Asynchronous Forward-checking for DisCSPs

A new search algorithm for solving distributed constraint satisfaction problems (DisCSPs) is presented. Agents assign variables sequentially, but perform forward checking asynchronously. The asynchronous forward-checking algorithm (AFC) is a distributed search algorithm that keeps one consistent partial assignment at all times. Forward checking is performed by sending copies of the partial assignment to all unassigned agents concurrently. The algorithm is described in detail and its correctness proven. The sequential assignment method of AFC leads naturally to dynamic ordering of agents during search. Several ordering heuristics are presented. The three best heuristics are evaluated and shown to improve the performance of AFC with static order by a large factor. An experimental comparison of AFC to asynchronous backtracking (ABT) on randomly generated DisCSPs is also presented. AFC with ordering heuristics outperforms ABT by a large factor on the harder instances of random DisCSPs. These results hold for two measures of performance: number of non-concurrent constraints checks and number of messages sent. Research supported by the Lynn and William Frankel Center for Computer Sciences and the Paul Ivanier Center for Robotics and Production Management.     

Message delay and DisCSP search algorithms

Distributed constraint satisfaction problems (DisCSPs) are composed of agents, each holding its own variables, that are connected by constraints to variables of other agents. Due to the distributed nature of the problem, message delay can have unexpected effects on the behavior of distributed search algorithms on DisCSPs. This has been recently shown in experimental studies of asynchronous backtracking algorithms (Bejar et al., Artif. Intell., 161:117–148, 2005; Silaghi and Faltings, Artif. Intell., 161:25–54, 2005). To evaluate the impact of message delay on the run of DisCSP search algorithms, a model for distributed performance measures is presented. The model counts the number of non concurrent constraints checks, to arrive at a solution, as a non concurrent measure of distributed computation. A simpler version measures distributed computation cost by the non-concurrent number of steps of computation. An algorithm for computing these distributed measures of computational effort is described. The realization of the model for measuring performance of distributed search algorithms is a simulator which includes the cost of message delays. Two families of distributed search algorithms on DisCSPs are investigated. Algorithms that run a single search process, and multiple search processes algorithms. The two families of algorithms are described and associated with existing algorithms. The performance of three representative algorithms of these two families is measured on randomly generated instances of DisCSPs with delayed messages. The delay of messages is found to have a strong negative effect on single search process algorithms, whether synchronous or asynchronous. Multi search process algorithms, on the other hand, are affected very lightly by message delay.     

A note on the Gustafson-Kessel and adaptive fuzzy clusteringalgorithms

In this letter, we show that the Gustafson-Kessel (G-K) algorithm (1979) and the original adaptive fuzzy clustering (AFC) algorithm can be thought of as special cases of a more general algorithm. Our analysis shows that the G-K algorithm is better suited for ellipsoidal clusters of equal volume, whereas the original AFC algorithm is better suited for linear clusters. We also discuss a new variation of these algorithms, which can be used to improve the results of the G-K and AFC algorithms in some cases     

Local search with edge weighting and configuration checking heuristics for minimum vertex cover

The Minimum Vertex Cover (MVC) problem is a well-known combinatorial optimization problem of great importance in theory and applications. In recent years, local search has been shown to be an effective and promising approach to solve hard problems, such as MVC. In this paper, we introduce two new local search algorithms for MVC, called EWLS (Edge Weighting Local Search) and EWCC (Edge Weighting Configuration Checking). The first algorithm EWLS is an iterated local search algorithm that works with a partial vertex cover, and utilizes an edge weighting scheme which updates edge weights when getting stuck in local optima. Nevertheless, EWLS has an instance-dependent parameter. Further, we propose a strategy called Configuration Checking for handling the cycling problem in local search. This is used in designing a more efficient algorithm that has no instance-dependent parameters, which is referred to as EWCC. Unlike previous vertex-based heuristics, the configuration checking strategy considers the induced subgraph configurations when selecting a vertex to add into the current candidate solution.A detailed experimental study is carried out using the well-known DIMACS and BHOSLIB benchmarks. The experimental results conclude that EWLS and EWCC are largely competitive on DIMACS benchmarks, where they outperform other current best heuristic algorithms on most hard instances, and dominate on the hard random BHOSLIB benchmarks. Moreover, EWCC makes a significant improvement over EWLS, while both EWLS and EWCC set a new record on a twenty-year challenge instance. Further, EWCC performs quite well even on structured instances in comparison to the best exact algorithm we know. We also study the run-time behavior of EWLS and EWCC which shows interesting properties of both algorithms.     

