We propose revisions to the research agenda in Automated Planning. The proposal is based on a review of the role of the Planning Domain Definition Language (PDDL) in the activities of the AI planning community and the impact of PDDL on parts of its research agenda. We specifically show how specific properties of PDDL have impacted research on planning, by putting emphasis on certain research topics and complicating others. We argue that the development of more advanced modeling languages would be — analogously to the impact PDDL has had — a low overhead and smooth route for the ICAPS community shift its research focus to increasingly promising and relevant research topics.

As one of the most important state-of-the-art classification techniques, Support Vector Machine (SVM) has been widely adopted in many real-world applications, such as object detection, face recognition, text categorization, etc., due to its competitive practical performance and elegant theoretical interpretation. However, it treats all samples independently, and ignores the fact that, in many real situations especially when data are in high dimensional space, samples typically lie on low dimensional manifolds of the feature space and thus a sample can be related to its neighbors by being represented as a linear combination of other samples on the same manifold. This linear representation, which is usually sparse, reflects the structure of underlying manifolds. It has been extensively explored in the recent literature and proven to be critical for the performance of classification. To benefit from both the underlying low dimensional manifold structure and the large margin classifier, this paper proposes a novel method called Sparsity Preserving Support Vector Machine(SP-SVM), which explicitly considers the sparse representation of samples while maximizing the margin between different classes. Consequently, SP-SVM inherits both the discriminative power of support vector machine and the merits of sparsity. A set of experiments on real-world benchmark data sets show that SP-SVM achieves significantly higher precision on recognition task than various competitive baselines including the traditional SVM, the sparse representation based method and the classical nearest neighbor classifier.

The related problems of transfer learning and multitask learning have attracted significant attention, generating a rich literature of models and algorithms. Yet most existing approaches are studied in an offline fashion, implicitly assuming that data from different domains are given as a batch. Such an assumption is not valid in many real-world applications where data samples arrive sequentially, and one wants a good learner even from few examples. The goal of our work is to provide sound extensions to existing transfer and multitask learning algorithms such that they can be used in an anytime setting. More specifically, we propose two novel online boosting algorithms, one for transfer learning and one for multitask learning, both designed to leverage the knowledge of instances in other domains. The experimental results show state-of-the-art empirical performance on standard benchmarks, and we present results of using our methods for effectively detecting new seizures in patients with epilepsy from very few previous samples.

With the rise of social media, learning from informal text has become increasingly important. We present a novel semantic lexicon induction approach that is able to learn new vocabulary from social media. Our method is robust to the idiosyncrasies of informal and open-domain text corpora. Unlike previous work, it does not impose restrictions on the lexical features of candidate terms — e.g. by restricting entries to nouns or noun phrases —while still being able to accurately learn multiword phrases of variable length. Starting with a few seed terms for a semantic category, our method first explores the context around seed terms in a corpus, and identifies context patterns that are relevant to the category. These patterns are used to extract candidate terms — i.e. multiword segments that are further analyzed to ensure meaningful term boundary segmentation. We show that our approach is able to learn high quality semantic lexicons from informally written social media text of Twitter, and can achieve accuracy as high as 92% in the top 100 learned category members.

We describe a new instance-based learning algorithm called the Boundary Forest (BF) algorithm, that can be used for supervised and unsupervised learning. The al- gorithm builds a forest of trees whose nodes store previ- ously seen examples. It can be shown data points one at a time and updates itself incrementally, hence it is nat- urally online. Few instance-based algorithms have this property while being simultaneously fast, which the BF is. This is crucial for applications where one needs to respond to input data in real time. The number of chil- dren of each node is not set beforehand but obtained from the training procedure, which makes the algorithm very flexible with regards to what data manifolds it can learn. We test its generalization performance and speed on a range of benchmark datasets and detail in which settings it outperforms the state of the art. Empirically we find that training time scales as O(DN log(N )) and testing as O(Dlog(N)), where D is the dimensionality and N the amount of data.

Bayesian decision-theory underpins robust decision-making in applications ranging from plant control to robotics where hedging action selection against state uncertainty is critical for minimizing low probability but potentially catastrophic outcomes (e.g, uncontrollable plant conditions or robots falling into stairwells). Unfortunately, belief state distributions in such settings are often complex and/or high dimensional, thus prohibiting the efficient application of analytical techniques for expected utility computation when real-time control is required. This leaves Monte Carlo evaluation as one of the few viable (and hence frequently used) techniques for online action selection. However, loss-insensitive Monte Carlo methods may require large numbers of samples to identify optimal actions with high certainty since they may sample from highprobability regions that do not disambiguate action utilities. In this paper we remedy this problem by deriving an optimal proposal distribution for a loss-calibrated Monte Carlo importance sampler that bounds the regret of using an estimated optimal action. Empirically, we show that using our loss-calibrated Monte Carlo method yields high-accuracy optimal action selections in a fraction of the number of samples required by conventional loss-insensitive samplers.

A large-scale training corpus consisting of microblogs belonging to a desired category is important for high-accuracy microblog retrieval. Obtaining such a large-scale microblgging corpus manually is very time and labor-consuming. Therefore, some models for the automatic retrieval of microblogs froman exterior corpus have been proposed. However, these approaches may fail in considering microblog-specific features. To alleviate this issue, we propose a methodology that constructs a simulated microblogging corpus rather than directly building a model from the exterior corpus. The performance of our model is better since the microblog-special knowledge of the microblogging corpus is used in the end by the retrieval model. Experimental results on real-world microblogs demonstrate the superiority of our technique compared to the previous approaches.

Our society is organized around a number of (interdependent) global systems. Logistic and supply chains, health services, energy networks, financial markets, computer networks, and cities are just a few examples of such global, complex systems. These global systems are socio-technical and involve interactions between complex infrastructures, man-made processes, natural phenomena, multiple stakeholders, and human behavior. For the first time in the history of manking, we have access to data sets of unprecedented scale and accuracy about these infrastructures, processes, natural phenomena, and human behaviors. In addition, progress in high-performancing computing, data mining, machine learning, and decision support opens the possibility of looking at these problems more holistically, capturing many of these aspects simultaneously. This paper addresses emergent architectures enabling controlling, predicting and reaoning on these systems.

The rapid urban expansion has greatly extended the physical boundary of users' living area and developed a large number of POIs (points of interest). POI recommendation is a task that facilitates users' urban exploration and helps them filter uninteresting POIs for decision making. While existing work of POI recommendation on location-based social networks (LBSNs) discovers the spatial, temporal, and social patterns of user check-in behavior, the use of content information has not been systematically studied. The various types of content information available on LBSNs could be related to different aspects of a user's check-in action, providing a unique opportunity for POI recommendation. In this work, we study the content information on LBSNs w.r.t. POI properties, user interests, and sentiment indications. We model the three types of information under a unified POI recommendation framework with the consideration of their relationship to check-in actions. The experimental results exhibit the significance of content information in explaining user behavior, and demonstrate its power to improve POI recommendation performance on LBSNs.

Online advertising is an important and huge industry. Having knowledge of the website attributes can contribute greatly to business strategies for ad-targeting, content display, inventory purchase or revenue prediction. In this paper, we introduce a stochastic blockmodeling for the website relations induced by the event of online user visitation. We propose two clustering algorithms to discover the intrinsic structures of websites, and compare the performance with a goodness-of-fit method and a deterministic graph partitioning method. We demonstrate the effectiveness of our algorithms on both simulation and AOL website dataset.