Local search algorithms applied to optimization problems often suffer from getting trapped in a local optimum. The common solution for this deficiency is to restart the algorithm when no progress is observed. Alternatively, one can start multiple instances of a local search algorithm, and allocate computational resources (in particular, processing time) to the instances depending on their behavior. Hence, a multi-start strategy has to decide (dynamically) when to allocate additional resources to a particular instance and when to start new instances. In this paper we propose multi-start strategies motivated by works on multi-armed bandit problems and Lipschitz optimization with an unknown constant. The strategies continuously estimate the potential performance of each algorithm instance by supposing a convergence rate of the local search algorithm up to an unknown constant, and in every phase allocate resources to those instances that could converge to the optimum for a particular range of the constant. Asymptotic bounds are given on the performance of the strategies. In particular, we prove that at most a quadratic increase in the number of times the target function is evaluated is needed to achieve the performance of a local search algorithm started from the attraction region of the optimum. Experiments are provided using SPSA (Simultaneous Perturbation Stochastic Approximation) and k-means as local search algorithms, and the results indicate that the proposed strategies work well in practice, and, in all cases studied, need only logarithmically more evaluations of the target function as opposed to the theoretically suggested quadratic increase.

Most of the text mining tasks, such as clustering, is dominated by statistical approaches that treat text as a bag of words. Semantics in the text is largely ignored in the mining process, and the mining results are often not easily interpretable. One particular challenge faced by such approaches is short text understanding, as short text lacks enough content from which a statistical conclusion can be drawn. For example, traditional topic analysis methods consider topic segments with tens of hundreds of words. Latent topic modeling, such as latent Dirichlet allocation, also requires sufficient words to infer document topic distribution. We enhance machine learning algorithms by first giving the machine a probabilistic knowledgebase that contains as big, rich, and consistent concepts (of worldly facts) as those in our mental world. Then a Bayesian inference mechanism is developed to conceptualize words and short text. We conducted comprehensive tests of our method on conceptualizing set of text terms, as well as clustering Twitter messages (tweets), which are typically approximately ten words long. Compared to latent semantic topic modeling and other four kinds of methods that using WordNet, Freebase and Wikipedia (category links and explicit semantic analysis), we show significant improvements in terms of tweets clustering accuracy.

In this paper we address the problem of matrix factorization on compressively-sampled measurements which are obtained by random projections. While this approach improves the scalability of matrix factorization, its performance is not satisfactory. We present a matrix co-factorization method where compressed measurements and a small number of uncompressed measurements are jointly decomposed, sharing a factor matrix. We evaluate the performance of three matrix factorization methods in terms of Cram{\'e}r-Rao bounds, including: (1) matrix factorization on uncompressed data (MF); (2) matrix factorization on compressed data (CS-MF); (3) matrix co-factorization on compressed and uncompressed data (CS-MCF). Numerical experiments demonstrate that CS-MCF improves the performance of CS-MF, emphasizing the useful behavior of exploiting side information (a small number of uncompressed measurements).

Model checking is one of the most effective techniques in automated system verification. Although this technique can handle complex verifications, model checking tools usually do not give any suggestions on how to repair inconsistent system models. In this paper, we show that approaches developed to update models of Computation Tree Logic (CTL) cannot deal with all kinds of changes. We introduce the concept of CTL model revision: an approach based on belief revision to handle system inconsistency in a static context.

Recently, the use of social and human computing has witnessed increasing interest in the AI community. However, in order to harness the true potential of social computing, human subjects must play an active role in achieving computation in social networks and related media. Our work proposes an initial desiderata for effective social computing, drawing inspiration from artificial intelligence. Extensive experimentation reveals that several open issues and research questions have to be answered before the true potential of social and human computing is achieved. We, however, take a somewhat novel approach, by implementing a social networks environment where human subjects cooperate towards computational problem solving. In our social environment, human and artificial agents cooperate in their computation tasks,which may lead to a single problem-solving social network that potentially allows seamless cooperation among human and machine agents.

One of the main research problems in Structural Bioinformatics is the analysis and prediction of three-dimensional structures (3-D) of polypeptides or proteins. The 1990’s Genome projects resulted in a large increase in the number of protein sequences. However, the number of identified 3-D protein structures has not followed the same trend.The determination of protein structure is experimentally expensive and time consuming. This makes scientists largely dependent on computational methods that can predict correct 3-D protein structures only from extended and full amino acid sequences. Several computational methodologies and algorithms have been proposed as a solution to the Protein Structure Prediction (PSP) problem. We briefly describe the AI techniques we have been used to tackle this problem.

In today's services driven economic environment, it is imperative for organizations to provide better quality service experience to differentiate and grow their business. Customer satisfaction (C-SAT) is the key driver for retention and growth in Retail Banking. Wait time, the time spent by a customer at the branch before getting serviced, contributes significantly to C-SAT. Due to high footfall, it is improbable to improve the wait time of every customer walking in the branch. Therefore, banks in developing countries are strategically looking to segment its customers and services and offer differentiated QoS based service delivery. In this work, we present a system for customer segmentation, and scheduling based on historic value of the customer and characteristics of current service request. We describe the system and give mathematical formulation of the scheduling problem and the associated heuristics. We present results and experience of deployment of this solution in multiple branches of a leading bank in India.

This paper formulates learning optimal Bayesian network as a shortest path finding problem. An A* search algorithm is introduced to solve the problem. With the guidance of a consistent heuristic, the algorithm learns an optimal Bayesian networkby only searching the most promising parts of the solution space. Empirical results show that the A*search algorithm significantly improves the time and space efficiency of existing methods on a set of benchmark datasets.

Planning with partial observability can be formulated as a non-deterministic search problem in belief space. The problem is harder than classical planning as keeping track of beliefs is harder than keeping track of states, and searching for action policies is harder than searching for action sequences. In this work, we develop a framework for partial observability that avoids these limitations and leads to a planner that scales up to larger problems. For this, the class of problems is restricted to those in which 1) the non-unary clauses representing the uncertainty about the initial situation are nvariant, and 2) variables that are hidden in the initial situation do not appear in the body of conditional effects, which are all assumed to be deterministic. We show that such problems can be translated in linear time into equivalent fully observable non-deterministic planning problems, and that an slight extension of this translation renders the problem solvable by means of classical planners. The whole approach is sound and complete provided that in addition, the state-space is connected. Experiments are also reported.

As tweets has become a comprehensive repository of fresh information, Semantic Role Labeling (SRL) for tweets has aroused great research interests because of its center role in a wide range of tweet related studies such as fine-grained information extraction, sentiment analysis and summarization. However, the fact that a tweet is often too short and informal to provide sufficient information poses a main challenge. To tackle this challenge, we propose a new method to collectively label similar tweets. The underlying idea is to exploit similar tweets to make up for the lack of information in a tweet. Specifically, similar tweets are first grouped together by clustering. Then for each cluster a two-stage labeling is conducted: One labeler conducts SRL to get statistical information, such as the predicate/argument/role triples that occur frequently, from its highly confidently labeled results; then in the second stage, another labeler performs SRL with such statistical information to refine the results. Experimental results on a human annotated dataset show that our approach remarkably improves SRL by 3.1% F1.