3653022
SERGEANT: A framework for building more flexible web agents by exploiting a search engine
With the rapid growth of the World Wide Web, there is growing interest in developing web agents that interact with online services to acquire information. However, finding the online services perfectly suited for a given task is not always feasible. First, the agents might not be given sufficient information to fill in the required input fields for querying an online service. Second, the online service might generate only partial information. Third, the agents might need to know the information about B by some input set A, but they can only find the online services that generate A from B. Fourth, most of the online services do not tolerate errors in the inputs, thus even a minor typo in the input field can hinder them from generating any meaningful results. This paper proposes SERGEANT, a framework for building flexible web agents that handle these imperfect situations. In this framework we exploit an information retrieval (IR) system as a general discovery tool to assist finding and pruning information. To demonstrate SERGEANT, we implemented two web agents: the Internet inverse geocoder and the address lookup module. Our experiments show that these agents are capable of generating high-quality results under imperfect situations.
746230
Designing the Market Game for a Trading Agent Competition
The authors discuss the design and operation of a trading agent competition, focusing on the game structure and some of the key technical issues in running and playing the game. They also describe the competition's genesis, its technical infrastructure, and its organization. The article by A. Greenwald and P. Stone (2001), describes the competition from a participant's perspective and describes the strategies of some of the top-placing agents. A visualization of the competition and a description of the preliminary and final rounds of the TAC are available in IC Online (http://computer.org/internet/tac.htm).
15193012
Unsupervised link discovery in multi-relational data via rarity analysis
A significant portion of knowledge discovery and data mining research focuses on finding patterns of interest in data. Once a pattern is found, it can be used to recognize satisfying instances. The new area of link discovery requires a complementary approach, since patterns of interest might not yet be known or might have too few examples to be learnable. We present an unsupervised link discovery method aimed at discovering unusual, interestingly linked entities in multi-relational datasets. Various notions of rarity are introduced to measure the "interestingness" of sets of paths and entities. These measurements have been implemented and applied to a real-world bibliographic dataset where they give very promising results.
3645474
Exploiting a search engine to develop more flexible Web agents
With the rapid growth of the World Wide Web, more and more people rely on the online services to acquire and integrate information. However, it is time consuming to find the online services that are perfectly suited for a given task. First, the users might not have enough information to fill in the required input fields for querying an online service. Second, the online service might generate only partial information. Third, the user might only find the inverse version of the desired service. We propose a framework to develop flexible Web agents that handle these imperfect situations. In this framework we exploit a search engine as a general information discovery tool to assist finding and pruning information. To demonstrate this framework, we implemented two Web agents: the Internet inverse geocoder and the address lookup module.
45339234
Interesting Instance Discovery in Multi-Relational Data
The general area of machine discovery focuses on methods to use computers to perform or assist discovery tasks. Herbert Simon described it as “gradual problemsolving processes of searching large problem spaces for incompletely defined goal objects” [Simon, 1995, p.171]. Today machine discovery research falls into two major categories, scientific discovery and knowledge discovery and data mining (KDD). In this paper we propose a new research direction that lies somewhere in-between these two trends: we call it interesting instance discovery (IID) which aims at discovering interesting instances in large, multi-relational datasets. There are three important characteristics for IID research: (1) Unlike scientific discovery and KDD, it aims at the discovery of particular interesting instances as opposed to general laws or patterns. (2) It is dealing with multi-relational data instead of numeric data that is best described as a relational graph or a semantic net. In such a network nodes represent objects and links represent relationships between them – see Figure 1 for an example of such a network from a bibliography domain. (3) Similar to KDD, it also focuses on data that are too large and complex to be analyzed manually by humans.
10564243
Discovering the linear writing order of a two-dimensional ancient hieroglyphic script
This paper demonstrates how machine learning methods can be applied to deal with a real-world decipherment problem where very little background knowledge is available. The goal is to discover the linear order of a two-dimensional ancient script, Hieroglyphic Luwian. This paper records a complete decipherment process including encoding, modeling, parameter learning, optimization, and evaluation. The experiment shows that the proposed approach is general enough to recover the linear order of various manually generated two-dimensional scripts without needing to know in advance what language they represent and how the two-dimensional scripts were generated. Since the proposed method does not require domain specific knowledge, it can be applied not only to language problems but also order discovery tasks in other domains such as biology and chemistry.
