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Improving the anytime behavior of two-phase local search

ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2011年第2期61卷 125-154页

作者： Dubois-Lacoste, Jeremie Lopez-Ibanez, Manuel Stutzle, Thomas Univ Libre Bruxelles IRIDIA Brussels Belgium

algorithms based on the two-phase local search (TPLS) framework are a powerful method to efficiently tackle multi-objective combinatorial optimization problems. TPLS algorithms solve a sequence of scalarizations, that is, weighted sum aggregations, of the multi-objective problem. Each successive scalarization uses a different weight from a predefined sequence of weights. TPLS requires defining the stopping criterion (the number of weights) a priori, and it does not produce satisfactory results if stopped before completion. Therefore, TPLS has poor "anytime" behavior. This article examines variants of TPLS that improve its "anytime" behavior by adaptively generating the sequence of weights while solving the problem. The aim is to fill the "largest gap" in the current approximation to the Pareto front. The results presented here show that the best adaptive TPLS variants are superior to the "classical" TPLS strategies in terms of anytime behavior, matching, and often surpassing, them in terms of final quality, even if the latter run until completion.

关键词： Multi-objective anytime algorithms Two-phase local search

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GUARANTEEING REAL-TIME RESPONSE WITH LIMITED RESOURCES

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ARTIFICIAL INTELLIGENCE IN MEDICINE 1993年第1期5卷 49-66页

作者： ASH, D GOLD, G SEIVER, A HAYESROTH, B STANFORD UNIV KNOWLEDGE SYST LABPALO ALTOCA 94304

Unanticipated problems detected by patient-monitoring systems may sometimes require real-time response in order to provide high-quality care and avoid catastrophic outcomes. In this paper, we present an approach for guaranteeing a response to such events by a monitoring agent even in situations where we have limited problem-solving resources. We show that an action-based hierarchy can accomplish this goal. We also analyze the performance of this hierarchy under varying resource availability and discuss decision-theoretic approaches to enable us to best structure such a hierarchy. We also describe an implementation of these ideas, called ReAct, in the BB1 architecture. All the ideas are illustrated with examples from the surgical intensive care unit (SICU).

关键词： ICU MONITORING REAL TIME RESOURCE LIMITATIONS anytime algorithms DIAGNOSIS DECISION THEORY

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anytime argumentative and abductive reasoning

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SOFT COMPUTING 2003年第2期8卷 142-149页

作者： Haenni, R Univ Konstanz Ctr Junior Res Fellows D-7750 Constance Germany

This article presents a new approximation method for computing arguments or explanations in the context of logic-based argumentative or abductive reasoning. The algorithm can be interrupted at any time returning the solution found so far. The quality of the approximation increases monotonically when more computational resources are available. The method is based on cost functions and returns lower and upper bounds.

关键词： probabilistic argumentation abduction cost-bounded approximation anytime algorithms

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On optimal stopping strategies for text recognition in a video stream as an application of a monotone sequential decision model

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INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION 2019年第3期22卷 303-314页

作者： Bulatov, Konstantin Razumnyi, Nikita Arlazarov, Vladimir V. FRC CSC RAS Inst Syst Anal Moscow Russia Smart Engines Moscow Russia Moscow Inst Phys & Technol Moscow Russia

The paper describes the problem of stopping the text field recognition process in a video stream, which is a novel problem, particularly relevant to real-time mobile document recognition systems. A decision-theoretic framework for this problem is provided, and similarities with existing stopping rule problems are explored. Following the theoretical works on monotone stopping rule problems, a strategy is proposed based on thresholding the estimation of the expected difference between consequent recognition results. The efficiency of this strategy is evaluated on an openly accessible dataset. The results show that this method outperforms the previously published methods based on identical results cluster size thresholding. Notes on future work include incorporation of recognition result confidence estimations in the proposed model and more precise evaluation of the observation cost.

