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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Rituraj Rituraj Annamária R. Várkonyi Kóczy Doctoral School of Applied Informatics and Applied Mathematics Óbuda University H-1034 Budapest Bécsi u. 96/b Hungary Kálmán Faculty of Electrical Engineering Óbuda University H-1034 Budapest Bécsi u. 96/b Hungary

ISBN: (数字)9781728131498

ISBN: (纸本)9781728131504

Data is becoming an important source of information these days. According to the recent Forbes survey, there are 2.5 quintillion bytes of data created each day at our current pace. While getting these amounts of data, it is necessary to process them in order to extract information. These data can be accessed in many ways in order to get meaningful information. This paper only deals with the query plans model through multi-objective optimization process using anytime algorithm. Query plans is an ordered stairway used for accessing data in SQL relational database systems. Query plans provides diverse tradeoff between conflicting cost matrices. The cost matrices are execution time, energy consumption and execution fees in multi-objective aspects. When SQL database run the queries by choosing an optimum query execution plan then it minimizes the query cost, which is very crucial for the query optimizer. Multiobjective query optimization and anytime algorithm possess very specific properties in order to support an interactive process which dynamically add various constraints and then finally select the best plan based on the continuously refined visualization of optimal cost tradeoffs. First, the anytime algorithms generate the multiple result plan sets which increase the quality with low latency rate between consecutive results. Second, the consecutive results will be incremented to avoid regenerating query plans when being invoked several times for the same query but with different user constraints. This paper deals with the advantages of anytime algorithm for the multi objective query optimization to analyze the complexity which offers an attractive tradeoff between the results. It can be used to update frequency, single invocation time complexity and multiple time over invocations. These properties make anytime algorithm suitable to be used within an interactive query optimization process.

关键词： Query plans Multi-objective optimization anytime algorithm query analysis Information source SQL database Optimization Procedure algorithms interactive processing tradeoffs specific properties

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An anytime algorithm for Camera-Based Character Recognition

An Anytime Algorithm for Camera-Based Character Recognition

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International Conference on Document Analysis and Recognition

作者： Takuya Kobayashi Masakazu Iwamura Takahiro Matsuda Koichi Kise Graduate School of Engineering Osaka Prefecture University

ISBN: (纸本)9781479901937

In a scene image, some characters are difficult to recognize and some others are recognized easily. Such difficult characters usually make the processing time long while easy characters are recognized in a short time. In this paper, we propose a system which recognizes each character with a proper cost for the difficulty. Through the process, easy characters are recognized early and difficult ones are recognized late. This is a desired property of an anytime algorithm that the recognition accuracy does not decrease as the time increases. In order to realize it, we propose a method which splits the recognition process into several times and accumulates the recognition results and extracted features. We also discuss what is required to realize the anytime algorithm for the scene character recognition task. Experiments reveal that the proposed method obtains recognition results of easy characters earlier than the conventional method.

关键词： Scene character recognition anytime algorithm Local feature ASIFT Tracking Video input

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anytime algorithm based on adaptive variable-step-size mechanism for path planning of UAVs

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Chinese Journal of Aeronautics 2024年

作者： Hui GAO Yuhong JIA Liwen XU Fengxing PAN Shaowei LI Yaoming ZHOU School of Aeronautic Science and Engineering Beihang University Beijing 100191 China Beijing Advanced Discipline Center for Unmanned Aircraft System Beihang University Beijing 100191 China Tianmushan Laboratory Yuhang District Hangzhou 31115 China

For autonomous Unmanned Aerial Vehicles (UAVs) flying in real-world scenarios, time for path planning is always limited, which is a challenge known as the anytime problem. anytime planners address this by finding a collision-free path quickly and then improving it until time runs out, making UAVs more adaptable to different mission scenarios. However, current anytime algorithms based on A* have insufficient control over the suboptimality bounds of paths and tend to lose their anytime properties in environments with large concave obstacles. This paper proposes a novel anytime path planning algorithm, anytime Radiation A* (ARaA*), which can generate a series of suboptimal paths with improved bounds through decreasing search step sizes and can generate the optimal path when time is sufficient. The ARaA* features two main innovations: an adaptive variable-step-size mechanism and elliptic constraints based on waypoints. The former helps achieve fast path searching in various environments. The latter allows ARaA* to control the suboptimality bounds of paths and further enhance search efficiency. Simulation experiments show that the ARaA* outperforms anytime Repairing A* (ARA*) and anytime D* (AD*) in controlling suboptimality bounds and planning time, especially in environments with large concave obstacles. Final flight experiments demonstrate that the paths planned by ARaA* can ensure the safe flight of quadrotors.

