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.
7881691
Dynamic Refinement of Feature Weights Using Quantitative Introspective Learning
Recently more and more researchers have been supporting the view that learning is a goaldriven process. One of the key properties of a goal-driven learner is introspectiveness-the ability to notice the gaps in its knowledge and to reason about the information required to fill in those gaps. In this paper, we introduce a quantitative introspective learning paradigm into case-based reasoning (CBR). The result is an integrated problem-solving model which will learn introspectively feature weights in a case base in order to be responsive dynamically to its users. In contrast to the existing qualitative methods for introspective learning, our model has the advantage of being able to capture accurate learning information in the interactions with its users. A CBR system equipped with quantitative introspective learning ability can allow the feature weights to be captured automatically and to track its users' changing preferences continuously. In such a system, while the reasoning part is still case-based, the learning part is shouldered by a quantitative introspective learning model. Weight learning and evolution are accomplished in the background. The effectiveness of this integration will be demonstrated through a series of empirical experiments.
16719759
Exploring in the weblog space by detecting informative and affective articles
Weblogs have become a prevalent source of information for people to express themselves. In general, there are two genres of contents in weblogs. The first kind is about the webloggers' personal feelings, thoughts or emotions. We call this kind of weblogs affective articles. The second kind of weblogs is about technologies and different kinds of informative news. In this paper, we present a machine learning method for classifying informative and affective articles among weblogs. We consider this problem as a binary classification problem. By using machine learning approaches, we achieve about 92% on information retrieval performance measures including precision, recall and F1. We set up three studies on the applications of above classification approach in both research and industrial fields. The above classification approach is used to improve the performance of classification of emotions from weblog articles. We also develop an intent-driven weblog-search engine based on the classification techniques to improve the satisfaction of Web users. Finally, our approach is applied to search for weblogs with a great deal of informative articles.
9034586
An Agent for Intelligent Situation Assessment
Coordinating Search and Rescue (SAR) operations is a knowledge and information intensive task. Upon receiving an initial indication about a possible aircraft related problem, a Rescue Coordination Center (RCC) Controller sets out to find out more about the nature of the problem. This situation assessment phase is highly complex due to the diverse and sophisticated nature of the many information sources. In this paper, we present an intelligent agent architecture incorporating multiple, continual information planning agents for assisting the RCC controller in performing critical situation assessment tasks. The agents monitor the actions of the human controller and make their decisions on when and how to acquire more information to help the human controller, and to remind him of important steps that may have been overlooked. The system is designed based on several technologies, including hierarchical task networks, case based retrieval and intelligent agent systems.
14958621
Accurate and Low-cost Location Estimation Using Kernels
We present a novel method for indoor-location estimation using a vector-space model based on signals received from a wireless client. Our aim is to obtain an accurate mapping between the signal space and the physical space without incurring too much human calibration effort. This problem has traditionally been tackled through probabilistic models trained on manually labeled data, which are expensive to obtain. In this paper, we present a novel approach to building a mapping between the signalvector space and the physical location space using kernel canonical correlation analysis (KCCA). Its training requires much less human labor. Moreover, unlike traditional location-estimation systems that treat grid points as independent and discrete target classes during training, we use the physical location as a continuous feedback to build a similarity mapping using KCCA. We test our algorithm in a 802.11 wireless LAN environment, and demonstrate the advantage of our method in both accuracy and its ability to utilize a much smaller set of labeled training data than previous methods.
1868308
Plan Mining by Divide-and-Conquer
Plans or sequences of actions are an important form of data With the proliferation of database technology plan databases or planbases are increasingly common E cient discovery of important patterns of actions in plan databases presents a challenge to data mining In this paper we present a method for mining signi cant patterns of successful actions in a large planbase using a divide and conquer strategy The method exploits multi dimensional generalization of sequences of actions and extracts the inherent hierarchical structure and sequential patterns of plans at di erent levels of abstraction These patterns are used in turn to subsequently narrow down the search for more speci c patterns The process is analogous to the use of divide and conquer methods in hierarchical planning We illustrate our approach using a travel planning database
31570297
Performance of Multiple-Bus Interconnections for Multiprocessors
Abstract Bus structures, in general, are easily understood and therefore preferred by manufactures for implementation. Multiple-bus systems can be viewed as an incremental expansion of single-bus architectures that can provide high bandwidth and reliability. This research note provides a brief overview of various analytical techniques suitable for performance evaluation of the multiple-bus multiprocessors. Some results and comparisons based on these techniques are also provided.
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.
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.
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.
8600912
Performance Evaluation of Distributed Web Server Architectures under E-Commerce Workloads
One of the central and key components to make the ECommerce a success is high performance and highly reliable web server architectures. Extensive research has been sparked aiming at improving web server performance. Fundamental to the goal of improving web server performance is a solid understanding of behavior and performance of web servers. However, very little research is found on evaluating web server performance based on realistic workload representing E-Commerce applications which are usually exemplified by a large amount of CGI (Common Gateway Interface), ASP (Active Server Page), or Servlet (Java Server-side interface) calls. This paper presents a performance study under the workload with a mixture of static web page requests, CGI requests, Servlet requests, and database queries. System throughputs and user response times are measured for five different server architectures consisting of PCs that run both a web server program and a database. We observed that performance behaviors of the web server architectures considered under this mixed workload are quite different from that under static page workload and sometimes counter-intuitive. Our performance results suggest that there is a large room for potential performance improvement for web servers.