7135727
Control Knowledge in Planning: Benefits and Tradeoffs
Recent new planning paradigms, such as Graphplan and Satplan, have been shown to outperform more traditional domain-independent planners. An interesting aspect of these planners is that they do not incorporate domain specific control knowledge, but instead rely on efficient graph-based or propositional representations and advanced search techniques. An alternative approach has been proposed in the TLPlan system. TLPlan is an example of a powerful planner incorporating declarative control specified in temporal logic formulas. We show how these control rules can be parsed into Satplan. Our empirical results show up to an order of magnitude speed up. We also provide a detailed comparison with TLPlan, and show how the search strategies in TLPlan lead to efficient plans in terms of the number of actions but with little or no parallelism. The Satplan and Graphplan formalisms on the other hand do find highly parallel plans, but are less effective in sequential domains. Our results enhance our understanding of the various tradeoffs in planning technology, and extend earlier work on control knowledge in the Satplan framework by Ernst et al. (1997) and Kautz and Selman (1998).
15221020
Learning Declarative Control Rules for Constraint-BAsed Planning
Despite the long history of research in using machine learning to speed-up state-space planning, the techniques that have been developed are not yet in widespread use in practical planning systems. One limiting factor is that traditional domain-independent planning systems scale so poorly that extensive learned control knowledge is required to raise their performance to an acceptable level. Therefore, work in this area has focused on learning large numbers of control rules that are specific to the details of the underlying planning algorithms, which can be extremely costly. In recent years, a new generation of planning systems with much improved speed and scalability has become available. These systems formulate planning as solving a large constraint satisfaction problem. This formulation opens up the possibility that domain-specific control knowledge can be added to the planner in a purely declarative manner via a set of additional constraints. In this paper we present the first positive results on automatically acquiring such high-level, declarative constraints using machine learning techniques. In particular, we will show that a new heuristic method for generating training examples together with a rule induction algorithm can learn useful control rules in a variety of domains. Only a small number of rules are needed to reduce solution times by two orders of magnitude or more on larger problems, training times are short, and the learned rules can be exported to other planning systems.
15471957
A Generic Electronic Payment Model Supporting Multiple Merchant Transactions
This paper presents a generic electronic payment model which is intended for a multi-merchant transaction, in which more than one merchant (the retailer, multiple resellers and content providers) are involved in the distribution of electronic contents. With our payment model, every involved merchant will be notified to verify the license information of a content on the settlement day before the customer actually pays for his purchase. Thus, piracy can be discouraged, intellectual property rights of electronic contents can be protected. Each of the involved merchants can obtain his own deserved share of the customer's payment in accordance with their union agreement on this content. As examples to demonstrate the applications of our generic payment model, two well-known payment systems are enhanced herein, based on our payment model.
21184559
Region-based image retrieval
This paper presents a region-based method which uses multiple regions as the key to retrieve images. The proposed method represents the semantics or concepts embedded in the input images with three ingredients. One is a set of regions with weighted importance; the second is the corresponding feature distributions of the regions and the last is the spatial relationships between these regions. The importance of each segmented region in the input example images can be automatically and efficiently determined through a formulated linear system. In addition, a novel method for matching the spatial relationship between regions is also presented to capture the structural semantics of the content of images. By combining the feature distributions and the spatial relationships of regions with appropriate weights, the experimental results show that the retrieval results are much more accurate than other methods which utilize low-level features, such as color, texture, shape, and so on.
12850870
Supporting Mobile Multimedia Services with Intermittently Available Grid Resources
Advances in high quality digital wireless networks and differentiated services have enabled the development of mobile multimedia applications that can execute in global infrastructures. In this paper, we introduce a novel approach to supporting mobile multimedia services by effectively exploiting the intermittently available idle computing, storage and communication resources in a Grid infrastructure. Specifically, we develop efficient resource discovery policies that can ensure continuous access to information sources and maintain application Quality-of-Service (QoS) requirements, e.g. required network transmission bandwidth on the mobile clients. Our performance studies indicate that mobility patterns obtained via tracking or user-supplied itineraries assist in optimizing resource allocation. The proposed policies are also resilient to dynamic changes in the availability of grid resources.
10970909
MAPGrid: A New Architecture for Empowering Mobile Data Placement in Grid Environments
The rising popularity of mobile applications and devices has brought about an enhanced interest in infrastructure support for mobile computing. Our work focuses on the development of a mobile grid infrastructure called MAPGrid (mobile applications on Grids), where grid resources are exploited as proxies to enable advanced mobile applications. However, intermittent availability of grid resources presents challenges for data-intensive mobile applications. In this paper, we propose novel methodologies for placing mobile data on grid proxies. We introduce a notion of two-tier architecture for MAPGrid, where the upper tier captures grid related features and the lower tier represents features associated with mobile environments. We further develop an intelligent mobile data placement mechanism that effectively balances tradeoffs between replication cost and data access cost by leveraging knowledge of grid availability and mobile data request patterns. Through extensive experimentation, we illustrate the superiority of the proposed techniques over several popular data placement strategies.
18171732
Data Placement in Intermittently Available Environments
In this paper, we address the problem of data placement in a grid based multimedia environment, where the resource providers, i.e. servers, are intermittently available. The goal is to optimize the system performance by admitting maximum number of users into the system while ensuring user Quality of Service (QoS). We define and formulate various placement strategies that determine the degree of replication necessary for video objects by using a cost-based optimization procedure based on predictions of expected requests under various time-map scenarios and QoS demands. We also devise methods for dereplication of videos based on changes in popularity and server usage patterns. Our performance results indicate the benefits obtained the judicious use of dynamic placement strategies.