8693533
Characterizing and Automatically Finding Primary Effects in Planning
The use of primary effects of operators in planning is an effective approach to reduce search costs. However, the characterization of “good” primary effects has remained at an informal level. In this paper we present a formal criterion for selecting useful primary effects, which guarantees planning efficiency, completeness, and optimality. We also describe an inductive learning algorithm based on this criterion that automatically selects primary effects of operators. Both the sample complexity and the time complexity of our learning algorithm are polynomial in the size of the domain.
11767978
Real-Time Scheduling for Multi-agent Call Center Automation
In a call center, service agents with different capabilities are available for solving incoming customer problems at any time. To supply quick response and better problem solution to customers, it is necessary to schedule customer problems to appropriate service agents efficiently. We developed SANet, a service agent network for call center, which integrates multiple service agents including both software agents and human agents, and employs a broker to schedule customer problems to service agents for better solutions according to their changing capabilities and availability. This paper describes the real-time scheduling method in SANet as well as its architecture. There are two phases in our scheduling method. One is problem-type learning. The broker is trained to learn the problem types and hence can decide the type of incoming problems automatically. The other is the scheduling algorithm based on problem types, capabilities and availability of service agents. We highlight an application in which we apply SANet to a call center problem for a cable-TV company. Finally, we support our claims via experimental results and discuss related works.
15099164
A scalable supervised algorithm for dimensionality reduction on streaming data
Algorithms on streaming data have attracted increasing attention in the past decade. Among them, dimensionality reduction algorithms are greatly interesting due to the desirability of real tasks. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two of the most widely used dimensionality reduction approaches. However, PCA is not optimal for general classification problems because it is unsupervised and ignores valuable label information for classification. On the other hand, the performance of LDA is degraded when encountering limited available low-dimensional spaces and singularity problem. Recently, Maximum Margin Criterion (MMC) was proposed to overcome the shortcomings of PCA and LDA. Nevertheless, the original MMC algorithm could not satisfy the streaming data model to handle large-scale high-dimensional data set. Thus an effective, efficient and scalable approach is needed. In this paper, we propose a supervised incremental dimensionality reduction algorithm and its extension to infer adaptive low-dimensional spaces by optimizing the maximum margin criterion. Experimental results on a synthetic dataset and real datasets demonstrate the superior performance of our proposed algorithm on streaming data.
13595177
Multiple-Goal Recognition from Low-Level Signals
Researchers and practitioners from both the artificial intelligence and pervasive computing communities have been paying increasing attention to the task of inferring users' high-level goals from low-level sensor readings. A common assumption made by most approaches is that a user either has a single goal in mind, or achieves several goals sequentially. However, in real-world environments, a user often has multiple goals that are concurrently carried out, and a single action can serve as a common step towards multiple goals. In this paper, we formulate the multiple-goal recognition problem and exemplify it in an indoor environment where an RF-based wireless network is available. We propose a goal-recognition algorithm based on a dynamic model set and show how goal models evolve over time based on pre-defined states. Experiments with real data demonstrate that our method can accurately and efficiently recognize multiple interleaving goals in a user's trace.
9645876
Handling feature interactions in process-planning
Abstract The development of a successful approach to the integration of computer-aided design and computer-aided process-planning will require solutions to several fundamental problems in planning and geometric reasoning. In this article, we consider two such problems, both of which arise from interactions among machinable features. The first problem is that interactions can lead to several alternative interpretations of what the machinable features are. We address this problem by developing an algebra of features. The second problem is that interactions among features can cause problems in optimizing the process plans. For this problem, we have developed a branch-and-bound optimization algorithm with good heuristics to guide the search. This article presents the theory and mathematical framework for both problems and also the details of the implementation and experimental results.
5296157
A Novel Scalable Algorithm for Supervised Subspace Learning
Subspace learning approaches aim to discover important statistical distribution on lower dimensions for high dimensional data. Methods such as principal component analysis (PCA) do not make use of the class information, and linear discriminant analysis (LDA) could not be performed efficiently in a scalable way. In this paper, we propose a novel highly scalable supervised subspace learning algorithm called as supervised Kampong measure (SKM). It assigns data points as close as possible to their corresponding class mean, simultaneously assigns data points to be as far as possible from the other class means in the transformed lower dimensional subspace. Theoretical derivation shows that our algorithm is not limited by the number of classes or the singularity problem faced by LDA. Furthermore, our algorithm can be executed in an incremental manner in which learning is done in an online fashion as data streams are received. Experimental results on several datasets, including a very large text data set RCV1, show the outstanding performance of our proposed algorithm on classification problems as compared to PCA, LDA and a popular feature selection approach, information gain (IG).
