Distributed word representations have a rising interest in NLP community. Most of existing models assume only one vector for each individual word, which ignores polysemy and thus degrades their effectiveness for downstream tasks. To address this problem, some recent work adopts multi-prototype models to learn multiple embeddings per word type. In this paper, we distinguish the different senses of each word by their latent topics. We present a general architecture to learn the word and topic embeddings efficiently, which is an extension to the Skip-Gram model and can model the interaction between words and topics simultaneously. The experiments on the word similarity and text classification tasks show our model outperforms state-of-the-art methods.

This paper studies the complexity of model checking multiagent systems, in particular systems succinctly described by two practical representations: concurrent representation and symbolic representation. The logics we concern include branching time temporal logics and several variants of alternating time temporal logics.

Symmetries are common in many constraint problems. They can be broken statically or dynamically. The focus of this paper is the symmetry breaking during search (SBDS) method that adds conditional symmetry breaking constraints upon each backtracking during search. To trade completeness for efficiency, partial SBDS (ParSBDS) is proposed by posting only a subset of symmetries. We propose an adaptation method recursive SBDS (ReSBDS) of ParSBDS which extends ParSBDS to break more symmetry compositions. We observe that the symmetry breaking constraints added for each symmetry at a search node are nogoods and increasing. A global constraint (incNGs), which is logically equivalent to a set of increasing nogoods, is derived. To further trade pruning power for efficiency, we propose weak-nogood consistency (WNC) for nogoods and a lazy propagator for SBDS (and its variants) using watched literal technology. We further define generalized weak-incNGs consistency (GWIC) for a conjunction of increasing nogoods, and give a lazy propagator for incNGs.

The last years have seen an exponential increase in the interest for the development of methods for combining probability with Description Logics (DLs). These methods are very useful to model real world domains, where incompleteness and uncertainty are common. This combination has become a fundamental component of the Semantic Web.Our work started with the development of a probabilistic semantics for DL, called DISPONTE, that applies the distribution semantics to DLs. Under DISPONTE we annotate axioms of a theory with a probability, that can be interpreted as the degree of our belief in the corresponding axiom, and we assume that each axiom is independent of the others. Several algorithms have been proposed for supporting the development of the Semantic Web. Efficient DL reasoners, such us Pellet, are able to extract implicit information from the modeled ontologies. Despite the availability of many DL reasoners, the number of probabilistic reasoners is quite small. We developed BUNDLE, a reasoner based on Pellet that allows to compute the probability of queries. BUNDLE, like most DL reasoners, exploits an imperative language for implementing its reasoning algorithm. Nonetheless, usually reasoning algorithms use non-deterministic operators for doing inference. One of the most used approaches for doing reasoning is the tableau algorithm which applies a set of consistency preserving expansion rules to an ABox, but some of these rules are non-deterministic.In order to manage this non-determinism, we developed the system TRILL which performs inference over DISPONTE DLs. It implements the tableau algorithm in the declarative Prolog language, whose search strategy is exploited for taking into account the non-determinism of the reasoning process. Moreover, we developed a second version of TRILL, called TRILL^P, which implements some optimizations for reducing the running time. The parameters of probabilistic KBs are difficult to set. It is thus necessary to develop systems which automatically learn this parameters starting from the information available in the KB. We presented EDGE that learns the parameters of a DISPONTE KB, and LEAP, that learn the structure together with the parameters of a DISPONTE KB. The main objective is to apply the developed algorithms to Big Data. Nonetheless, the size of the data requires the implementation of algorithms able to handle it. It is thus necessary to exploit approaches based on the parallelization and on cloud computing. Nowadays, we are working to improve EDGE and LEAP in order to parallelize them.

We set out to challenge the understanding that it is difficult My work is an integrated interdisciplinary effort which employs to study the complex processing of natural stories. We used functional neuroimaging, and revolves around the development functional Magnetic Resonance Imaging (fMRI) to record the of machine learning methods to uncover multilayer brain activity of subjects while they read an unmodified chapter cognitive processes from brain activity recordings. of a popular book. Unprecedently, we modeled the measured Studying how the human brain represents meaning is not brain activity as a function of the content of the text only important for expanding our scientific knowledge of the being read Wehbe et al. [2014a]. Our model is able to extrapolate brain and of intelligence. By mapping behavioral traits to differences to predict brain activity for novel passages of text - in brain representations, we increase our understanding beyond those on which it has been trained.

