Logo des Repositoriums

Auflistung nach Schlagwort "Optimization"

1 - 10 von 10
  • Konferenzbeitrag
    asprin: Answer Set Programming with Preferences
    (Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Romero, Javier
    Answer Set Programming (ASP) is a well established approach to declarative problem solving, combining a rich yet simple modeling language with high-performance solving capacities. In this talk we present asprin, a general, flexible and extensible framework for preferences in ASP. asprin is general and captures many of the existing approaches to preferences. It is flexible, because it allows for the combination of different types of preferences. It is also extensible, allowing for an easy implementation of new approaches to preferences. Since it is straightforward to capture propositional theories and constraint satisfaction problems in ASP, the framework is also relevant to optimization in Satisfiability Testing and Constraint Processing.
  • Zeitschriftenartikel
    Digital Forensics AI: Evaluating, Standardizing and Optimizing Digital Evidence Mining Techniques
    (KI - Künstliche Intelligenz: Vol. 36, No. 2, 2022) Solanke, Abiodun A.; Biasiotti, Maria Angela
    The impact of AI on numerous sectors of our society and its successes over the years indicate that it can assist in resolving a variety of complex digital forensics investigative problems. Forensics analysis can make use of machine learning models’ pattern detection and recognition capabilities to uncover hidden evidence in digital artifacts that would have been missed if conducted manually. Numerous works have proposed ways for applying AI to digital forensics; nevertheless, scepticism regarding the opacity of AI has impeded the domain’s adequate formalization and standardization. We present three critical instruments necessary for the development of sound machine-driven digital forensics methodologies in this paper. We cover various methods for evaluating, standardizing, and optimizing techniques applicable to artificial intelligence models used in digital forensics. Additionally, we describe several applications of these instruments in digital forensics, emphasizing their strengths and weaknesses that may be critical to the methods’ admissibility in a judicial process.
  • Textdokument
    Enabling decentralized demand side management in industrial energy supply systems
    (INFORMATIK 2020, 2021) Bull, Daniel; Bürger, Adrian; Bohlayer, Markus; Fleschutz, Markus; Braun, Marco
    Due to the increasing share of fluctuating renewable energy resources in the energy supply, the supply-demand balance needs to be increasingly supported by prosumers, who are able to adapt their energy demand and production depending on the current supply. Since small and medium-sized companies are expected to yield the potential for providing a significant share of the required flexibility, we propose an approach that enables an efficient development, testing and implementation of advanced control strategies and further data applications in decentralized energy supply systems of medium-sized companies to support the integration of such technologies and the increase of prosumer-side flexibility. The approach is based on an adaptable control framework, which is at first applied to a physical simulation model of the industrial energy system to test and train new control strategies and can afterwards be moved to the actual energy supply system of the plant.
  • Zeitschriftenartikel
    Mental Models for Intelligent Systems: eRobotics Enables New Approaches to Simulation-Based AI
    (KI - Künstliche Intelligenz: Vol. 28, No. 2, 2014) Roßmann, Jürgen; Guiffo Kaigom, Eric; Atorf, Linus; Rast, Malte; Grinshpun, Georgij; Schlette, Christian
    eRobotics is a newly evolving branch of e-Systems engineering, providing tools to support the whole life cycle of robotic applications by means of electronic media. With the eRobotics methodology, the target system and its environment can be modeled, validated, and calibrated to achieve a close-to-reality simulation. In this contribution, we present simulation-based mental models for autonomous systems as a foundation for new approaches to prediction and artificial intelligence. We formulate a methodology to construct optimization problems within simulation environments in order to assist autonomous systems in action planning. We illustrate the usefulness and performance of this approach through various examples in different fields. As application for space robotics, we focus on climbing strategies of a legged mobile exploration robot. Furthermore, we enable skillfull interaction control in service robotics and address energy consumption issues. The contribution concludes with a detailed discussion of the concept presented here.
