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.

We research and develop autonomous mobile service robots as Collaborative Robots, i.e., CoBots. For the last three years, our four CoBots have autonomously navigated in our multi-floor office buildings for more than 1,000km, as the result of the integration of multiple perceptual, cognitive, and actuations representations and algorithms. In this paper, we identify a few core aspects of our CoBots underlying their robust functionality. The reliable mobility in the varying indoor environments comes from a novel episodic non-Markov localization. Service tasks requested by users are the input to a scheduler that can consider different types of constraints, including transfers among multiple robots. With symbiotic autonomy, the CoBots proactively seek external sources of help to fill-in for their inevitable occasional limitations. We present sampled results from a deployment and conclude with a brief review of other features of our service robots.

Rosetta is a European Space Agency (ESA) cornerstone mission that entered orbit around the comet 67P/Churyumov-Gerasimenko in August 2014 and will escort the comet for a 1.5 year nominal mission offering the most detailed study of a comet ever undertaken by humankind. The Rosetta orbiter has 11 scientific instruments (4 remote sensing) and the Philae lander to make complementary measurements of the comet nucleus, coma (gas and dust), and surrounding environment. The ESA Rosetta Science Ground Segment has developed a science scheduling system that includes an automated scheduling capability to assist in developing science plans for the Rosetta Orbiter. While automated scheduling is a small portion of the overall Science Ground Segment (SGS) as well as the overall scheduling system, this paper focuses on the automated and semi-automated scheduling software (called ASPEN-RSSC) and how this software is used.

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.

I study the problems of optimizing a range of stochastic processes occurring in networks, such as the information spreading process in a social network, species migration processes in landscape network, virus spreading process in human contact network. The standard network design frameworks, such as Steiner tree problem and survival network design problem, fail to capture certain properties of these problems. To solve the problems, the existing techniques, such as standard mixed integer program solver, greedy algorithms or heuristic based methods, also suffer from limited scalability or poor performance. My thesis contributes to both modeling and algorithm development. My first goal is to define a unifying network design framework called stochastic network design (SND) to model a broad class of network design problems under stochasticity. My second goal, which is my major focus, is to design effective and scalable general-purpose approximate algorithms to solve problems that can be formulated by the SND framework.

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.

An Enterprise Architecture (EA) provides a holistic view of an enterprise. In creating or changing an EA, multiple decisions have to be made, which are based on assumptions about the situation at hand. In this thesis, we develop a framework for reasoning about changing decisions and assumptions, based on logical theories of intentions. This framework serves as the underlying formalism for a recommender system for EA decision making.

Feature Selection plays an important role in machine learning and data mining, and it is often applied as a data pre-processing step. This task can speed up learning algorithms and sometimes improve their performance. In multi-label learning, label dependence is considered another aspect that can contribute to improve learning performance. A replicable and wide systematic review performed by us corroborates this idea. Based on this information, it is believed that considering label dependence during feature selection can lead to better learning performance. The hypothesis of this work is that multi-label feature selection algorithms that consider label dependence will perform better than the ones that disregard it. To this end, we propose multi-label feature selection algorithms that take into account label relations. These algorithms were experimentally compared to the standard approach for feature selection, showing good performance in terms of feature reduction and predictability of the classifiers built using the selected features.