatisierungssystemen eine Lösung. Dabei wird menschliches Expertenwissen in die Automatisierung verlagert, indem Ziele deklarativ formuliert werden, anstatt prozedurale Handlungsabläufe zu beschreiben. Dieser Ansatz ermöglicht es intelligenten Systemen, ausreichenden Handlungsspielraum zu haben und den menschlichen Aufwand bei der Optimierung, Inbetriebnahme und Anlagenumbau zu reduzieren. Um intelligente Automation umzusetzen, werden neue Automatisierungstechniken und Software-Services benötigt, die verschiedene Methoden wie maschinelles Lernen, Condition-Monitoring und Diagnose-Algorithmen sowie Optimierungsverfahren nutzen. Derzeit werden diese Services unabhängig voneinander implementiert und die Schnittstellen sind oft proprietär, was den Austausch von Daten, Modellen und Ergebnissen erschwert. Dennoch strebt Industrie 4.0 die Zusammenarbeit von Geräten und Komponenten unterschiedlicher Hersteller an. Als ein Lösungsansatz wurde in diesem Projekt eine kognitive Referenzarchitektur entwickelt, welche die genannten Punkte adressiert.
Sequential Parameter Optimization is a model-based optimization methodology, which includes several techniques for handling uncertainty. Simple approaches such as sharp- ening and more sophisticated approaches such as optimal computing budget allocation are available. For many real world engineering problems, the objective function can be evaluated at different levels of fidelity. For instance, a CFD simulation might provide a very time consuming but accurate way to estimate the quality of a solution.The same solution could be evaluated based on simplified mathematical equations, leading to a cheaper but less accurate estimate. Combining these different levels of fidelity in a model-based optimization process is referred to as multi-fidelity optimization. This chapter describes uncertainty-handling techniques for meta-model based search heuristics in combination with multi-fidelity optimization. Co-Kriging is one power- ful method to correlate multiple sets of data from different levels of fidelity. For the first time, Sequential Parameter Optimization with co-Kriging is applied to noisy test functions. This study will introduce these techniques and discuss how they can be applied to real-world examples.
The performance of optimization algorithms relies crucially on their parameterizations. Finding good parameter settings is called algorithm tuning. Using
a simple simulated annealing algorithm, we will demonstrate how optimization algorithms can be tuned using the Sequential Parameter Optimization Toolbox (SPOT). SPOT provides several tools for automated and interactive tuning. The underlying concepts of the SPOT approach are explained. This includes key techniques such as exploratory fitness landscape analysis and response surface methodology. Many examples illustrate
how SPOT can be used for understanding the performance of algorithms and gaining insight into algorithm behavior. Furthermore, we demonstrate how SPOT can be used as an optimizer and how a sophisticated ensemble approach is able to combine several meta models via stacking.
Dieser Schlussbericht beschreibt die im Projekt „Methoden der Computational Intelligence für Vorhersagemodelle in der Finanzund Wasserwirtschaft“ (FIWA) im Zeitraum von Juni 2009 bis einschließlich November 2012 erzielten Ergebnisse. In der Praxis werden für diese Vorhersagemodelle Verfahren der linearen und nichtlinearen Regression, NN, Support Vector Machines (SVM) und viele weitere Verfahren eingesetzt. Das Projekt FIWA befasste sich mit der Entwicklung modularer Systeme zur Analyse und Prognose von Daten aus der Finanz- und Wasserwirtschaft mittels Verfahren der Computational Intelligence (CI) mit methodischem Fokus auf dem CI-Unterbereich Genetic Programming (GP). Ein zentrales Ergebnis der wissenschaftlichtechnischen Arbeit im Projekt FIWA ist die Entwicklung der Open-Source Software RGP. Dabei handelt es sich um ein Software- Framework für GP, welches auf die automatische Erstellung von Vorhersagemodellen spezialisiert ist. Für die Finanzwirtschaft stand ein Handelssimulator zu Verfügung, der auf Basis von echten Finanzdaten die Qualität verschiedener Strategien testen kann. Dieser wurde im Projekt weiterentwickelt. GP wurde genutzt, um auf Basis der Simulationen genaue Vorhersagen und damit verbesserte Handelsstrategien zu entwerfen. Auch für die Wasserwirtschaft wurden Prognoseverfahren mit GP entwickelt. Der Schwerpunkt lag dabei auf der Füllstandprognose für Regenüberlaufbecken. Hier konnten moderne Verfahren mit GP oder SVM klassische Methoden deutlich schlagen oder verbessern. Auch der Einsatz von Sequentieller Parameter Optimierung zeigte signifikante Verbesserungen für die Prognosegenauigkeit. Dabei war die Kombination von klassischen Methoden und GP besonders erfolgreich. GP ist nach wie vor ein sehr aktives Forschungsgebiet und erlaubt auch für die Folgezeit zahlreiche Kooperationen mit den Partnern der Fachhochschule Köln. Sowohl für technische Anwendungen als auch zur Lösung von Forschungsfragen bieten sich zahlreiche Möglichkeiten an.
Ziel des Forschungsprojektes "Mehrkriterielle CI-basierte Optimierungsverfahren für den industriellen Einsatz" (MCIOP) war die Verringerung von Schadstoffemissionen in Kohlekraftwerken. Der wissenschaftliche Fokus lag auf der Entwicklung von Methoden, die in der Lage sind, interpretierbare Modelle für die Schadstoffemissionen automatisch zu generieren. Hierzu wurden mehrkriterielle Optimierungsverfahren entwickelt und eingesetzt. Zur Zeit- und Kostenreduktion wurde die Optimierung durch Surrogat-Modelle erfolgen, die abgestuft mit aufwändigeren Simulationen zum Einsatz kamen („optimization via simulation“). Bei der Untersuchung von Staubabscheidern konnten durch eine mehrkriterielle Optimierung unterschiedliche Zielgrößen, wie z.B. Abscheidegrad und Druckverlust, gleichzeitig berücksichtigt werden.
Dieser Bericht beschreibt die im Projekt MCIOP im Zeitraum von August 2011 bis einschließlich Juni 2015 erzielten Ergebnisse.