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A stabilizing iteration scheme for model predictive control based on relaxed barrier functions

AUTOMATICA 2017年 80卷 328-339页

作者： Feller, Christian Ebenbauer, Christian Univ Stuttgart Inst Syst Theory & Automat Control Pfaffenwaldring 9 D-70550 Stuttgart Germany

We propose and analyze a stabilizing iteration scheme for the algorithmic implementation of model predictive control for linear discrete-time systems subject to polytopic input and state constraints. The required on-line optimization makes use of a relaxed barrier function based problem formulation and performs only a limited, possibly small, number of optimization algorithm iterations between two consecutive sampling instants. The optimization algorithm dynamics as well as the resulting suboptimality of the applied control input are taken into account explicitly in the stability analysis, and the origin of the resulting overall closed-loop system, consisting of state and optimization algorithm dynamics, is proven to be asymptotically stable. The corresponding constraint satisfaction properties are also analyzed. Both the theoretical results and a presented numerical example illustrate the fact that asymptotic stability as well as a satisfactory closed-loop performance may be achieved independently of the number of optimization algorithm iterations, thus leading to a novel class of stabilizing MPC algorithms. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Predictive control anytime algorithms Optimization Barrier functions

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Deterministic approximation of marginal probabilities in Bayes nets

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 1998年第4期28卷 377-393页

作者： Santos, E Shimony, SE Univ Connecticut Dept Comp Sci & Engn Storrs CT 06269 USA Ben Gurion Univ Negev Dept Math & Comp Sci IL-84105 Beer Sheva Israel

Computation of marginal probabilities in Bayes nets is central to numerous reasoning and automatic decision making systems. This paper presents a deterministic approximation scheme for this hard problem that supplies provably correct bounds by aggregating probability mass in independence-based (IB) assignments. The scheme presented refines recent work in belief updating for Bayes networks: attempts to approximate posterior probabilities by finding a small number of the highest probability complete (or perhaps evidentially supported) assignments. Under certain assumptions, the probability mass in the union of these assignments is sufficient to obtain a good approximation. Such methods are especially useful for highly connected networks, where the maximum clique size or the cutset size make many existing algorithms intractable. Since IB assignments contain fewer assigned variables, the probability mass in each assignment is greater than in the respective complete assignment. Thus, fewer IB assignments are sufficient, and a good approximation can be obtained more efficiently. Two classes of algorithms for finding the high probability IB assignments are suggested: best-first heuristic search and a special-purpose integer linear program (ILP), Since IB assignments may be overlapping events in probability space, accumulating the mass in a set of assignments may be hard. In the ILP variant, it is easy to avoid the problem by adding equations that prohibit overlap. In the best-first search algorithm, other schemes are necessary, but experimental results suggest that using inclusion-exclusion (potentially exponential-time in the worst case) in the overlap cases is not too expensive for most problem instances.

关键词： anytime algorithms approximate belief updating approximating marginal probabilities Bayesian belief networks decision making systems probabilistic reasoning

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Adaptive online scheduling of tasks with anytime property on heterogeneous resources

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COMPUTERS & OPERATIONS RESEARCH 2016年第0期76卷 95-117页

作者： Modos, Istvan Sucha, Premysl Vaclavik, Roman Smejkal, Jan Hanzalek, Zdenek Czech Tech Univ Fac Elect Engn Dept Control Engn Karlovo Namesti 13 Prague 12135 2 Czech Republic Merica U Ladek 353-37 Ricany Strasin 25101 Czech Republic Czech Tech Univ Czech Inst Informat Robot & Cybernet Zikova St 1903-4 Prague 16636 6 Czech Republic

An acceptable response time of a server is an important aspect in many client-server applications;this is evident in situations in which the server is overloaded by many computationally intensive requests. In this work, we consider that the requests, or in this case tasks, generated by the clients are instances of optimization problems solved by anytime algorithms, i.e. the quality of the solution increases with the processing time of a task. These tasks are submitted to the server which schedules them to the available computational resources where the tasks are processed. To tackle the overload problem, we propose a scheduling algorithm which combines traditional scheduling approaches with a quality control heuristic which adjusts the requested quality of the solutions and thus changes the processing time of the tasks. Two efficient quality control heuristics are introduced: the first heuristic sets a global quality for all tasks, whereas the second heuristic sets the quality for each task independently. Moreover, in practice, the relationship between the processing time and the quality is not known a priori. Because it is crucial for scheduling algorithms to know at least the estimation of these relationships, we propose a general procedure for estimating these relationships using information obtained from the already executed tasks. Finally, the performance of the proposed scheduling algorithm is demonstrated on a real-world problem from the domain of personnel rostering with very good results. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Online scheduling anytime algorithms Machine learning Adaptive systems

