6390508
A Practical Markov Chain Monte Carlo Approach to Decision Problems
Decision and optimization problems involving graphs arise in many areas of artificial intelligence, including probabilistic networks, robot navigation, and network design. Many such problems are NP-complete; this has necessitated the development of approximation methods, most of which are very complex and highly problemspecific. We propose a straightforward, practical approach to algorithm design based on Markov Chain Monte Carlo (MCMC), a statistical simulation scheme for efficiently sampling from a large (possibly exponential) set, such as the set of feasible solutions to a combinatorial task. This facilitates the development of simple, efficient, and general solutions to whole classes of decision problems. We provide detailed examples showing how this approach can be used for spanning tree problems such as Degree-Constrained Spanning Tree, Maximum Leaf Spanning Tree, and Kth Best Spanning Tree.
1127387
Automatic Symbolic Traffic Scene Analysis Using Belief Networks
Automatic symbolic traffic scene analysis is essential to many areas of IVHS (Intelligent Vehicle Highway Systems). Traffic scene information can be used to optimize traffic flow during busy periods, identify stalled vehicles and accidents, and aid the decision-making of an autonomous vehicle controller. Improvements in technologies for machine vision-based surveillance and high-level symbolic reasoning have enabled us to develop a system for detailed, reliable traffic scene analysis. The machine vision component of our system employs a contour tracker and an affine motion model based on Kalman filters to extract vehicle trajectories over a sequence of traffic scene images. The symbolic reasoning component uses a dynamic belief network to make inferences about traffic events such as vehicle lane changes and stalls. In this paper, we discuss the key tasks of the vision and reasoning components as well as their integration into a working prototype.
6997209
Object Identification in a Bayesian Context
Object identification--the task of deciding that two observed objects are in fact one and the same object--is a fundamental requirement for any situated agent that reasons about individuals. Object identity, as represented by the equality operator between two terms in predicate calculus, is essentially a first-order concept. Raw sensory observations, on the other hand, are essentially propositional-- especially when formulated as evidence in standard probability theory. This paper describes patterns of reasoning that allow identity sentences to be grounded in sensory observations, thereby bridging the gap. We begin by defining a physical event space over which probabilities are defined. We then introduce an identity criterion, which selects those events that correspond to identity between observed objects. From this, we are able to compute the probability that any two objects are the same, given a stream of observations of many objects. We show that the appearance probability, which defines how an object can be expected to appear at subsequent observations given its current appearance, is a natural model for this type of reasoning. We apply the theory to the task of recognizing cars observed by cameras at widely separated sites in a freeway network, with new heuristics to handle the inevitable complexity of matching large numbers of objects and with online learning of appearance probability models. Despite extremely noisy observations, we are able to achieve high levels of performance.
11536780
The BATmobile: Towards a Bayesian Automated Taxi
The problem of driving an autonomous vehicle in normal traffic engages many areas of AI research and has substantial economic significance. We describe work in progress on a new approach to this problem that uses a decision-theoretic architecture using dynamic probabilistic networks. The architecture provides a sound solution to the problems of sensor noise, sensor failure, and uncertainty about the behavior of other vehicles and about the effects of one's own actions. We report on advances in the theory of inference and decision making in dynamic, partially observable domains. Our approach has been implemented in a simulation system, and the autonomous vehicle successfully negotiates a variety of difficult situations.
14495930
Experiments with learning opening strategy in the game of go
In this paper, we present an experimental methodology and results for a machine learning approach to learning opening strategy in the game of Go, a game for which the best computer programs play only at the level of an advanced beginning human player. While the evaluation function in most computer Go programs consists of a carefully crafted combination of pattern matchers, expert rules, and selective search, we employ a neural network trained by self-play using temporal difference learning. Our focus is on the sequence of moves made at the beginning of the game. Experimental results indicate that our approach is effective for learning opening strategy, and they also identify higherlevel features of the game that improve the quality of the learned evaluation function.
45740416
Automated Information Mining on Multimedia TV News Archives
This paper addresses an integrated information mining techniques for multimedia TV-news archive. The utilizes techniques from the fields of acoustic, image, and video analysis, for information retrieval on news story title, newsman and scene identification. The goal is to construct a compact yet meaningful abstraction of broadcast news video, allowing users to browse through large amounts of data in a non-linear fashion with flexibility and efficiency. By using acoustic analysis, the system can classify video into news versus commercials, with 90% accuracy on a data set of 400 hours TV-news recorded off the air from July 2003 to August of 2004. By applying speaker identification and/or image detection techniques, each news stories can be segmented with an accuracy of 96%. On screen captions or subtitles are recognized by OCR techniques to produce the text title of each news stories. The extracted title words can be used to link or to navigate more related News contents on the WWW. In cooperation with facial and scene analysis and recognition techniques, OCR results can provide users with multimodality query for specific news stories. Some experimental results are presented and discussed for the system reliability and performance evaluation and comparison.
