1087607
Automatically Selecting and Using Primary Effects in Planning: Theory and Experiments
Abstract The use of primary effects of operators is an effective approach to improving the efficiency of planning. The characterization of “good” primary effects, however, has remained at an informal level and there have been no algorithms for selecting primary effects of operators. We formalize the use of primary effects in planning and present a criterion for selecting useful primary effects, which guarantees efficiency and completeness. We analyze the efficiency of planning with primary effects and the quality of the resulting plans. We then describe a learning algorithm that automatically selects primary effects and demonstrate, both analytically and empirically, that the use of this algorithm significantly reduces planning time and does not compromise completeness.
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.
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).
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.
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.
31114007
Cooperative Target Localization Method for Heterogeneous Sensor Networks
Based on the binary sensor model, a novel method for target localization in heterogeneous sensor networks is presented. With the binary information reported by nodes, target's position is locked into the intersection area of sensing areas of all nodes which detect the same target, and then the estimated position is computed by geometric means. The proposed method adapts to heterogeneous sensor networks, moreover, it can integrate with other target localization approaches easily. Simulation results demonstrate that, in sensor networks composed of the same type of sensors, our method lead to a decrease in average localization errors compared with the traditional method; in heterogeneous sensor networks, the method renders more accurate estimate of the target's location.
854297
Performance of Cache Memories for Vector Computers
Abstract Recent studies [5] have shown that the memory system is the major bottleneck of vector computers. Performance measurements on CRAY-2 computers indicate that as much as a factor of 4.4 performance degradation results from memory latency. In this paper, we study the performance of incorporating cache memories into vector computers. Two cache organizations that we proposed [22] are considered: direct-mapped cache and prime-mapped cache. We analyze the caching behavior of three typical blocked algorithms for numerical applications: matrix multiplication, Gaussian elimination, and FFT. By analyzing the algorithm structures in conjunction with system architectures, we develop analytical models based on real applications rather than on statistical estimates. Our performance models give the expected value of execution time of an algorithm averaged over a wide range of problem sizes. Performance measurements of the algorithms on a real machine are carried out to validate our analysis. Numerical results show that the prime-mapped cache minimizes the cache miss ratio caused by line interferences that are critical for numerical applications.
8767636
The Design and Implementation of a DCD Device Driver for Unix
Recent research results [1, 2] using simulation have demonstrated that Disk Caching Disk (DCD), a new disk I/O architecture, has the potential for drastically improving disk write performance besides its higher reliability than traditional disk systems. To validate whether DCD can live up to its promise in the real world environment, we have designed and implemented a DCD device driver for the Sun's Solaris operating system. Measured performance results are very promising. For metadata intensive benchmarks, our DCD driver outperforms the traditional system by a factor of 2-6 in terms of program execution speeds. The driver also guarantees file system integrity in the events of system crashes or failures. Moreover, unlike other approaches such as Log-structured File Systems or Soft Updates, the DCD driver is completely transparent to the OS. It does not require any changes to the OS or the on-disk data layout. As a result, it can be used as a "drop-in" replacement for the traditional disk device driver in an existing system to obtain immediate performance improvement. Our multi-layered device-driver approach significantly reduces the implementation overhead and improves portability.
11280884
A novel cache design for vector processing
This paper introduces an innovative cache design for vector computers, called prime-mapped cache. By utilizing the special properties of a Mersenne prime, the new design does not increase the critical path length of a processor, nor does it increase the cache access time as compared to a direct-mapped cache. The prime-mapped cache minimizes cache miss ratio caused by line interferences that have been shown to be critical for numerical applications by previous investigators. We show that significant performance gains are possible by adding the proposed cache memory into an existing vector computer provided that application programs can be blocked. The performance gain will increase with the increase of the speed gap between processors and memories. We develop an analytical performance model based on a generic vector computation model to study the performance of the design. Our preliminary performance analysis on various vector access patterns shows that the prime-mapped cache can provide as much as a factor of 2 to 3 performance improvement over the conventional direct-mapped cache in the vector processing environment. Moreover, the additional hardware cost introduced by the new mapping scheme is negligible.
6583750
Building Trust in Online Rating Systems Through Signal Modeling
Online feedback-based rating systems are gaining popularity. Dealing with unfair ratings in such systems has been recognized as an important but difficult problem. This problem is challenging especially when the number of regular ratings is relatively small and the unfair ratings contribute to a significant portion of the overall ratings. In this paper, we propose a novel algorithm to detect the unfair ratings that cannot be effectively prevented by existing state-of-the-art techniques. Our algorithm is particularly effective to detect malicious raters that collaboratively manipulate ratings of one or several products. The main idea of our algorithm is to use an autoregressive signal modeling technique combined with trust-enhanced rating aggregation. We are able to detect and filter out unfair ratings very accurately. Extensive experiments through simulations and real-world data have been performed to validate the proposed algorithm. The experimental results show significant improvements on detecting collaborative unfair raters over existing techniques.