基于刚性图的多智能体编队控制研究

对多智能体编队执行结点扩展、集结、分离等操作后,保持编队队形稳定的问题进行研究。利用代数图论为研究工具,介绍了刚性图和最小刚性图的概念,基于刚性图理论对多智能体编队操作进行形式化描述和数学建模,重点研究了结点增减、编队集结、分离等操作下编队刚性保持的条件,并给出保持编队刚性的理论证明。采用编队控制的形式化建模方法,可对进一步深入编队队形控制算法以及控制器的设计等问题提供编队操作的形式化表达,具有一定的借鉴意义。     

Exploration Strategies for Model-based Learning in Multi-agent Systems: Exploration Strategies

An agent that interacts with other agents in multi-agent systems can benefit significantly from adapting to the others. When performing active learning, every agent's action affects the interaction process in two ways: The effect on the expected reward according to the current knowledge held by the agent, and the effect on the acquired knowledge, and hence, on future rewards expected to be received. The agent must therefore make a tradeoff between the wish to exploit its current knowledge, and the wish to explore other alternatives, to improve its knowledge for better decisions in the future. The goal of this work is to develop exploration strategies for a model-based learning agent to handle its encounters with other agents in a common environment. We first show how to incorporate exploration methods usually used in reinforcement learning into model-based learning. We then demonstrate the risk involved in exploration—an exploratory action taken by the agent can yield a better model of the other agent but also carries the risk of putting the agent into a much worse position.We present the lookahead-based exploration strategy that evaluates actions according to their expected utility, their expected contribution to the acquired knowledge, and the risk they carry. Instead of holding one model, the agent maintains a mixed opponent model, a belief distribution over a set of models that reflects its uncertainty about the opponent's strategy. Every action is evaluated according to its long run contribution to the expected utility and to the knowledge regarding the opponent's strategy. Risky actions are more likely to be detected by considering their expected outcome according to the alternative models of the opponent's behavior. We present an efficient algorithm that returns an almost optimal exploration plan against the mixed model and provide a proof of its correctness and an analysis of its complexity.We report experimental results in the Iterated Prisoner's Dilemma domain, comparing the capabilities of the different exploration strategies. The experiments demonstrate the superiority of lookahead-based exploration over other exploration methods.     

Graph Decomposition for Memoryless Periodic Exploration

We consider a general framework in which a memoryless robot periodically explores all the nodes of a connected anonymous graph by following local information available at each vertex. For each vertex?v, the endpoints of all edges adjacent to v are assigned unique labels within the range?1 to?deg?(v) (the degree of?v). The generic exploration strategy is implemented using a right-hand-rule transition function: after entering vertex v via the edge labeled?i, the robot proceeds with its exploration, leaving via the edge having label [i mod deg?(v)]+1 at v. A lot of attention has been given to the problem of labeling the graph so as to achieve a periodic exploration having the minimum possible length?π. It has recently been proved (Czyzowicz et?al., Proc.?SIROCCO’09, 2009) that $\pi\leq4\frac{1}{3}n$ holds for all graphs of n vertices. Herein, we provide a new labeling scheme which leads to shorter exploration cycles, improving the general bound to π≤4n?2. This main result is shown to be tight with respect to the class of labellings admitting certain connectivity properties. The labeling scheme is based on a new graph decomposition which may be of independent interest.     

Efficient Relational Techniques for Processing Graph Queries

Graphs are widely used for modeling complicated data such as social networks, chemical compounds, protein interactions and semantic web. To effectively understand and utilize any collection of graphs, a graph database that efficiently supports elementary querying mechanisms is crucially required. For example, Subgraph and Supergraph queries are important types of graph queries which have many applications in practice. A primary challenge in computing the answers of graph queries is that pair-wise comparisons of graphs are usually hard problems. Relational database management systems (RDBMSs) have repeatedly been shown to be able to efficiently host different types of data such as complex objects and XML data. RDBMSs derive much of their performance from sophisticated optimizer components which make use of physical properties that are specific to the relational model such as sortedness, proper join ordering and powerful indexing mechanisms. In this article, we study the problem of indexing and querying graph databases using the relational infrastructure. We present a purely relational framework for processing graph queries. This framework relies on building a layer of graph features knowledge which capture metadata and summary features of the underlying graph database. We describe different querying mechanisms which make use of the layer of graph features knowledge to achieve scalable performance for processing graph queries. Finally, we conduct an extensive set of experiments on real and synthetic datasets to demonstrate the efficiency and the scalability of our techniques.     