30107270
ImageGrouper: a group-oriented user interface for content-based image retrieval and digital image arrangement
In content-based image retrieval (CBIR), experimental (trial-and-error) query with relevance feedback is essential for successful retrieval. Unfortunately, the traditional user interfaces are not suitable for trying different combinations of query examples. This is because first, these systems assume query examples are always added incrementally. Second, the query and the result display are done on the same workspace. Once the user removes an image from the query examples, the image may disappear from the user interface. In addition, it is difficult to combine the result of different queries. 
 
In this paper, we propose a new interface for Content-based image retrieval named ImageGrouper. ImageGrouper is a Group-Oriented user interface in that all operations are done by creating groups of images. This approach has several advantages. First, the users can interactively compare different combinations of the query examples by dragging and grouping the images on the workspace (Query-by-Group). Because the query results are displayed on another pane, the user can quickly review the results and modify the query. Combining different queries is also easy. Furthermore, the concept of “Image Groups” is also applied for annotating and organizing many images. The Annotation-by-Groups method relieves the user of tedious task of annotating textual information on a large number of images. This method realizes a hierarchical annotation of the images as well as Bulk Annotation. The Organize-by-Group method lets the users manipulate the image groups as “Photo Albums” to organize the images. Finally, the usability of the system is compared with the traditional user interfaces. By incorporating the lessons from the experiments, the usability of ImageGrouper is further improved.
1080978
Fast pattern-based throughput prediction for TCP bulk transfers
The ability to quickly predict the throughput of a TCP transfer between a client and a server, or between peers, has wide application in scientific computing and commercial computing. This paper presents a new approach to fast prediction of overall throughput of a large TCP file transfer. The method constructs the time series of windows of segments arriving at the receiver, and predicts future throughput by exploiting knowledge of how TCP manages transfer window size. When the file transfer time series resembles a known TCP pattern, this information is utilized for prediction, otherwise simple heuristics are used. We have compared TCP pattern based prediction against traditional methods like a simple moving average, exponential weighted moving average, and aggregate measured throughput on a large suite of real life TCP traces. Our results show that TCP pattern based prediction generally performs as well or better than the best of other methods in any given scenario.
15741182
Bias Term b in SVMs Again
The paper discusses and presents the use and calculation of the e x- plicit bias term b in the support vector machines (SVMs) within the Iterative Single t raining Data learning Algorithm (ISDA). The approach proposed can be used for both nonlinear classification and nonlinear regression tasks. Unlike the other iterative methods in solvin g the SVMs learning problems containing the huge data sets, such as sequential minimal optimization (SMO) and its variants that must use at least two training data pairs, the algorithms shown here use the single training data based iteration routine for so lving QP learning problem. In this way the various 2 nd order heuristics in choosing the data for an updating is avoided. This makes the proposed ISD learning method remarkably quick. The algorithm can also be thought off as an application of a classic Gauss-Seidel (GS) coordinate ascent procedure and its derivative known as the successive over-relaxation (SOR) algorithm in SVMs learning from huge data sets subject to both the box constraints and the equality ones. (The later coming from min i- mizing the primal objective function in respect to the bias term b). The final solution in a dual domain is not an approximate one, but it is the optimal set of dual variables which would have been obtained by using any of existing and proven QP problem solvers if they only could deal with huge data sets. Proceedings of 12
14318268
Learning and Feature Selection in Stereo Matching
We present a novel stereo matching algorithm which integrates learning, feature selection, and surface reconstruction. First, a new instance based learning (IBL) algorithm is used to generate an approximation to the optimal feature set for matching. In addition, the importance of two separate kinds of knowledge, image dependent knowledge and image independent knowledge, is discussed. Second, we develop an adaptive method for refining the feature set. This adaptive method analyzes the feature error to locate areas of the image that would lead to false matches. Then these areas are used to guide the search through feature space towards maximizing the class separation distance between the correct match and the false matches. Third, we introduce a self-diagnostic method for determining when apriori knowledge is necessary for finding the correct match. If the a priori knowledge is necessary then we use a surface reconstruction model to discriminate between match possibilities. Our algorithm is comprehensively tested against fixed feature set algorithms and against a traditional pyramid algorithm. Finally, we present and discuss extensive empirical results of our algorithm based on a large set of real images. >