1266771
Applying LVQ techniques to compress historical information in sensor networks
Summary form only given. In the emerging area of wireless sensor networks, a typical challenge is to retrieve historical information from the sensor nodes. We propose a new technique, called adaptive learning vector quantization (ALVQ), to compress this historical information. Our technique is based on the following two observations: (1) in sensor networks, the historical information exhibits similar patterns over time; and (2) different measurements are intrinsically correlated. Our algorithm works as follows: first, the codebook is obtained through a LVQ (learning vector quantization), which adjusts the codebook to be nearer to the optimal codebook. Second, ALVQ compresses the codebook update data pieces and transfers the compressed information to the base station. Using 2-level piece-wise regression, ALVQ can compress the updates with high precision while saving more bandwidth for data transmission in order to increase the quality of the approximation. In our experiments we used weather data to compare the performance of the ALVQ algorithm with the recently proposed SBR (self based regression) technique. Our experimental results demonstrate that the LVQ learning process significantly improves the quality of the codebook, thus increasing the regression precision. In addition the use of two-level regression for transmitting the codebook updates further minimizes the required bandwidth. Overall the ALVQ technique can achieve the same precision with SBR while using 75% of the bandwidth.
16095846
Correlation of Entropy with Similarity and Symmetry
Informational entropy is quantitatively related to similarity and symmetry. Some tacit assumptions regarding their correlation have been shown to be wrong. The Gibbs paradox statement (indistinguishability corresponds to minimum entropy, which is zero) has been rejected. All their correlations are based on the relation that less information content corresponds to more entropy. Higher value of entropy is correlated to higher molecular similarity. The maximum entropy of any system (e.g., a mixture or an assemblage) corresponds to indistinguishability (total loss of information), to perfect symmetry or highest symmetry, and to the highest simplicity. This conforms without exception to all the experimental facts of both dynamic systems and static structures and the related information loss processes.
1475764
An Extendible Hash for Multi-Precision Similarity Querying of Image Databases
We propose multi-precision similarity matching where the image is divided into a number of subblocks, each with its associated color histogram. We present experimental results showing that the spatial distribution information recorded by multiprecision color histograms helps to make similarity matching more precise. We also show that sub-image queries are much better supported with multi-precision color histograms. To minimize the overhead, we employ a filtering scheme based on the 3-dimensional average color vectors. We provide a formal result proving that filtering with multi-precision color histograms is complete. Finally, we develop a novel extendible hashing structure for indexing the average color vectors. We give experimental results showing that the proposed structure significantly outperforms the SR-tree.
51877153
ATPC: adaptive transmission power control for wireless sensor networks
Extensive empirical studies presented in this paper confirm that the quality of radio communication between low power sensor devices varies significantly with time and environment. This phenomenon indicates that the previous topology control solutions, which use static transmission power, transmission range, and link quality, might not be effective in the physical world. To address this issue, online transmission power control that adapts to external changes is necessary. This paper presents ATPC, a lightweight algorithm of Adaptive Transmission Power Control for wireless sensor networks. In ATPC, each node builds a model for each of its neighbors, describing the correlation between transmission power and link quality. With this model, we employ a feedback-based transmission power control algorithm to dynamically maintain individual link quality over time. The intellectual contribution of this work lies in a novel pairwise transmission power control, which is significantly different from existing node-level or network-level power control methods. Also different from most existing simulation work, the ATPC design is guided by extensive field experiments of link quality dynamics at various locations and over a long period of time. The results from the real-world experiments demonstrate that 1) with pairwise adjustment, ATPC achieves more energy savings with a finer tuning capability and 2) with online control, ATPC is robust even with environmental changes over time.
11736259
Analog Placement Based on Novel Symmetry-Island Formulation
In this paper, we present the first amortized linear-time packing algorithm for the placement with symmetry constraints. We first introduce the concept of a symmetry island which is formed by modules of the same symmetry group in a single connected placement. Based on this concept and the B*-tree representation, we propose automatically symmetric-feasible B*-trees (ASF-B*-trees) to directly model the placement of a symmetry island. Unlike the previous works that can handle only ID symmetry constraints, our ASF-B*-tree is the first in the literature to additionally consider 2D symmetry. We then present hierarchical B*-trees (HB*-trees) which can simultaneously optimize the placement with both symmetry islands and non-symmetry modules. Unlike the previous works, our approach can guarantee the close proximity of symmetry modules and significantly reduce the search space based on the symmetry-island formulation. In particular, the packing time for an ASF-B*- tree or an HB*-tree is the same as that for a plain B*-tree (only amortized linear) and much faster than previous works which need at least loglinear time. Experimental results show that our approach achieves the best published quality and runtime efficiency for analog placement.