Users share and access large volumes of information on social networking sites like Facebook, Flickr, del.icio.us, etc. Whereas a few of these sites have generic, impersonal searching mechanisms, we have developed an agent-based framework that mines the social network of a user to improve search results. Our Social Network-based Item Search (SNIS) system uses agents that utilize the connections of a user in the social network to facilitate the search for items of interest. Our approach generates targeted search results that can improve the precision of the result returned from a user's query. We have implemented the SNIS agent-based framework in Flickr, a photo-sharing social network, for searching for photos by using tag lists as search queries. We discuss the architecture of SNIS, motivate the searching scheme used, and demonstrate the effectiveness of the SNIS approach by presenting results. We also show how SNIS can be utilized for expertise location.

This paper addresses the problem of activity and event discovery in multi dimensional time series data by proposing a novel method for locating multi dimensional motifs in time series. While recent work has been done in finding single dimensional and multi dimensional motifs in time series, we address motifs in general case, where the elements of multi dimensional motifs have temporal, length, and frequency variations. The proposed method is validated by synthetic data, and empirical evaluation has been done on several wearable systems that are used by real subjects.

Recently, online reputation mechanisms have been proposed that reward agents for honest feedback about products and services with fixed quality. Many real-world settings, however, are inherently dynamic. As an example, consider a web service that wishes to publish the expected download speed of a file mirrored on different server sites. In contrast to the models of Miller, Resnick and Zeckhauser and of Jurca and Faltings, the quality of the service (e. g., a server’s available bandwidth) changes over time and future agents are solely interested in the present quality levels. We show that hidden Markov models (HMM) provide natural generalizations of these static models and design a payment scheme that elicits honest reports from the agents after they have experienced the quality of the service.

We have developed a simulation model that accepts instructions in unconstrained natural language, and then guides a robot to the correct destination. The instructions are segmented on the basis of the actions to be taken, and each segment is labeled with the required action. This flat formulation reduces the problem to a sequential labeling task, to which machine learning methods are applied. We propose an innovativemachine learningmethod for explicitly modeling the actions described in instructions and integrating learning and inference about the physical environment. We obtained a corpus of 840 route instructions that experimenters verified as follow-able, given by people in building navigation situations. Using the four-fold cross validation, our experiments showed that the simulated robot reached the correct destination 88% of the time.

In this paper, we consider a general problem of semi-supervised preference learning, in which we assume that we have the information of the extreme cases and some ordered constraints, our goal is to learn the unknown preferences of the other places. Taking the potential housing place selection problem as an example, we have many candidate places together with their associated information (e.g., position, environment), and we know some extreme examples (i.e., several places are perfect for building a house, and several places are the worst that cannot build a house there), and we know some partially ordered constraints (i.e., for two places, which place is better), then how can we judge the preference of one potential place whose preference is unknown beforehand? We propose a Bayesian framework based on Gaussian process to tackle this problem, from which we not only solve for the unknown preferences, but also the hyperparameters contained in our model.

First order decision diagrams (FODD) were recently introduced as a compact knowledge representation expressing functions over relational structures. FODDs represent numerical functions that, when constrained to the Boolean range, use only existential quantification. Previous work developed a set of operations over FODDs, showed how they can be used to solve relational Markov decision processes (RMDP) using dynamic programming algorithms, and demonstrated their success in solving stochastic planning problems from the International Planning Competition in the system FODD-Planner. A crucial ingredient of this scheme is a set of operations to remove redundancy in decision diagrams, thus keeping them compact. This paper makes three contributions. First, we introduce Generalized FODDs (GFODD) and combination algorithms for them, generalizing FODDs to arbitrary quantification. Second, we show how GFODDs can be used in principle to solve RMDPs with arbitrary quantification, and develop a particularly promising case where an arbitrary number of existential quantifiers is followed by an arbitrary number of universal quantifiers. Third, we develop a new approach to reduce FODDs and GFODDs using model checking. This yields a reduction that is complete for FODDs and provides a sound reduction procedure for GFODDs.

Spatial processes are typically used to analyse and predict geographic data. This paper adapts such models to predicting a user's interests (i.e., implicit item ratings) within a recommender system in the museum domain. We present the theoretical framework for a model based on Gaussian spatial processes, and discuss efficient algorithms for parameter estimation. Our model was evaluated with a real-world dataset collected by tracking visitors in a museum, attaining a higher predictive accuracy than state-of-the-art collaborative filters.

This paper investigates a generative history-based parsing model that synchronises the derivation of non-planar graphs representing semantic dependencies with the derivation of dependency trees representing syntactic structures. To process non-planarity online, the semantic transition-based parser uses a new technique to dynamically reorder nodes during the derivation. While the synchronised derivations allow different structures to be built for the semantic non-planar graphs and syntactic dependency trees, useful statistical dependencies between these structures are modeled using latent variables. The resulting synchronous parser achieves competitive performance on the CoNLL-2008 shared task, achieving relative error reduction of 12% in semantic F score over previously proposed synchronous models that cannot process non-planarity online.

A method for expressive melody synthesis is presented seeking to capture the prosodic (stress and directional) element of musical interpretation. An expressive performance is represented as a note-level annotation, classifying each note according to a small alphabet of symbols describing the role of the note within a larger context.  An audio performance of the melody is represented in terms of two time-varying functions describing the evolving frequency and intensity.  A method is presented that transforms the expressive annotation into the frequency and intensity functions, thus giving the audio performance. The problem of expressive rendering is then cast as estimation of the most likely sequence of hidden variables corresponding to the prosodic annotation. Examples are presented on a dataset of around 50 folk-like melodies, realized both from hand-marked and estimated annotations.

A common approach to the control problem in partially observable environments is to perform a direct search in policy space, as defined over some set of features of history. In this paper we consider predictive features, whose values are conditional probabilities of future events, given history. Since predictive features provide direct information about the agent's future, they have a number of advantages for control. However, unlike more typical features defined directly over past observations, it is not clear how to maintain the values of predictive features over time. A model could be used, since a model can make any prediction about the future, but in many cases learning a model is infeasible. In this paper we demonstrate that in some cases it is possible to learn to maintain the values of a set of predictive features even when a learning a model is infeasible, and that natural predictive features can be useful for policy-search methods.