Trust systems are widely used to facilitate interactions among agents based on trust evaluation. These systems may have robustness issues, that is, they are affected by various attacks. Designers of trust systems propose methods to defend against these attacks. However, they typically verify the robustness of their defense mechanisms (or trust models) only under specific attacks. This raises problems: first, the robustness of their models is not guaranteed as they do not consider all attacks. Second, the comparison between two trust models depends on the choice of specific attacks, introducing bias. We propose to quantify the strength of attacks, and to quantify the robustness of trust systems based on the strength of the attacks it can resist.Our quantification is based on information theory, and provides designers of trust systems a fair measurement of the robustness.

In this thesis, we focus on automated advising agents. The advice given is a form of relating recommendations or guidance from an automated agent to its human user. Providing the right advice at the right time is extremely complex, and requires a good adaptation to human desires and changing environments. We propose a novel methodology for designing automated advising agents and evaluate it in three real world environments. Our intelligent advising agents were evaluated through extensive field trials, with hundreds of human subjects. A significant increase in human performance as well as a high level of user satisfaction was recorded when they were equipped with our agents.

In my doctoral dissertation I investigate patterns appearing in sentences referring to the future. Such patterns are useful in predicting future events. I base the study on a multiple newspaper corpora. I firstly perform a preliminary study to find out that the patterns appearing in future-reference sentences often consist of disjointed elements within a sentence. Such patterns are also usually semantically and grammatically consistent, although lexically variant. Therefore, I propose a method for automatic extraction of such patterns, applying both grammatical (morphological) and semantic information to represent sentences in morphosemantic structure, and then extract frequent patterns, including those with disjointed elements. Next, I perform a series of experiments, in which I firstly train fourteen classifier versions and compare them to choose the best one. Next, I compare my method to the state-of-the-art, and verify the final performance of the method on a new dataset. I conclude that the proposed method is capable to automatically classify future-reference sentences, significantly outperforming state-of-the-art, and reaching 76% of F-score.

Earth observation from space allows us to better understand natural phenomenas such as marine currents, to prevent or follow natural disasters, to follow climate evolution and many other things. To achieve that, there are a great number of artificial satellites orbiting Earth, equipped with high-resolution optical instruments and communicating with a network of ground stations. A satellite is said to be agile when it is able to move quickly around its gravity center along its three axes while moving along its orbit, thanks to gyroscopic actuators. It is equipped with a body-mounted optical instrument. To observe a ground area with the instrument, the satellite must be pointed to it. In practice, users submit observation requests to a mission center, which builds activity plans which are sent to the satellites. These plans contain several types of actions such as orbital maneuvers, acquisition realisations and acquisition downloads towards ground stations. Many techniques are used to synthesize such activity plans. Until now, plans are computed offline on the ground and converted into telecommands that the satellite executes strictly, without any flexibility. However, the satellite evolves in a dynamic environment. Unexpected events occur, such as meteorological changes or new urgent observation requests, that the system must handle. Moreover, resource consumption is not always well known. Until now, to ensure that plans will be executable on board with these uncertainties, they are built with worst-case hypothesis on resources consumption. The objective of this work is to give more autonomy to the satellite without compromising the predictability that is needed for some activities. On the ground, we have high computing power and high uncertainty, while on board we have very low computing power and low uncertainty. The main idea is to share decision-making between ground and board to take advantage of the high computing power on the ground and of the low uncertainty on board. First we apply this idea to download scheduling which consists in scheduling file downloads during ground station visibility windows. Second, we apply this idea to observation planning.

Increasing complexity of technical systems requires a precise fault localization in order to reduce maintenance costs and system downtimes. Model-based diagnosis has been presented as a method to derive root causes for observed symptoms, utilizing a description of the system to be diagnosed. Practical applications of model-based diagnosis, however, are often prevented by the initial modeling task and computational complexity associated with diagnosis. In the proposed thesis, we investigate techniques addressing these issues. In particular, we utilize a mapping function which converts fault information available in practice into propositional horn logic sentences to be used in abductive model-based diagnosis. Further, we plan on devising algorithms which allow an efficient computation of explanations given the obtained models.