The combinatorial problems that constraint programming typically solves belong to the class of NP-hard problems. The AI planning community focuses on even harder problems: for example, classical planning is PSPACE-hard. A natural and well-known constraint programming approach to classical planning solves a succession of fixed plan-length problems, but with limited success. We revisit this approach in light of recent progress on general-purpose branching heuristics. We conduct an empirical comparison of our proposal against state-of-the-art planners.

In this paper, we explore the idea of building an adaptive user interest model. Our proposed system uses implicit data extracted from a user’s search queries to select categorical information from DBpedia. By combining the categorical information collected from multiple queries and exploiting the semantic relationships between these categories, it becomes possible for our system to build a model of the user's interests. This model is designed to be responsive to changes in the user's interests over time by including concepts of aging and expiration. Our system also includes mechanisms to pinpoint the correct categories when an ambiguous term is queried. We evaluated our system using a predefined set of test queries and shown to correctly model user short term and long term interests.

In this work, we introduce a new generalized nonlinear tensor regression framework called kernel-based multiblock tensor partial least squares (KMTPLS) for predicting a set of dependent tensor blocks from a set of independent tensor blocks through the extraction of a small number of common and discriminative latent components. By considering both common and discriminative features, KMTPLS effectively fuses the information from multiple tensorial data sources and unifies the single and multiblock tensor regression scenarios into one general model. Moreover, in contrast to multilinear model, KMTPLS successfully addresses the nonlinear dependencies between multiple response and predictor tensor blocks by combining kernel machines with joint Tucker decomposition, resulting in a significant performance gain in terms of predictability. An efficient learning algorithm for KMTPLS based on sequentially extracting common and discriminative latent vectors is also presented. Finally, to show the effectiveness and advantages of our approach, we test it on the real-life regression task in computer vision, i.e., reconstruction of human pose from multiview video sequences.

Personalized medicine targets the customization of treatment strategies to patients' individual characteristics. Here we consider the problem of optimizing personalized pharmacological treatment strategies for cancer. We focus primarily on developing effective strategies to collect the data necessary for the construction of personalized treatments. We formulate this problem as a contextual bandit and present a new algorithm based on repeated sub-sampling for robust data collection in this framework. We present a case study showing experiments on a simulation setting, built from real data collected in a previous animal experiments. Promising results in this case study have since lead us to deploy this strategy in a partner wet lab to allocate treatments for the next phase of animal experiments.

This paper presents an extension of the cascading Indian buffet process (CIBP) intended to learning arbitrary directed acyclic graph structures as opposed to the CIBP, which is limited to purely layered structures. The extended cascading Indian buffet process (eCIBP) essentially consists in adding an extra sampling step to the CIBP to generate connections between non-consecutive layers. In the context of graphical model structure learning, the proposed approach allows learning structures having an unbounded number of hidden random variables and automatically selecting the model complexity. We evaluated the extended process on multivariate density estimation and structure identification tasks by measuring the structure complexity and predictive performance. The results suggest the extension leads to extracting simpler graphs without scarifying predictive precision.

We study in this paper how explicit user feedback can be used by a case-based reasoning system to improve the quality of its retrieval phase. More specifically, we explore how both ranking feedback and relevance feedback can be exploited to modify the weights of case features. We propose some options to cope with each type of feedback. We also evaluate, in an interactive setting, their impact on a travel scenario where some user provides feedback on a series of queries. Our results indicate that the combined weight-learning scheme proposed in this paper succeeds, on average, to assign more weights to the features used to formulate relevance and ranking feedback.

Over the course of the last decade, infrared (IR) and particularly thermal IR imaging based face recognition has emerged as a promising complement to conventional, visible spectrum based approaches which continue to struggle when applied in the real world. While inherently insensitive to visible spectrum illumination changes, IR images introduce specific challenges of their own, most notably sensitivity to factors which affect facial heat emission patterns, e.g. emotional state, ambient temperature, and alcohol intake. In addition, facial expression and pose changes are more difficult to correct in IR images because they are less rich in high frequency detail which is an important cue for fitting any deformable model. In this paper we describe a novel method which addresses these major challenges. Specifically, to normalize for pose and facial expression changes we generate a synthetic frontal image of a face in a canonical, neutral facial expression from an image of the face in an arbitrary pose and facial expression. This is achieved by piecewise affine warping which follows active appearance model (AAM) fitting. This is the first publication which explores the use of an AAM on thermal IR images; we propose a pre-processing step which enhances detail in thermal images, making AAM convergence faster and more accurate. To overcome the problem of thermal IR image sensitivity to the exact pattern of facial temperature emissions we describe a representation based on reliable anatomical features. In contrast to previous approaches, our representation is not binary; rather, our method accounts for the reliability of the extracted features. This makes the proposed representation much more robust both to pose and scale changes. The effectiveness of the proposed approach is demonstrated on the largest public database of thermal IR images of faces on which it achieved 100% identification rate, significantly outperforming previously described methods.

Analyzing international political behavior based on similar precedent circumstances is one of the basic techniques that policymakers use to monitor and assess current situations. Our goal is to investigate how to analyze geopolitical conflicts as sequences of events and to determine what probabilistic models are suitable to perform these analyses. In this paper, we evaluate the performance of N-grams models on the problem of forecasting political conflicts from sequences of events. For the current phase of the project, we focused on event data collected from the Balkans war in the 1990's. Our experimental results indicate that N-gram models have impressive results when applied to this data set, with accuracies above 90\% for most configurations.

This paper presents a technique for approximating, up to any precision, the set of subgame-perfect equilibria (SPE) in repeated games with discounting. The process starts with a single hypercube approximation of the set of SPE payoff profiles. Then the initial hypercube is gradually partitioned on to a set of smaller adjacent hypercubes, while those hypercubes that cannot contain any SPE point are gradually withdrawn. Whether a given hypercube can contain an equilibrium point is verified by an appropriate mixed integer program. A special attention is paid to the question of extracting players' strategies and their representability in form of finite automata.