关键词： Path planning anytime algorithm Adaptive variable-step-size Suboptimality bound Unmanned aerial vehicle (UAV)

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iADA*: Improved anytime Path Planning and Replanning algorithm for Autonomous Vehicle

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2020年第3-4期100卷 1005-1013页

作者： Maw, Aye Aye Tyan, Maxim Lee, Jae-Woo Konkuk Univ Dept Aerosp Informat Engn Seoul South Korea

Path planning of autonomous mobile robots in a real-world environment presents several challenges which are usually not raised in other areas. The real world is inherently complex, uncertain and dynamic. Therefore, accurate models of path planning are difficult to obtain and quickly become outdated. anytime planners are ideal for this type of problem as they can find an initial solution very quickly and then improve it as time allows. This paper proposes a new anytime incremental search algorithm named improved anytime Dynamic A*(iADA*). The algorithm is based on the currently popular anytime heuristic search algorithm, which is anytime Dynamic A*(ADA*). The iADA* algorithm improves the calculation of the path lengths and decreases the calculating frequency of the path throughout the search, making it significantly faster. The algorithm is designed to provide an efficient solution to a complex, dynamic search environment when the locally changes affected. Our study shows that the two-dimensional path-planning iADA* experiments were between 2.0 to 3.7 times faster than ADA*, both in partially known and fully unknown dynamic environments. Additionally, in this paper shows the experiment results of the comparison with other four existing algorithms based on computing time and path lengths. iADA* was an average 2.57 times reduced on the computational time for the environment which locally changes effected. For the path length is little increase, but it is not the worst case. According to the experiments, the more the environmental problems and complexity increases, the more iADA* provides a rapid in-search time and total time to obtain the final solution.

关键词： anytime algorithm Path planning and replanning Concurrent path planner Real-time path planner Dynamic environment

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An anytime Visibility-Voronoi graph-search algorithm for generating robust and feasible unmanned surface vehicle paths

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AUTONOMOUS ROBOTS 2022年第8期46卷 911-927页

作者： Schoener, Marco Coyle, Eric Thompson, David Embry Riddle Aeronaut Univ 1 Aerosp Blvd Daytona Beach FL 32114 USA

While path planning for Unmanned Surface Vehicles (USVs) is in many ways similar to path planning for ground vehicles, the lack of reliable USV models and significant maritime environmental uncertainties requires an increased focus on robustness and safety. This paper presents a novel graph construction method based on Visibility-Voronoi diagrams that allow users to tune path optimality and path safety while considering vehicle dynamics and model uncertainty. The vehicle state is defined as both a 2D location and heading. The method is based on a roadmap generated from a Visibility-Voronoi diagram, and uses motion curves and path smoothing to ensure path feasibility. The roadmap can then be searched using any graph-search algorithm to return optimal paths subject to a cost function. This paper also shows how to generate and search this roadmap in an anytime fashion, which makes the method suitable for local planning where sensors are used to build a map of the environment in real-time. This approach is demonstrated effectively on underactuated systems, with empirical results from USV docking and obstacle field navigation scenarios. These case studies show the path maintains feasibility subject to a simplified vehicle model, and is able to maximize safety when navigating close to obstacles. Simulation results are also used to analyze algorithm complexity, prove suitability for local planning, and demonstrate the benefits of anytime roadmap generation.

关键词： USV Path planning Graph search Feasibility anytime algorithm

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Efficient anytime algorithms to solve the bi-objective Next Release Problem

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JOURNAL OF SYSTEMS AND SOFTWARE 2019年 156卷 217-231页

作者： Angel Dominguez-Rios, Miguel Chicano, Francisco Alba, Enrique del Aguila, Isabel del Sagrado, Jose Univ Malaga Dept Lenguajes & Ciencias Comp Malaga Spain Univ Almeria Dept Informat Almeria Spain

The Next Release Problem consists in selecting a subset of requirements to develop in the next release of a software product. The selection should be done in a way that maximizes the satisfaction of the stake-holders while the development cost is minimized and the constraints of the requirements are fulfilled. Recent works have solved the problem using exact methods based on Integer Linear Programming. In practice, there is no need to compute all the efficient solutions of the problem;a well-spread set in the objective space is more convenient for the decision maker. The exact methods used in the past to find the complete Pareto front explore the objective space in a lexicographic order or use a weighted sum of the objectives to solve a single-objective problem, finding only supported solutions. In this work, we propose five new methods that maintain a well-spread set of solutions at any time during the search, so that the decision maker can stop the algorithm when a large enough set of solutions is found. The methods are called anytime due to this feature. They find both supported and non-supported solutions, and can complete the whole Pareto front if the time provided is long enough. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Next release problem Multi-objective optimization Search-based software engineering anytime algorithm Pareto front