关键词： Recognition in video stream Mobile OCR Stopping rules Decision making Mobile document recognition anytime algorithms

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An anytime assignment algorithm: From local task swapping to global optimality

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AUTONOMOUS ROBOTS 2013年第4期35卷 271-286页

作者： Liu, Lantao Shell, Dylan A. Texas A&M Univ Dept Comp Sci & Engn College Stn TX 77843 USA

The assignment problem arises in multi-robot task-allocation scenarios. Inspired by existing techniques that employ task exchanges between robots, this paper introduces an algorithm for solving the assignment problem that has several appealing features for online, distributed robotics applications. The method may start with any initial matching and incrementally improve the current solution to reach the global optimum, producing valid assignments at any intermediate point. It is an any-time algorithm with a performance profile that is attractive: quality improves linearly with stages (or time). Additionally, the algorithm is comparatively straightforward to implement and is efficient both theoretically (complexity of is better than many widely used solvers) and practically (comparable to the fastest implementation, for up to hundreds of robots/tasks). The algorithm generalizes "swap" primitives used by existing task exchange methods already used in the robotics community but, uniquely, is able to obtain global optimality via communication with only a subset of robots during each stage. We present a centralized version of the algorithm and two decentralized variants that trade between computational and communication complexity. The centralized version turns out to be a computational improvement and reinterpretation of the little-known method of Balinski-Gomory proposed half a century ago. Thus, deeper understanding of the relationship between approximate swap-based techniques-developed by roboticists-and combinatorial optimization techniques, e.g., the Hungarian and Auction algorithms-developed by operations researchers but used extensively by roboticists-is uncovered.

关键词： Multi-robot task allocation Decentralized assignment anytime algorithms Task swapping

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Accelerating Dynamic Time Warping Clustering with a Novel Admissible Pruning Strategy 15

Accelerating Dynamic Time Warping Clustering with a Novel Ad...

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21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

作者： Begum, Nurjahan Ulanova, Liudmila Wang, Jun Keogh, Eamonn Univ Calif Riverside Riverside CA 92521 USA Univ Texas Dallas Dallas TX USA

ISBN: (纸本)9781450336642

Clustering time series is a useful operation in its own right, and an important subroutine in many higher-level data mining analyses, including data editing for classifiers, summarization, and outlier detection. While it has been noted that the general superiority of Dynamic Time Warping (DTW) over Euclidean Distance for similarity search diminishes as we consider ever larger datasets, as we shall show, the same is not true for clustering. Thus, clustering time series under DTW remains a computationally challenging task. In this work, we address this lethargy in two ways. We propose a novel pruning strategy that exploits both upper and lower bounds to prune off a large fraction of the expensive distance calculations. This pruning strategy is admissible;giving us provably identical results to the brute force algorithm, but is at least an order of magnitude faster. For datasets where even this level of speedup is inadequate, we show that we can use a simple heuristic to order the unavoidable calculations in a most-useful-first ordering, thus casting the clustering as an anytime algorithm. We demonstrate the utility of our ideas with both single and multidimensional case studies in the domains of astronomy, speech physiology, medicine and entomology.

关键词： Clustering Time Series anytime algorithms

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Effectively Handling New Relationship Formations in Closeness Centrality Analysis of Social Networks using anytime Anywhere Methodology 6

Effectively Handling New Relationship Formations in Closenes...

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2016 IEEE International Conferences on BDCloud/SocialCom/SustainCom 2016

作者： Santos, Eunice E. Korah, John Murugappan, Vairavan Subramanian, Suresh IIT Dept Comp Sci Chicago IL 60616 USA

ISBN: (纸本)9781509039364

The flood of real time social data, generated by various social media applications and sensors, is enabling researchers to gain critical insights into important social modeling and analysis problems such as the evolution of social relationships and analysis of emergent social processes. However, current computational tools have to address the grand challenge of analyzing large and dynamic social networks within strict time constraints before the available social data can be effectively utilized. The computational issues are further exacerbated by the network size, which can range in the millions of nodes, and by the need for analytical tools to work with various computational architectures. Existing methodologies primarily deal with dynamic relationships in social networks by simply re-computing the results, and relying on massive parallel and distributed processing resources to maintain time constraints. In previous work, we introduced an overarching parallel/distributed algorithm design framework called the anytime anywhere framework, which leverages the inherent iterative property of graph algorithms to generate partial results, whose quality increase with the processing time, and which efficiently incorporates network changes. In this paper, we focus on closeness centrality algorithm design for dynamic social networks where new relationships are formed due to edge additions. Using both theoretical analysis and empirical results, we will demonstrate how this algorithm efficiently reuses the partial results and reduces the need for re-computations.