Piecemeal Graph Exploration by a Mobile Robot

We study how a mobile robot can learn an unknown environment in a piecemeal manner. The robot's goal is to learn a complete map of its environment, while satisfying the constraint that it must return every so often to its starting position (for refueling, say). The environment is modeled as an arbitrary, undirected graph, which is initially unknown to the robot. We assume that the robot can distinguish vertices and edges that it has already explored. We present a surprisingly efficient algorithm for piecemeal learning an unknown undirected graph G=(V, E) in which the robot explores every vertex and edge in the graph by traversing at most O(E+V^1+o(1)) edges. This nearly linear algorithm improves on the best previous algorithm, in which the robot traverses at most O(E+V²) edges. We also give an application of piecemeal learning to the problem of searching a graph for a “treasure.”     

Techniques for Edge Stratification of Complex Graph Drawings

We propose an approach that allows a user (e.g., an analyst) to explore a layout produced by any graph drawing algorithm, in order to reduce the visual complexity and clarify its presentation. Our approach is based on stratifying the drawing into layers with desired properties; to this aim, heuristics are presented. The produced layers can be explored and combined by the user to gradually acquire details. We present a user study to test the effectiveness of our approach. Furthermore, we performed an experimental analysis on popular force-directed graph drawing algorithms, in order to evaluate what is the algorithm that produces the smallest number of layers and if there is any correlation between the number of crossings and the number of layers of a graph layout. The proposed approach is useful to explore graph layouts, as confirmed by the presented user study. Furthermore, interesting considerations arise from the experimental evaluation, in particular, our results suggest that the number of layers of a graph layout may represent a reliable measure of its visual complexity. The algorithms presented in this paper can be effectively applied to graph layouts with a few hundreds of edges and vertices. For larger drawings that contain lots of crossings, the time complexity of our algorithms grows quadratically in the number of edges and more efficient techniques need to be devised. The proposed approach takes as input a layout produced by any graph drawing algorithm, therefore it can be applied in a variety of application domains. Several research directions can be explored to extend our framework and to devise new visualization paradigms to effectively present stratified drawings.     

Generalizing the TT-transform

The TT-transform is a method of dividing a primary time series into a set of secondary, time-localized time series, through use of a translatable, scalable Gaussian window. These secondary time series resemble ordinary windowed time series, except that higher frequencies are more strongly concentrated around the midpoint of the Gaussian, as compared with lower frequencies. In this paper the TT-transform is generalized to accommodate arbitrary scalable windows. The generalized TT-transform can be useful in resolving the times of event initiations when used jointly with a related time–frequency distribution, the generalized S-transform.     

Generalizing Version Spaces

Although a landmark work, version spaces have proven fundamentally limited by being constrained to only consider candidate classifiers that are strictly consistent with data. This work generalizes version spaces to partially overcome this limitation. The main insight underlying this work is to base learning on version-space intersection, rather than the traditional candidate-elimination algorithm. The resulting learning algorithm, incremental version-space merging (IVSM), allows version spaces to contain arbitrary sets of classifiers, however generated, as long as they can be represented by boundary sets. This extends version spaces by increasing the range of information that can be used in learning; in particular, this paper describes how three examples of very different types of information—ambiguous data, inconsistent data, and background domain theories as traditionally used by explanation-based learning—can each be used by the new version-space approach.     

Generalizing relevance weighted LDA

In this paper, a new variant on linear discriminant analysis (LDA) that we refer to as generalizing relevance weighted LDA or GRW-LDA is proposed. GRW-LDA extends the applicability to cases that LDA cannot handle by combining the advantages of two recent LDA enhancements, namely generalized singular value decomposition based LDA and relevance weighted LDA. Experimental results on FERET face database demonstrate the effectiveness of the proposed method.     

Generalizing sampling-based multilingual alignment

Sub-sentential alignment is the process by which multi-word translation units are extracted from sentence-aligned multilingual parallel texts. This process is required, for instance, in the course of training statistical machine translation systems. Standard approaches typically rely on the estimation of several probabilistic models of increasing complexity and on the use of various heuristics, that make it possible to align, first isolated words, then, by extension, groups of words. In this paper, we explore an alternative approach which relies on a much simpler principle: the comparison of occurrence profiles in sub-corpora obtained by sampling. After analyzing the strengths and weaknesses of this approach, we show how to improve the detection of multi-word translation units and evaluate these improvements on machine translation tasks.     