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anytime algorithms for Non-Ending Computations

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INTERNATIONAL JOURNAL OF FOUNDATIONS OF COMPUTER SCIENCE 2015年第4期26卷 465-475页

作者： Calude, Cristian S. Desfontaines, Damien Univ Auckland Dept Comp Sci Auckland 1 New Zealand Ecole Normale Super F-75005 Paris France

A program which eventually stops but does not halt "too quickly" halts at a time which is algorithmically compressible. This result originally proved in [4] - is proved in a more general setting. Following Mania [11] we convert the result into an anytime algorithm for the halting problem and we show that the stopping time (en off temporal bound) cannot be significantly improved.

关键词： anytime algorithm algorithmically compressible number halting problem density

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anytime algorithms for multi-agent visibility-based pursuit-evasion games 12

Anytime algorithms for multi-agent visibility-based pursuit-...

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Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3

作者： Viliam Lisý Branislav Bošanský Michal Pěchouček Czech Technical University Technická Prague Czech Republic

ISBN: (纸本)9780981738130

We investigate algorithms for playing multi-agent visibility-based pursuit-evasion games. A team of pursuers attempts to maintain visibility contact with an evader who actively avoids tracking. We aim for applicability of the algorithms in real-world scenarios; hence, we impose hard constraints on the run-time of the algorithms and we evaluate them in a simulation model based on a real-world urban area. We compare Monte-Carlo tree search (MCTS) and iterative deepening minimax algorithms running on the information-set tree of the imperfect-information game. The experimental results demonstrate that both methods create comparable good strategies for the pursuer, while the later performs better in creating the evader's strategy.

关键词： informationset search Monte-Carlo tree search anytime algorithm pursuit-evasion game

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A batch informed sampling-based algorithm for fast anytime asymptotically-optimal motion planning in cluttered environments

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EXPERT SYSTEMS WITH APPLICATIONS 2020年第0期144卷 113124-000页

作者： Xu, Jing Song, Kechen Dong, Hongwen Yan, Yunhui Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China

Practical applications favor anytime asymptotically-optimal algorithms that find and improve an initial solution toward the optimal solution as quickly as possible due to the algorithms may be terminated at any time. We present Batch-to-batch Informed Fast Marching Tree (BBI-FMT*), an anytime asymptotically-optimal sampling-based algorithm that is designed for solving complex motion planning problems. The proposed algorithm has the ability to fast find an initial low-cost solution by the batch sampling-based incremental search and the "lazy" optimal search, then it employs the batch informed sampling-based incremental search and the anytime optimal search to quickly improve the tree and achieve the optimal solution. The proposed anytime optimal search strategy integrates the "lazy" and "non-lazy" optimal search to efficiently improve the tree to the minimum-cost spanning tree in cluttered environments. This paper theoretically analyzes the proposed algorithm in depth and evaluates it by numerical experiments under a few challenging scenarios. The experimental results show that BBI-FMT* outperforms the state-of-the-art algorithms in the self-adaptability, robustness, convergence rate, and success rate of the planning. The proposed algorithm can be widely applied to intelligent robots with expert systems to improve the efficiency and stability of the motion planning and navigation modules which are the core modules in the expert systems. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Motion planning anytime algorithm Asymptotic optimality Optimal path planning

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A Recommendation System in Cognitive Radio Networks With Random Data Traffic

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2011年第4期60卷 1352-1364页

作者： Li, Husheng Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA

A recommendation system is proposed to enhance the efficiency of spectrum access in cognitive radio networks, in which secondary users broadcast the indices of channels that they have successfully accessed. The probabilities of different actions, i.e., taking a recommendation or probing an unrecommended channel, could be either fixed or adjustable. For the constant probability case with and without retransmission, the system is modeled as a Markov random process, and the corresponding state transition probabilities are obtained. For the adjustable probability case, the anytime multiarmed bandit technique is used to adopt the strategies to the uncertain environments, and a performance lower bound is obtained. Numerical simulation results demonstrate that the proposed recommendation system can effectively orient the channel selections and significantly improve the performance of cognitive radio networks.

关键词： anytime algorithm channel selection cognitive radio networks recommendation

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