17338631
DCD—disk caching disk: a new approach for boosting I/O performance
This paper presents a novel disk storage architecture called DCD, Disk Caching Disk, for the purpose of optimizing I/O performance. The main idea of the DCD is to use a small log disk, referred to as cache-disk, as a secondary disk cache to optimize write performance. While the cache-disk and the normal data disk have the same physical properties, the access speed of the former differs dramatically from the latter because of different data units and different ways in which data are accessed. Our objective is to exploit this speed difference by using the log disk as a cache to build a reliable and smooth disk hierarchy. A small RAM buffer is used to collect small write requests to form a log which is transferred onto the cache-disk whenever the cache-disk is idle. Because of the temporal locality that exists in office/engineering work-load environments, the DCD system shows write performance close to the same size RAM (i.e. solid-state disk) for the cost of a disk. Moreover, the cache-disk can also be implemented as a logical disk in which case a small portion of the normal data disk is used as the log disk. Trace-driven simulation experiments are carried out to evaluate the performance of the proposed disk architecture. Under the office/engineering work-load environment, the DCD shows superb disk performance for writes as compared to existing disk systems. Performance improvements of up to two orders of magnitude are observed in terms of average response time for write operations. Furthermore, DCD is very reliable and works at the device or device driver level. As a result, it can be applied directly to current file systems without the need of changing the operating system.
27730185
Parallel All-Row Preconditioned Interval Linear Solver for Nonlinear Equations on Multiprocessors
Interval Newton methods in conjunction with generalized bisection form the basis of algorithms that find all real roots within a specified X ⊂ Rn of a system of nonlinear equations F(X) = 0 with mathematical certainty, even in finite-precision arithmetic. One of the major computational cost in such methods is solving a correspondent linear interval system. This paper proposes parallel implementations of the all-row preconditioned interval linear solver on two multiprocessor architectures: shared bus multiprocessor and hypercube connected multiprocessor. Efficient parallel algorithms that are specifically tailored for these architectures are presented. The algorithms effectively use the hardware features to minimize communication overheads. Performance evaluation is carried out by means of actual measurements of the algorithms running on real parallel computers. Our numerical results show that data partitioning, data allocation and processor scheduling play important roles in the performance of the parallel computation. In both systems, significant speedup can be obtained by properly overlapping computation and communication.
40202410
On Performance of Parallel iSCSI Protocol for Networked Storage Systems
A newly emerging protocol for storage networking, iSCSI, was recently ratified by the Internet Engineering Task Force. The iSCSI protocol is perceived as a low cost alternative to the FC protocol for networked storages. It allows block level storage data to be transported over the popular TCP/IP network that is widely understood. This paper presents a new storage architecture allowing parallel processing of iSCSI packets. By leveraging inexpensive Ethernet ports, we are able to greatly improve the throughput of iSCSI storages through parallel processing. We have carried out a performance analysis to evaluate the performance of the new architecture as compared to the existing iSCSI storages. Our numerical results show that the new architecture has good performance potential
6054148
Hamiltonian cycles in circulant digraphs with two stripes
Abstract The circulant traveling salesman problem ( CTSP ) is the problem of finding a minimum weight Hamiltonian cycle in a weighted graph with circulant distance matrix. The computational complexity of this problem is not known. In fact, even the complexity of deciding Hamiltonicity of the underlying graph is unknown. This paper provides a characterization of Hamiltonian digraphs with circulant distance matrix containing only two nonzero stripes . The corresponding conditions can be checked in polynomial time. Secondly, we show that all Hamiltonian cycles of a circulant 2-digraph are periodic. Based on these two results, a method for enumerating all Hamiltonian cycles in such digraphs is described. Moreover, two simple algorithms are derived for solving the sum and bottleneck versions of CTSP for circulant distance matrices with two nonzero stripes.
5241796
Finding Biclusters by Random Projections
Given a matrix X composed of symbols, a bicluster is a submatrix of X obtained by removing some of the rows and some of the columns of X in such a way that each row of what is left reads the same string. In this paper, we are concerned with the problem of finding the bicluster with the largest area in a large matrix X. The problem is first proved to be NP-complete. We present a fast and efficient randomized algorithm that discovers the largest bicluster by random projections. A detailed probabilistic analysis of the algorithm and an asymptotic study of the statistical significance of the solutions are given. We report results of extensive simulations on synthetic data.