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.
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.
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. >
813090
Evaluation of salient point techniques
Abstract In image retrieval, global features related to color or texture are commonly used to describe the image content. The problem with this approach is that these global features cannot capture all parts of the image having different characteristics. Therefore, local computation of image information is necessary. By using salient points to represent local information, more discriminative features can be computed. In this paper, we compare a wavelet-based salient point extraction algorithm with two corner detectors using the criteria: repeatability rate and information content. We also show that extracting color and texture information in the locations given by our salient points provides significantly improved results in terms of retrieval accuracy, computational complexity, and storage space of feature vectors as compared to global feature approaches.
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.
9038741
Semantic Scoring Based on Small-World Phenomenon for Feature Selection in Text Mining
This paper proposes an effective scoring scheme for feature selection in Text Mining, using characteristics of Small-World Phenomenon on the semantic networks of documents. Our focus is on the reservation of both syntactic and statistical information of words, rather than solely simple frequency summarization in prevailing scoring schemes, such as TFIDF. Experimental results on TREC dataset show that our scoring scheme outperforms the prevailing schemes.
17604511
POINT: a database for the prediction of protein-protein interactions based on the orthologous interactome
One possible path towards understanding the biological function of a target protein is through the discovery of how it interfaces within protein-protein interaction networks. The goal of this study was to create a virtual protein-protein interaction model using the concepts of orthologous conservation (or interologs) to elucidate the interacting networks of a particular target protein. POINT (the prediction of interactome database) is a functional database for the prediction of the human protein-protein interactome based on available orthologous interactome datasets. POINT integrates several publicly accessible databases, with emphasis placed on the extraction of a large quantity of mouse, fruit fly, worm and yeast protein-protein interactions datasets from the Database of Interacting Proteins (DIP), followed by conversion of them into a predicted human interactome. In addition, protein-protein interactions require both temporal synchronicity and precise spatial proximity. POINT therefore also incorporates correlated mRNA expression clusters obtained from cell cycle microarray databases and subcellular localization from Gene Ontology to further pinpoint the likelihood of biological relevance of each predicted interacting sets of protein partners.