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Ordered valuation algebras: a generic framework for approximating inference

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2004年第1期37卷 1-41页

作者： Haenni, R Univ Konstanz Ctr Jnr Res Fellows D-78457 Constance Germany

The paper presents a generic approach of approximating inference. The method is based on the concept of valuation algebras with its wide range of possible applications in many different domains. We present convenient resource-bounded anytime algorithms, where the maximal time of computation is determined by the user. (C) 2003 Elsevier Inc. All rights reserved.

关键词： approximation anytime algorithms resource-bounded computation valuation algebras local computation binary join trees bucket elimination minibuckets

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Deliberative on-line local path planning for autonomous mobile robots

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2003年第1期37卷 1-19页

作者： Diéguez, AR Sanz, R López, J Univ Vigo Dpto Ingn Sistemas & Automat ETS Ingn Vigo 36200 Spain

This paper describes a method for local path planning for mobile robots that combines reactive obstacle avoidance with on-line local path planning. Our approach is different to other model-based navigation approaches since it integrates both global and local planning processes in the same architecture while other methods only combine global path planning with a reactive method to avoid non-modelled obstacles. Our local planning is only triggered when an unexpected obstacle is found and reactive navigation is not able to regain the initial path. A new trajectory is then calculated on-line using only proximity sensor information. This trajectory can be improved during the available time using an anytime algorithm. The proposed method complements the reactive behaviour and allows the robot to navigate safely in a partially known environment during a long time period without human intervention.

关键词： local path planning obstacle avoidance robot navigation autonomous mobile robots anytime algorithms

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Time-bounded distributed QoS-aware service configuration in heterogeneous cooperative environments

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2009年第6期69卷 491-507页

作者： Nogueira, Luis Pinho, Luis Miguel Poltech Inst Porto ISEP IPP Sch Engn P-4200072 Oporto Portugal

The scarcity and diversity of resources among the devices of heterogeneous computing environments may affect their ability to execute services within the users' requested Quality of Service levels, particularly in open real-time environments where the characteristics of the computational load cannot always be predicted in advance but, nevertheless, response to events still has to be provided within precise timing constraints in order to guarantee a desired level of performance. This paper proposes a cooperative service execution, allowing resource constrained devices to collectively execute services with their more powerful neighbours, meeting non-functional requirements that otherwise would not be met by an individual execution. Nodes dynamically group themselves into a new coalition, allocating resources to each new service and establishing an initial service configuration which maximises the satisfaction of the QoS constraints associated with the new service and minimises the impact on the global QoS caused by the new service's arrival. However, the increased complexity of open real-time environments may prevent the possibility of computing optimal local and global resource allocations within a useful and bounded time. As such, the QoS optimisation problem is here reformulated as a heuristic-based anytime optimisation problem that can be interrupted at any time and quickly respond to environmental changes. Extensive simulations demonstrate that the proposed anytime algorithms are able to quickly find a good initial service solution and effectively optimise the rate at which the quality of the current solution improves at each iteration of the algorithms, with an overhead that can be considered negligible when compared against the introduced benefits. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Distributed systems Dynamic real-time systems Quality of service anytime algorithms

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Classification of streaming time series under more realistic assumptions

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DATA MINING AND KNOWLEDGE DISCOVERY 2016年第2期30卷 403-437页

作者： Hu, Bing Chen, Yanping Keogh, Eamonn Univ Calif Riverside Dept Comp Sci & Engn Riverside CA 92521 USA

Much of the vast literature on time series classification makes several assumptions about data and the algorithm's eventual deployment that are almost certainly unwarranted. For example, many research efforts assume that the beginning and ending points of the pattern of interest can be correctly identified, during both the training phase and later deployment. Another example is the common assumption that queries will be made at a constant rate that is known ahead of time, thus computational resources can be exactly budgeted. In this work, we argue that these assumptions are unjustified, and this has in many cases led to unwarranted optimism about the performance of the proposed algorithms. As we shall show, the task of correctly extracting individual gait cycles, heartbeats, gestures, behaviors, etc., is generally much more difficult than the task of actually classifying those patterns. Likewise, gesture classification systems deployed on a device such as Google Glass may issue queries at frequencies that range over an order of magnitude, making it difficult to plan computational resources. We propose to mitigate these problems by introducing an alignment-free time series classification framework. The framework requires only very weakly annotated data, such as "in this ten minutes of data, we see mostly normal heartbeats," and by generalizing the classic machine learning idea of data editing to streaming/continuous data, allows us to build robust, fast and accurate anytime classifiers. We demonstrate on several diverse real-world problems that beyond removing unwarranted assumptions and requiring essentially no human intervention, our framework is both extremely fast and significantly more accurate than current state-of-the-art approaches.

