219267
Planning with Primary Effects: Experiments and Analysis
The use of primary effects in planning is an effective approach to reducing search. The underlying idea of this approach is to select certain "important" effects among the effects of each operator and to use an operator only for achieving its important effects. In the past, there has been little analysis of planning with primary effects and few experimental results We provide empirical and analytical results on the use of primary effects First, we experimentally demonstrate that the use of primary effects may lead to an exponential reduction of the planning time. Second, we analytically ex plain the experimental results and identify the factors that influence the efficiency of planning with primary effects. Third we describe an application of our analysis to predicting the performance of a planner for a given selection of primary effects.
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.
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.
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).
7030953
Energy-aware partitioning for multiprocessor real-time systems
In this paper, we address the problem of partitioning periodic real-time tasks in a multiprocessor platform by considering both feasibility and energy-awareness perspectives: our objective is to compute the feasible partitioning that results in minimum energy consumption on multiple identical processors by using variable voltage earliest-deadline-first scheduling. We show that the problem is NP-hard in the strong sense on m /spl ges/ 2 processors even when feasibility is guaranteed a priori. Then, we develop our framework where load balancing plays a major role in producing energy-efficient partitionings. We evaluate the feasibility and energy-efficiency performances of partitioning heuristics experimentally.
38252541
A Memory Interference Model for Regularly Patterned Multiple Stream Vector Accesses
Most existing analytical models for memory interference generally assume random bank selection for each memory access. In vector computers, however, memory accesses are typically regularly patterned with a number of data items being accessed concurrently from different banks. Very little is known about the queueing behavior of memory interferences in multiple stream vector accesses. This paper presents an analytical model for memory interferences due to vector accesses in multiple vector processor systems. The model captures the effects of both bank conflicts among elements within one vector access stream and conflicts among multiple vector access streams on system performance. The model is based on a closed queueing network assuming an ideal interconnection network. An approximation technique is proposed to solve the memory queueing system that serves customers in a complicated way (non-FIFO). We also carry out extensive simulation experiments to study memory interference and validate our analytical model. Simulation results and analytical results are in a very good agreement, indicating that the model is very accurate. We further validate our analysis by comparing the numerical results obtained from our analytical model with those measurement results that were published by other researchers. Based on our analytical model and simulations, we carry out performance evaluation of the multiple vector processor systems. Our numerical results show that memory access conflicts pose a severe limitation on the number of useful processors in the system, implying that memory system design is essential to high-performance computing. >
40082925
Analysis of Packet-Switched Multiple-Bus Multiprocessor Systems
Performance analyses of packet-switched multiple-bus multiprocessor systems are presented. Approximate queuing network models are developed for both synchronous and asynchronous control schemes, and the results are shown to be in good agreement with simulation results. The analysis of the synchronous system is based on a decomposition technique, with each of the shared resources in the system being represented as a single-server queue. For asynchronous systems, the analysis is based on the flow equivalence technique. Numerical results obtained from the analyses indicate that packet-switched multiple-bus multiprocessors with only a few buses perform almost as well as crossbar-based multiprocessors. >
26868159
A compression-boosting transform for 2D data
In this paper, we present an invertible transform for 2D data which has the objective of reordering the matrix to improve its (lossless) compression at later stages. Given a binary matrix, the transform involves first searching for the largest uniform submatrix, that is, a submatrix solely composed by the same symbol (either 0 or 1) induced by a subset of rows and columns (which are not necessarily contiguous). Then, the rows and the columns are reordered such that the uniform submatrix is moved to the left-upper corner of the matrix. The transform is recursively applied on the rest of the matrix. The recursion is stopped when the partition produces a matrix which is smaller than a predetermined threshold. The inverse transform (decompression) is fast and can be implemented in linear time in the size of the matrix. The effects of the transform on the compressibility of 2D data is studied empirically by comparing the performance of gzip and bzip2 before and after the application of the transform on several inputs. The preliminary results show that the transform boosts compression.
13076928
Coverage Algorithm and Protocol in Heterogeneous Sensor Networks
One fundamental issue in wireless sensor networks is the coverage problem. In heterogeneous sensor networks composed of different types of sensors, the difference of the sensing radius of nodes augments the computation difficulty of coverage degree. In this paper, we analyze the features of heterogeneous sensor networks and discuss the approaches to guarantee and calculate the coverage degree of the region deployed by heterogeneous sensor networks. Based on our analysis, a Distributed Coverage Optimization Algorithm by which each node in the network can determine whether it should be turn active/inactive is proposed. Simulation shows that our algorithm can make the extraneous nodes inactive and minimize the number of nodes need to remain active.