Distributed constraint satisfaction with partially known constraints

Distributed constraint satisfaction problems (DisCSPs) are composed of agents connected by constraints. The standard model for DisCSP search algorithms uses messages containing assignments of agents. It assumes that constraints are checked by one of the two agents involved in a binary constraint, hence the constraint is fully known to both agents. This paper presents a new DisCSP model in which constraints are kept private and are only partially known to agents. In addition, value assignments can also be kept private to agents and not be circulated in messages. Two versions of a new asynchronous backtracking algorithm that work with partially known constraints (PKC) are presented. One is a two-phase asynchronous backtracking algorithm and the other uses only a single phase. Another new algorithm preserves the privacy of assignments by performing distributed forward-checking (DisFC). We propose to use entropy as quantitative measure for privacy. An extensive experimental evaluation demonstrates a trade-off between preserving privacy and the efficiency of search, among the different algorithms. Partially supported by the Spanish project TIN2006-15387-C03-01. Partially supported by the Lynn and William Frankel center for Computer Sciences and the Paul Ivanier Center for Robotics and Production Management.     

Constraint satisfaction algorithms1

Constraint satisfaction problems are ubiquitous in artificial intelligence and many algorithms have been developed for their solution. This paper provides a unified survey of some of these, in terms of three classes: (i) tree search, (ii) arc consistency (AC), and (iii) hybrid tree search/arc consistency algorithms. It is shown that several important algorithms, when slightly rearranged, are of the latter hybrid form, but with arc consistency components that do not necessarily achieve full arc consistency at the tree nodes. Accordingly, we define several new partial AC procedures, AC1/5, AC1/4, AC1/3, and AC½, analogous to the well-known full AC algorithms which Mackworth has called AC1, AC2, and AC3. The fractional suffixes on our AC algorithms are roughly proportional to the degree of partial arc consistency they achieve. Unlike traditional versions, our AC algorithms (full and partial) are presented in a parameterized form to allow them to be embedded efficiently at the nodes of a tree search process. Algorithm complexities are compared empirically, using the n-queens problem and a new version called confused n-queens. Gaschnig's Backmarking (a tree search algorithm) and Haralick's Forward Checking (a hybrid algorithm) are found to be the most efficient. For the hybrid algorithms, we find that it pays to do little arc consistency processing at the nodes, incurring more nodes, but sufficiently reducing the work per node so as to obtain less work over the whole tree. The unified view taken here suggests several new algorithms. Preliminary results show one of these to be the best algorithm so far.     

Actor-critic algorithms for hierarchical Markov decision processes

We consider the problem of control of hierarchical Markov decision processes and develop a simulation based two-timescale actor-critic algorithm in a general framework. We also develop certain approximation algorithms that require less computation and satisfy a performance bound. One of the approximation algorithms is a three-timescale actor-critic algorithm while the other is a two-timescale algorithm, however, which operates in two separate stages. All our algorithms recursively update randomized policies using the simultaneous perturbation stochastic approximation (SPSA) methodology. We briefly present the convergence analysis of our algorithms. We then present numerical experiments on a problem of production planning in semiconductor fabs on which we compare the performance of all algorithms together with policy iteration. Algorithms based on certain Hadamard matrix based deterministic perturbations are found to show the best results.     

Privacy Loss in Distributed Constraint Reasoning: A Quantitative Framework for Analysis and its Applications

It is critical that agents deployed in real-world settings, such as businesses, offices, universities and research laboratories, protect their individual users’ privacy when interacting with other entities. Indeed, privacy is recognized as a key motivating factor in the design of several multiagent algorithms, such as in distributed constraint reasoning (including both algorithms for distributed constraint optimization (DCOP) and distributed constraint satisfaction (DisCSPs)), and researchers have begun to propose metrics for analysis of privacy loss in such multiagent algorithms. Unfortunately, a general quantitative framework to compare these existing metrics for privacy loss or to identify dimensions along which to construct new metrics is currently lacking. This paper presents three key contributions to address this shortcoming. First, the paper presents VPS (Valuations of Possible States), a general quantitative framework to express, analyze and compare existing metrics of privacy loss. Based on a state-space model, VPS is shown to capture various existing measures of privacy created for specific domains of DisCSPs. The utility of VPS is further illustrated through analysis of privacy loss in DCOP algorithms, when such algorithms are used by personal assistant agents to schedule meetings among users. In addition, VPS helps identify dimensions along which to classify and construct new privacy metrics and it also supports their quantitative comparison. Second, the article presents key inference rules that may be used in analysis of privacy loss in DCOP algorithms under different assumptions. Third, detailed experiments based on the VPS-driven analysis lead to the following key results: (i) decentralization by itself does not provide superior protection of privacy in DisCSP/DCOP algorithms when compared with centralization; instead, privacy protection also requires the presence of uncertainty about agents’ knowledge of the constraint graph. (ii) one needs to carefully examine the metrics chosen to measure privacy loss; the qualitative properties of privacy loss and hence the conclusions that can be drawn about an algorithm can vary widely based on the metric chosen. This paper should thus serve as a call to arms for further privacy research, particularly within the DisCSP/DCOP arena.     