关键词： Time series classification Data dictionary anytime algorithms

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Exploiting case-based independence for approximating marginal probabilities

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 1996年第1期14卷 25-54页

作者： Shimony, SE Santos, E USAF INST TECHNOLDEPT ELECT & COMP ENGNWRIGHT PATTERSON AFBOH 45433

Computing marginal probabilities in Bayes networks is a hard problem. Deterministic anytime approximation schemes accumulate the probability mass in a small number of value assignments to the network variables. Under certain assumptions, the probability mass in the assignments is sufficient to obtain a good approximation. Such methods are especially useful for highly connected networks, where the topology makes the exact algorithms intractable. Bayes networks often possess a fine independence structure not evident from the topology, but apparent in local conditional distributions Independence-based (IB) assignments, originally proposed as a theory of abduction, take advantage of such independence, and thus contain fewer assigned variables-and more probability mass. We present several algorithms that use IB assignments for approximating marginal probabilities. Experimental results suggest that this approach is feasible for highly connected belief networks.

关键词： probabilistic reasoning Bayesian belief networks approximate belief updating approximating marginal probabilities anytime algorithms

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Mobile ID Document Recognition-Coarse-to-Fine Approach

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PATTERN RECOGNITION AND IMAGE ANALYSIS 2022年第1期32卷 89-108页

作者： Arlazarov, V. L. Arlazarov, V. V. Bulatov, K. B. Chernov, T. S. Nikolaev, D. P. Polevoy, D., V Sheshkus, A., V Skoryukina, N. S. Slavin, O. A. Usilin, S. A. Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow 119333 Russia Russian Acad Sci Inst Informat Transmiss Problems Kharkevich Inst Moscow 127051 Russia Smart Engines Serv LLC Moscow 121205 Russia

Automatic optical recognition of documents is a traditional function of modern document processing systems. In this context, recognition represents a complex process which includes image processing, segmentation, classification, and linguistic analysis. Although the idea of using mobile devices for recognition of paper documents is not new, direct usage of existing software solutions for scanned images recognition yields low recognition precision on images obtained using a mobile device. This is due, first of all, to perspective distortions and lower effective resolution in the latter case. In this paper, we present an original approach and a set of algorithms for recognition of video frame sequence containing a document image, which is suitable for mobile implementation. It is based on a coarse-to-fine methodology, where template matching and fields localization are performed on the image with lowered resolution, followed by lazy processing of parts of the images only corresponding to the fields which are not recognized yet. Video stream is utilized as a source of noise reduction both in coordinates of the fields and optical character recognition classifiers outputs. The algorithm based on the proposed approach is suitable for running on the device itself and can operate even when none of the video frames contain a document image of sufficient quality by themselves.

关键词： optical character recognition optical document recognition coarse-to-fine recognition real-time video processing frame quality evaluation document identification and matching anytime algorithms neural networks optimization

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Planning to Plan-Integrating Control Flow

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Tsinghua Science and Technology 2003年第1期8卷 1-7页

作者： Alexander Nareyek Computer Science Department Carnegie Mellon University

In many planning situations, computation itself becomes a resource to be planned and scheduled. We model such computational resources as conventional resources which are used by control-flow actions, e.g., to direct the planning process. Control-flow actions and conventional actions are planned/scheduled in an integrated way and can interact with each other. Control-flow actions are then executed by the planning engine itself. The approach is illustrated by examples, e.g., for hierarchical planning, in which tasks that are temporally still far away impose only rough constraints on the current schedule, and control-flow tasks ensure that these tasks are refined as they approach the current time. Using the same mechanism, anytime algorithms can change appropriate search methods or parameters over time, and problems like scheduling critical time-outs for garbage collection can be made part of the planning itself.

关键词： meta-planning resource-bounded reasoning online planning integrated planning, sensing and execution integrated planning and scheduling hierarchical planning anytime algorithms local search metaheuristics

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