  • Konferenzbeitrag
    Optimizing Enterprise Architecture Considering Different Budgets
    (INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (Workshop-Beiträge), 2019) Dohmen, Niklas; Koopmann, Kevin; Hacks, Simon
    Enterprise Architectures (EA) are used to define the structure and operation of an organization and commonly find usage in the realization and modification of IT business strategies. We propose a technique to optimize the costs incurred between two layers of the EA, especially, considering differing departmental budgets. This is achieved through consideration of a flow problem aiming to optimize a graph consisting of different nodes, allowing budgets to be used by different departments. Additionally, we implement techniques previously published to allow operational and transitioning costs to be taken into consideration, in an effort to better reflect the organizational problems found in reality.
  • Textdokument
    Optimizing Enterprise Architectures Using Linear Integer Programming Techniques
    (INFORMATIK 2017, 2017) Hacks, Simon; Lichter, Horst
    Within this paper, we present a technique to optimize the relations between two adjacent layers of Enterprise Architectures (EA). Therefore, we suggest to interpret the constraints between these two layers as triangles, where a needed capability of an upper layer element is realized by a lower layer element. This eases the communication of the optimization model e.g. to the management. Moreover, we propose a mapping between the elements of our technique to the widely accepted ArchiMate notation to enable the application of our technique in existing organizations.
  • Textdokument
    The Power of Regular Constraints in CSPs
    (INFORMATIK 2017, 2017) Löffler, Sven; Liu, Ke; Hofstedt, Petra
    This paper discusses the use of the regular membership constraint as a replacement for other (global) constraints. The goal is to replace some or all constraints of a constraint satisfaction problem (CSP) with regular constraints and to combine them into a new regular constraint to remove redundancy and to improve the solution speed of CSPs. By means of a rostering problem as an example it is shown that our approach allows a significant improvement of the solution performance due to a reduction of the size of the search tree.
  • Zeitschriftenartikel
    Resource Planning in Disaster Response
    (Business & Information Systems Engineering: Vol. 57, No. 4, 2015) Schryen, Guido; Rauchecker, Gerhard; Comes, Tina
    Managing the response to natural, man-made, and technical disasters is becoming increasingly important in the light of climate change, globalization, urbanization, and growing conflicts. Sudden onset disasters are typically characterized by high stakes, time pressure, and uncertain, conflicting or lacking information. Since the planning and management of response is a complex task, decision makers of aid organizations can thus benefit from decision support methods and tools. A key task is the joint allocation of rescue units and the scheduling of incidents under different conditions of collaboration. The authors present an approach to support decision makers who coordinate response units by (a) suggesting mathematical formulations of decision models, (b) providing heuristic solution procedures, and (c) evaluating the heuristics against both current best practice behavior and optimal solutions. The computational experiments show that, for the generated problem instances, (1) current best practice behavior can be improved substantially by our heuristics, (2) the gap between heuristic and optimal solutions is very narrow for instances without collaboration, and (3) the described heuristics are capable of providing solutions for all generated instances in less than a second on a state-of-the-art PC.
  • Konferenzbeitrag
    Searching for Optimal Models: Comparing Two Encoding Approaches (Summary)
    (Software Engineering 2020, 2020) John, Stefan; Burdusel, Alexandru; Bill, Robert; Strüber, Daniel; Taentzer, Gabriele; Zschaler, Steffen; Wimmer, Manuel
    This work summarizes our paper originally published in The Journal of Object Technology in the course of the International Conference on Model Transformations 2019.
  • Textdokument
    A Survey of Constraint Transformation Methods
    (INFORMATIK 2021, 2021) Löffler, Sven; Becker, Ilja; Kroll, Franz; Hofstedt, Petra
    The solution performance of finite domain (FD) constraint problems can often be improved by either transforming particular constraints or sub-problems into other FD constraints like binary, table or regular membership constraints, or by transformation of the complete FD problem into an equivalent problem but of another domain, e.g. in a SAT problem. Specialized constraint solvers (like binary or SAT solvers) can outperform general constraint solvers for certain problems. However, this comes with high efforts for the transformation and/or other disadvantages such as a restricted set of constraints such specialized solvers can handle or limitations on the variables domains. In this paper we give an overview of CSP and constraint transformations and discuss applicabilibty and advantages and disadvantages of these approaches.