A Generic Framework for Constrained Optimization Using Genetic Algorithms

In this paper, we propose a generic, two-phase framework for solving constrained optimization problems using genetic algorithms. In the first phase of the algorithm, the objective function is completely disregarded and the constrained optimization problem is treated as a constraint satisfaction problem. The genetic search is directed toward minimizing the constraint violation of the solutions and eventually finding a feasible solution. A linear rank-based approach is used to assign fitness values to the individuals. The solution with the least constraint violation is archived as the elite solution in the population. In the second phase, the simultaneous optimization of the objective function and the satisfaction of the constraints are treated as a biobjective optimization problem. We elaborate on how the constrained optimization problem requires a balance of exploration and exploitation under different problem scenarios and come to the conclusion that a nondominated ranking between the individuals will help the algorithm explore further, while the elitist scheme will facilitate in exploitation. We analyze the proposed algorithm under different problem scenarios using Test Case Generator-2 and demonstrate the proposed algorithm's capability to perform well independent of various problem characteristics. In addition, the proposed algorithm performs competitively with the state-of-the-art constraint optimization algorithms on 11 test cases which were widely studied benchmark functions in literature.     

Saving Space by Fully Exploiting Invisible Transitions

Checking that a given finite state program satisfies a linear temporal logic property suffers from the state explosion problem. Often the resulting lack of available memory is more significant than any time limitations. One way to cope with this is to reduce the state graph used for model checking. We present an algorithm for constructing a state graph that is a projection of the program's state graph. The algorithm maintains the transitions and states that affect the truth of the property to be checked. Especially in conjunction with known partial order reduction algorithms, we show a substantial reduction in memory over using partial order methods alone, both in the precomputation stage, and in the result presented to a model checker. The price of the space reduction is a single additional traversal of the graph obtained with partial order reduction. As part of our space-saving methods, we present a new way to exploit Holzmann's Bit Hash Table, which assists us in solving the revisiting problem.     

基于粒子群优化的有约束模型预测控制器

研究了模型预测控制(MPC)中解决带约束的优化问题时所用到的优化算法,针对传统的二次规划(QP)方法的不足,引入了一种带有混沌初始化的粒子群优化算法(CPSO),将其应用到模型预测控制中,用十解决同时带有输入约束和状态约束的控制问题.最后,引入了一个实际的带有约束的线性离散系统的优化控制问题,分别用二次规划和粒子群优化两种算法去解决,通过仿真结果的比较,说明了基于粒子群优化(PSO)的模型预测控制算法的优越性.     

Distributed Model Checking: From Abstract Algorithms to Concrete Implementations

Distributed Model Checking (DMC) is based on several distributed algorithms, which are often complex and error prone. In this paper, we consider one fundamental aspect of DMC design: message passing communication, the implementation of which presents hidden tradeoffs often dismissed in DMC related literature. We show that, due to such communication models, high level abstract DMC algorithms might face implicit pitfalls when implemented concretely. We illustrate our discussion with a generic distributed state space generation algorithm.     

DCSP和DCOP求解研究进展

分布式约束满足问题（DCSP）和分布式约束最优问题（DCOP）的研究是分布式人工智能领域的基础性工作。本文首先介绍了卿和DCOP的形式化描述及对实际应用问题的建模方法。在DCSP和DCOP的求解中,通常对问题要进行限制和要求,同时要满足分布性、异步性、局部性、完备性的原则。异步回溯（ABT）、异步弱承诺搜索（AWC）和分布式逃逸（DB）算法是求解DCSP的有代表性的算法;DCSP算法对DCOP求解产生了影响,但由DCSP一般化到DCOP的算法,仅适用于解决部分特定的问题,DCOP的最优、异步算法有异步分布式约束最优算法（A—dopt）和最优异步部分交叉算法（OptAPO）。本文讨论了上述算法的性能。相关的研究工作在多局部变量的处理、超约束DCSP、算法性能度量、通信的保密等方面进行了扩充,在对问题本身的研究、建模方法学、算法、与其他方法的结合以及拓展应用领域等方面仍有许多问题需要进一步研究。     

高度机动目标的异步多传感器偏差估计算法*

异步多传感器偏差估计问题是数据融合系统中的常见问题.在已有算法中,由于忽略了伪量测方程中的加速度项,在高度机动目标条件下的偏差估计可能是有偏的,为此提出了一种针对高度机动目标的异步多传感器偏差估计算法.该算法在构造伪量测差分矢量时通过反求最适的伪量测同步时刻,可以建立无加速度项的伪量测方程,并通过Kalman滤波对系统...     

