Planning in large partially observable Markov decision processes (POMDPs) is challenging especially when a long planning horizon is required. A few recent algorithms successfully tackle this case but at the expense of a weaker information-gathering capacity. In this paper, we propose Information Gathering and Reward Exploitation of Subgoals (IGRES), a randomized POMDP planning algorithm that leverages information in the state space to automatically generate "macro-actions" to tackle tasks with long planning horizons, while locally exploring the belief space to allow effective information gathering. Experimental results show that IGRES is an effective multi-purpose POMDP solver, providing state-of-the-art performance for both long horizon planning tasks and information-gathering tasks on benchmark domains. Additional experiments with an ecological adaptive management problem indicate that IGRES is a promising tool for POMDP planning in real-world settings.

Much work has been done on understanding and predicting human mobility in time. In this work, we are interested in obtaining a set of users who are spatio-temporally most similar to a query user. We propose an efficient way of user data representation called Spatio-Temporal Signatures to keep track of complete record of user movement. We define a measure called Spatio-Temporal similarity for comparing a given pair of users. Although computing exact pairwise Spatio-Temporal similarities between query user with all users is inefficient, we show that with our hybrid pruning scheme the most similar users can be obtained in logarithmic time with in a (1+\epsilon) factor approximation of the optimal. We are developing a framework to test our models against a real dataset of urban users.

Massive Open Online Courses (MOOCs) have received widespread attention for their potential to scale higher education, with multiple platforms such as Coursera, edX and Udacity recently appearing. Despite their successes, a major problem faced by MOOCs is low completion rates. In this paper, we explore the accurate early identification of students who are at risk of not completing courses. We build predictive models weekly, over multiple offerings of a course. Furthermore, we envision student interventions that present meaningful probabilities of failure, enacted only for marginal students.To be effective, predicted probabilities must be both well-calibrated and smoothed across weeks.Based on logistic regression, we propose two transfer learning algorithms to trade-off smoothness and accuracy by adding a regularization term to minimize the difference of failure probabilities between consecutive weeks. Experimental results on two offerings of a Coursera MOOC establish the effectiveness of our algorithms.

Topic modeling of textual corpora is an important and challenging problem. In most previous work, the “bag-of-words” assumption is usually made which ignores the ordering of words. This assumption simplifies the computation, but it unrealistically loses the ordering information and the semantic of words in the context. In this paper, we present a Gaussian Mixture Neural Topic Model (GMNTM) which incorporates both the ordering of words and the semantic meaning of sentences into topic modeling. Specifically, we represent each topic as a cluster of multi-dimensional vectors and embed the corpus into a collection of vectors generated by the Gaussian mixture model. Each word is affected not only by its topic, but also by the embedding vector of its surrounding words and the context. The Gaussian mixture components and the topic of documents, sentences and words can be learnt jointly. Extensive experiments show that our model can learn better topics and more accurate word distributions for each topic. Quantitatively, comparing to state-of-the-art topic modeling approaches, GMNTM obtains significantly better performance in terms of perplexity, retrieval accuracy and classification accuracy.

This paper overviews our application of state-of-the-art automated planning algorithms to real mobile robots performing an autonomous construction task, a domain in which robots are prone to faults. We describe how embracing these faults leads to better representations and smarter planning, allowing robots with limited precision to avoid catastrophic failures and succeed in intricate constructions.

Most word embedding models typically represent each word using a single vector, which makes these models indiscriminative for ubiquitous homonymy and polysemy. In order to enhance discriminativeness, we employ latent topic models to assign topics for each word in the text corpus, and learn topical word embeddings (TWE) based on both words and their topics. In this way, contextual word embeddings can be flexibly obtained to measure contextual word similarity. We can also build document representations, which are more expressive than some widely-used document models such as latent topic models. In the experiments, we evaluate the TWE models on two tasks, contextual word similarity and text classification. The experimental results show that our models outperform typical word embedding models including the multi-prototype version on contextual word similarity, and also exceed latent topic models and other representative document models on text classification.

Stock movements are essentially driven by new information. Market data, financial news, and social sentiment are believed to have impacts on stock markets. To study the correlation between information and stock movements, previous works typically concatenate the features of different information sources into one super feature vector. However, such concatenated vector approaches treat each information source separately and ignore their interactions. In this article, we model the multi-faceted investors’ information and their intrinsic links with tensors. To identify the nonlinear patterns between stock movements and new information, we propose a supervised tensor regression learning approach to investigate the joint impact of different information sources on stock markets. Experiments on CSI 100 stocks in the year 2011 show that our approach outperforms the state-of-the-art trading strategies.

In this paper we propose a wrapper based PSO method for gene selection in microarray datasets, where we gradually refine the feature (gene) space from a very coarse level to a fine grained one, by reducing the gene set at each step of the algorithm. We use the linear support vector machine weight vector to serve as the initial gene pool selection. In addition, we also examine integration of other filter based ranking methods with our proposed approach. Experiments on publicly available datasets, Colon, Leukemia and T2D show that our approach selects only a very small subset of genes while yielding substantial improvements in accuracy over state-of-the-art evolutionary methods.

Tractable classes constitute an important issue in Artificial Intelligence to define new islands of tractability for reasoning or problem solving. In the area of constraint networks, numerous tractable classes have been defined, and recently, the Broken Triangle Property (BTP) has been shown as one of the most important of them, this class including several classes previously defined. In this paper, we propose a new class called ETP for Extendable-Triple Property, which generalizes BTP, by including it. Combined with the verification of the Strong-Path-Consistency, ETP is shown to be a new tractable class. Moreover, this class inherits some desirable properties of BTP including the fact that the instances of this class can be solved thanks to usual algorithms (such as MAC or RFL) used in most solvers. We give the theoretical material about this new class and we present an experimental study which shows that from a practical viewpoint, it seems more usable in practice than BTP.

Sorted Neighborhood is an established blocking method for relational databases. It has not been applied on graph-based data models such as the Resource Description Framework (RDF). This poster presents a modular workflow for applying Sorted Neighborhood to RDF. Real-world evaluations demonstrate the workflow's utility against a popular baseline. Entity Resolution (ER) is the abstract problem of identifying Figure 1: A simple instance of ER in an RDF graph pairs of entities across databases that are syntactically disparate but logically equivalent. The problem goes by multiple names in the AI community, examples being record Table 1: Tuples sorted using blocking key values (BKVs) linkage, instance matching, and coreference resolution (Elmagarmid, ID First Name Last Name Zip BKV Ipeirotis, and Verykios 2007).