Cubegrades: Generalizing Association Rules

Cubegrades are a generalization of association rules which represent how a set of measures (aggregates) is affected by modifying a cube through specialization (rolldown), generalization (rollup) and mutation (which is a change in one of the cube's dimensions). Cubegrades are significantly more expressive than association rules in capturing trends and patterns in data because they can use other standard aggregate measures, in addition to COUNT. Cubegrades are atoms which can support sophisticated what if analysis tasks dealing with behavior of arbitrary aggregates over different database segments. As such, cubegrades can be useful in marketing, sales analysis, and other typical data mining applications in business.In this paper we introduce the concept of cubegrades. We define them and give examples of their usage. We then describe in detail an important task for computing cubegrades: generation of significant cubes whichis analogous to generating frequent sets. A novel Grid Based Pruning (GBP) method is employed for this purpose. We experimentally demonstrate the practicality of the method. We conclude with a number of open questions and possible extensions of the work.     

Generalizing predicates with string arguments

The least general generalization (LGG) of strings may cause an over-generalization in the generalization process of the clauses of predicates with string arguments. We propose a specific generalization (SG) for strings to reduce over-generalization. SGs of strings are used in the generalization of a set of strings representing the arguments of a set of positive examples of a predicate with string arguments. In order to create a SG of two strings, first, a unique match sequence between these strings is found. A unique match sequence of two strings consists of similarities and differences to represent similar parts and differing parts between those strings. The differences in the unique match sequence are replaced to create a SG of those strings. In the generalization process, a coverage algorithm based on SGs of strings or learning heuristics based on match sequences are used. Ilyas Cicekli received a Ph.D. in computer science from Syracuse University in 1991. He is currently a professor of the Department of Computer Engineering at Bilkent University. From 2001 till 2003, he was a visiting faculty at University of Central Florida. His current research interests include example-based machine translation, machine learning, natural language processing, and inductive logic programming. Nihan Kesim Cicekli is an Associate Professor of the Department of Computer Engineering at the Middle East Technical University (METU). She graduated in computer engineering at the Middle East Technical University in 1986. She received the MS degree in computer engineering at Bilkent University in 1988; and the PhD degree in computer science at Imperial College in 1993. She was a visiting faculty at University of Central Florida from 2001 till 2003. Her current research interests include multimedia databases, semantic web, web services, data mining, and machine learning.     

Generalizing the notion of confidence

In this paper, we explore extending association analysis to non-traditional types of patterns and non-binary data by generalizing the notion of confidence. We begin by describing a general framework that measures the strength of the connection between two association patterns by the extent to which the strength of one association pattern provides information about the strength of another. Although this framework can serve as the basis for designing or analyzing measures of association, the focus in this paper is to use the framework as the basis for extending the traditional concept of confidence to error-tolerant itemsets (ETIs) and continuous data. To that end, we provide two examples. First, we (1) describe an approach to defining confidence for ETIs that preserves the interpretation of confidence as an estimate of a conditional probability, and (2) show how association rules based on ETIs can have better coverage (at an equivalent confidence level) than rules based on traditional itemsets. Next, we derive a confidence measure for continuous data that agrees with the standard confidence measure when applied to binary transaction data. Further analysis of this result exposes some of the important issues involved in constructing a confidence measure for continuous data. Michael Steinbach earned the B.S. degree in mathematics, the M.S. degree in statistics, and the M.S. and Ph.D. degrees in computer science, all from the University of Minnesota. He also has held a variety of software engineering, analysis, and design positions in industry at Silicon Biology, Racotek, and NCR. Steinbach is currently a research associate in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities. He is a co-author of the textbook,Introduction to Data Mining and has published numerous technical papers in peer-reviewed journals and conference proceedings. His research interests include data mining, statistics, and bioinformatics. He is a member of the IEEE and the ACM. Vipin Kumar is currently William Norris Professor and Head of the Computer Science and Engineering Department at the University of Minnesota. He received the B.E. degree in electronics and communication engineering from the University of Roorkee, India, in 1977, the M.E. degree in electronics engineering from Philips International Institute, Eindhoven, The Netherlands, in 1979, and the Ph.D. degree in computer science from the University of Maryland, College Park, in 1982. Kumar’s current research interests include high-performance computing and data mining. His research has resulted in the development of the concept of isoefficiency metric for evaluating the scalability of parallel algorithms, as well as highly efficient parallel algorithms and software for sparse matrix factorization (PSPASES), graph partitioning (METIS, ParMetis, hMetis), and dense hierarchical solvers. He has authored over 200 research articles, and has coedited or coauthored 9 books including the widely used text booksIntroduction to Parallel Computing andIntroduction to Data Mining, both published by Addison Wesley. Kumar has served as chair/co-chair for many conferences/workshops in the area of data mining and parallel computing, including theIEEE International Conference on Data Mining (2002) and the 15th International Parallel and Distributed Processing Symposium (2001). Currently, Kumar is the Chair of the steering committee of theSIAM International Conference on Data Mining, and a member of the steering committee of theIEEE International Conference on Data Mining. Kumar serves or has served on the editorial boards ofData Mining and Knowledge Discovery,Knowledge and Information Systems,IEEE Computational Intelligence Bulletin,Annual Review of Intelligent Informatics, Parallel Computing,Journal of Parallel and Distributed Computing,IEEE Transactions of Data and Knowledge Engineering (1993–1997),IEEE Concurrency (1997–2000), andIEEE Parallel and Distributed Technology (1995–1997). He is a Fellow of the ACM and IEEE and a member of SIAM.     