Preemptive scheduling on uniformly related machines: minimizing the sum of the largest pair of job completion times

We revisit the classic problem of preemptive scheduling on m uniformly related machines. In this problem, jobs can be arbitrarily split into parts, under the constraint that every job is processed completely, and that the parts of a job are not assigned to run in parallel on different machines. We study a new objective which is motivated by fairness, where the goal is to minimize the sum of the two maximal job completion times. We design a polynomial time algorithm for computing an optimal solution. The algorithm can act on any set of machine speeds and any set of input jobs. The algorithm has several cases, many of which are very different from algorithms for makespan minimization (algorithms that minimize the maximum completion time of any job), and from algorithms that minimize the total completion time of all jobs.     

Relay Reachability Algorithm for Exploring Huge State Space

Since many desirable properties about finite-state model are expressed as a reachability problem, reachability algorithms have been extensively studied in model checking. On the other hand, reachability algorithms play an important role in game solving since reachability games are often described as a finite state model. In this sense, reachability algorithms are located in the intersection of the research areas of Model Checking and Artificial Intelligence.This paper interests in solving the reachability games called Push-Push. However, both exact and approximate reachability algorithms are not sufficient to the games since its state space is huge and requires lots of iterations such as 338 steps in the reachability computation. Thus we devise the new algorithm called relay reachability algorithm. It divides the global state space into several local ones. And exact reachability algorithm is applied on each local state space one by one. With these reachability algorithms, we solve all of the games.     

面向涉密检查系统的基于KMP思想的多模式匹配算法

模式匹配算法是涉密检查系统搜索引擎中的主要算法。在分析比较常用模式匹配算法基础上,提出了一种基于KMP算法跳跃思想的多模式匹配算法。该算法可兼容多模式匹配情况和单模式匹配情况,引入多维数组存储模式集并对模式集进行简单排序处理以简化后续操作,引入棋盘表记录各模式串的最大跳跃距离及模式串间跳跃距离。实验结果表明,该算法易于实现,并能有效提高匹配速度,对海量数据检索,有较好的时间和空间性能。     

GALS Test Chip on 130nm Process

We present a Globally Asynchronous Locally Synchronous test chip fabricated on a 130nm silicon process. The primary design goals of this chip were to measure the stability of local clocks on a deep submicron process technology, evaluate difficulties using GALS in a standard design flow, and to measure power consumption. The original Asynchronous Wrapper building blocks were used to construct a configurable data pipeline that can be tuned to emulate the operation of many different types of algorithms.     

A new adaptive filtering subband algorithm for two-channel acoustic noise reduction and speech enhancement

This paper addresses the problem of acoustic noise reduction and speech enhancement by adaptive filtering algorithms. Most speech enhancement methods and algorithms which use adaptive filtering structure are generally expressed in fullband form. One of these widespread structures is the Forward Blind Source Separation Structure (FBSS). This FBSS structure is often used to separate speech form noise and therefore enhance the speech signal at the processing output. In this paper, we propose a new subband implementation of this FBSS structure. In order to give more robustness to the proposed structure, we adapt then we apply to this subband structure a new combination of criteria based on the system mismatch and the smoothing filtering errors minimizations. The combination between this proposed subband structure with this optimal criteria allows to obtain a new two-channel subband forward (2CSF) algorithm that improves the convergence speed of the cross adaptive filters which are used to separate speech from noise. Objective tests under various environments are presented showing the good behavior of the proposed 2CSF algorithm.     

Step length in forward differencing for curved surface display

Principles and basic algorithms for evaluating polynomial functions by Forward Differencing (FD) and Adaptive Forward Differencing (AFD) are presented. Both techniques are used nowadays in scanning/display algorithms for parameteric polynomial surfaces by generating and displaying a set of points. Constraints to be put on such a set of points are made explicit. One FD and two AFD surface-scanning/display algorithms presented in literature are evaluated in terms of these constraints. The FD algorithm is proved to be valid, but the AFD algorithms are shown to be incorrect. A valid AFD algorithm is presented.This research was partially done at the Faculty of Technical Mathematics and Informations of the Delft University of Technology, Delft, The Netherlands