关键词： social network analysis parallel and distributed processing centrality analysis dynamic graphs anytime algorithms

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anytime Subgroup Discovery in Numerical Domains with Guarantees

Anytime Subgroup Discovery in Numerical Domains with Guarant...

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Belfodil, Aimene Belfodil, Adnene Kaytoue, Mehdi Univ Lyon CNRS INSA Lyon LIRIS UMR 5205 F-69621 Lyon France Mobile Devices Ingn 100 Ave Stalingrad F-94800 Villejuif France Infologic 99 Ave Lyon F-26500 Bourg Les Valence France

ISBN: (纸本)9783030109288;9783030109271

Subgroup discovery is the task of discovering patterns that accurately discriminate a class label from the others. Existing approaches can uncover such patterns either through an exhaustive or an approximate exploration of the pattern search space. However, an exhaustive exploration is generally unfeasible whereas approximate approaches do not provide guarantees bounding the error of the best pattern quality nor the exploration progression ("How far are we of an exhaustive search"). We design here an algorithm for mining numerical data with three key properties w.r.t. the state of the art: (i) It yields progressively interval patterns whose quality improves over time;(ii) It can be interrupted anytime and always gives a guarantee bounding the error on the top pattern quality and (iii) It always bounds a distance to the exhaustive exploration. After reporting experimentations showing the effectiveness of our method, we discuss its generalization to other kinds of patterns. Code related to this paper is available at: https://***/Adnene93/RefineAndMine.

关键词： Subgroup discovery anytime algorithms Discretization

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Degradable Inference for Energy Autonomous Vision Applications

Degradable Inference for Energy Autonomous Vision Applicatio...

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ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) / ACM International Symposium on Wearable Computers (ISWC)

作者： Montanari, Alessandro Alloulah, Mohammed Kawsar, Fahim Nokia Bell Labs Cambridge England Delft Univ Technol Delft Netherlands

ISBN: (纸本)9781450368698

Mobile vision systems, often battery-powered, are now incredibly powerful in capturing, analyzing, and understanding real-world events uncovering interminable opportunities for new applications in the areas of life-logging, cognitive augmentation, security, safety, wildlife surveillance, etc. There are two complementary challenges in the design of a mobile vision system today - improving the recognition accuracy at the expense of minimum energy consumption. In this work, we posit that best-effort sensing with degradable featurization and an elastic inference pipeline offers an interesting avenue to bring energy autonomy to mobile vision systems while ensuring acceptable recognition performance. Borrowing principles from Intermittent Computing, and Numerical Computing we propose such best-effort sensing using a Degradable-Inference pipeline supported by a parameterized Discrete Cosine Transformation (DCT) based featurization and an anytime Deep Neural Network. These two principles aim at extending the lifetime of a mobile vision system while minimizing compute and communication cost without compromising recognition performance. We report the design and early characterization of our proposed solution.

关键词： Energy autonomous anytime algorithms neural networks

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An anytime Querying Algorithm for Predicting Cardiac Arrest in Children: Work-in-Progress 20th

An Anytime Querying Algorithm for Predicting Cardiac Arrest ...

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20th International Conference on Artificial Intelligence in Medicine (AIME)

作者： Skinner, Michael A. Yu, Priscilla Raman, Lakshmi Natarajan, Sriraam Univ Texas Dallas Dallas TX 75080 USA Univ Texas Southwestern Med Ctr Dallas Dallas TX 75390 USA

ISBN: (纸本)9783031093425;9783031093418

Cardiac arrest (CA) is a devastating complication for children in the cardiac intensive care unit (CICU). We developed an "anytime" algorithm to predict CA, using the first few hours of EHR data for initial approximation, and then using information from subsequent time periods to augment the predictive model, improving performance at each iteration. Our initial empirical evaluation on EHR CICU data shows that the model achieves significantly higher performance than learning with all the available data at each iteration when predicting CA inside CICU.

关键词： Gradient boosting anytime algorithms Cardiac arrest

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