Generalizing the CHOKe flow protection

This paper proposes a simple and stateless active queue management (AQM) scheme, called geometric CHOKe (gCHOKe), to protect responsive flows from unresponsive ones. gCHOKe has its root in and is a generalization of the original CHOKe. It provides an additional power of protection, achieved by introducing an extra flow matching trial following each successful flow comparison of packets. The maximum number of comparisons permitted for an arrival can be controlled by a parameter called maxcomp. The quality of flow protection improves with maxcomp. Compared to the plain CHOKe (which is just the simplest case of gCHOKe), our analysis and simulations show that the scheme can achieve over 20% improvement in the bounds of both bandwidth and buffer space used by an aggressive flow. In addition, up to 14% of the total link capacity can be saved from the unresponsive flow, allowing responsive or rate-adaptive flows to obtain a better share of resources in the router.     

AGG and GENGED: Graph Transformation-Based Specification and Analysis Techniques for Visual Languages

The GENGED concepts and environment allow for the visual definition of visual languages (VLs) and to generate VL-specific visual environments for editing and simulation. The editing features capture either syntax-directed editing and/or free-hand editing. In the latter case, a user-defined diagram has to be analyzed in order to check the correctness of the diagram. In addition, behavioral diagrams can be simulated, i.e. the behavior of situations specified by diagrams can be validated. The specification and analysis of VLs by GENGED is based on algebraic graph transformation concepts realized by the AGG system. In this article we give a brief survey on AGG and GENGED.     

虚拟现实的三维实体的场景储存结构

虚拟现实技术是一门由应用驱动的涉及众多学科的新的实用技术,是集先进的计算机技术、传感与测量技术、仿真技术、微电子技术等为一体的综合集成技术。在论文中,作者讨论了虚拟现实技术中的三维实体的场景储存结构理论、结构构成原理与设计方法以及典型应用系统等内容。作者特别列举了一个汽车模型的三维实体的储存结构的例子。     

多Agent VSK-AF逻辑

提出了对多Agent系统的信息特征进行推理的形式化体系VSK-AF逻辑,建立了它与多Agent系统的形式化模型间的关系,给出了该逻辑的公理体系和交互公理,证明了该逻辑公理体系的一致性、无矛盾性以及完全性。讨论了进一步的研究工作。     

Multi-agent real-time pursuit

In this paper, we address the problem of multi-agent pursuit in dynamic and partially observable environments, modeled as grid worlds; and present an algorithm called Multi-Agent Real-Time Pursuit (MAPS) for multiple predators to capture a moving prey cooperatively. MAPS introduces two new coordination strategies namely Blocking Escape Directions and Using Alternative Proposals, which help the predators waylay the possible escape directions of the prey in coordination. We compared our coordination strategies with the uncoordinated one against a prey controlled by Prey A*, and observed an impressive reduction in the number of moves to catch the prey.