Recent research in the field of Description Logic (DL) investigated the complexity of the satisfiability problem for description logics that are obtained by enriching the well-known DL ALCQ with more complex set and cardinality constraints over role successors. The algorithms that have been proposed so far, despite providing worst-case optimal decision procedures for the concept satisfiability problem (both without and with a terminology) lack the efficiency needed to obtain usable implementations. In particular, the algorithm for the case without terminology is non-deterministic and the one for the case with a terminology is also best-case exponential. The goal of this thesis is to use well-established techniques from the field of numerical optimization, such as column generation, in order to obtain more practical algorithms. As a starting point, efficient approaches for dealing with counting quantifiers over unary predicates based on SAT-based column generation should be considered.:1. Introduction 2. Preliminaries 2.1. First-order logic 2.2. Linear Programming 2.3. The description logic ALCQ 2.4. Extending ALCQ with expressive role successor constraints 2.4.1. The logic QFBAPA 2.4.2 The description logic ALCSCC 3. The description logic ALCCQU 3.1. A normal form for ALCCQU 3.2. ALCQt as an equivalent formulation of ALCCQU 3.2.1. ALCQt is a sublogic of ALCCQU 3.2.2. ALCCQU is a sublogic of ALCQt 3.3. Model-theoretic characterization of ALCQt 3.3.1. ALCQt-bisimulation and invariance for ALCQt 3.3.2. Characterization of ALCQt concept descriptions 3.4. Expressive power 3.4.1. Relative expressivity of ALCQ and ALCCQU 3.4.2. Relative expressivity of ALCCQU and ALCSCC 3.5. ALCCQU as the first-order fragment of ALCSCC 4. Concept satisfiability in ALCCQU 4.1. The first-order fragment CQU 4.2. Column generation with SAT oracle 4.2.1. Column generation and CQU 4.2.2. From linear inequalities to propositional formulae 4.2.3. Column generation and ALCCQU 4.3. Branch-and-Price for ALCCQU concept satisfiability 4.4. Correctness of ALCCQU-BB 4.4.1. Complexity of ALCCQU-BB 5. Conclusion - Bibliography

Yes / This paper presents a critical evaluation framework for a linguistically orientated conversational software agent (CSA) (Panesar, 2017). The CSA prototype investigates the integration, intersection and interface of the language, knowledge, and speech act constructions (SAC) based on a grammatical object (Nolan, 2014), and the sub-­‐model of belief, desires and intention (BDI) (Rao and Georgeff, 1995) and dialogue management (DM) for natural language processing (NLP). A long-­‐standing issue within NLP CSA systems is refining the accuracy of interpretation to provide realistic dialogue to support the human-­‐to-­‐computer communication. This prototype constitutes three phase models: (1) a linguistic model based on a functional linguistic theory – Role and Reference Grammar (RRG) (Van Valin Jr, 2005); (2) Agent Cognitive Model with two inner models: (a) knowledge representation model employing conceptual graphs serialised to Resource Description Framework (RDF); (b) a planning model underpinned by BDI concepts (Wooldridge, 2013) and intentionality (Searle, 1983) and rational interaction (Cohen and Levesque, 1990); and (3) a dialogue model employing common ground (Stalnaker, 2002). The evaluation approach for this Java-­‐based prototype and its phase models is a multi-­‐approach driven by grammatical testing (English language utterances), software engineering and agent practice. A set of evaluation criteria are grouped per phase model, and the testing framework aims to test the interface, intersection and integration of all phase models and their inner models. This multi-­‐approach encompasses checking performance both at internal processing, stages per model and post-­‐implementation assessments of the goals of RRG, and RRG based specifics tests. The empirical evaluations demonstrate that the CSA is a proof-­‐of-­‐concept, demonstrating RRG’s fitness for purpose for describing, and explaining phenomena, language processing and knowledge, and computational adequacy. Contrastingly, evaluations identify the complexity of lower level computational mappings of NL – agent to ontology with semantic gaps, and further addressed by a lexical bridging consideration (Panesar, 2017).

<p>Graphs are the natural framework of many of today’s highest impact computing applications: from online social networking, to Web search, to product recommendations, to chemistry, to bioinformatics, to knowledge bases, to mobile ad-hoc networking. To develop successful applications in these domains, we often need representation learning methods ---models mapping nodes, edges, subgraphs or entire graphs to some meaningful vector space. Such models are studied in the machine learning subfield of graph representation learning (GRL). Previous GRL research has focused on learning node or entire graph representations through associational tasks. In this work I study higher-order (k>1-node) representations of graphs in the context of both associational and counterfactual tasks.<br> </p>

yes / This paper presents a critical evaluation framework for a linguistically motivated conversational software agent (CSA). The CSA prototype investigates the integration, intersection and interface of the language, knowledge, and speech act constructions (SAC) based on a grammatical object, and the sub-model of belief, desires and intention (BDI) and dialogue management (DM) for natural language processing (NLP). A long-standing issue within NLP CSA systems is refining the accuracy of interpretation to provide realistic dialogue to support human-to-computer communication. This prototype constitutes three phase models: (1) a linguistic model based on a functional linguistic theory – Role and Reference Grammar (RRG), (2) an Agent Cognitive Model with two inner models: (a) a knowledge representation model, (b) a planning model underpinned by BDI concepts, intentionality and rational interaction, and (3) a dialogue model. The evaluation strategy for this Java-based prototype is multi-approach driven by grammatical testing (English language utterances), software engineering and agent practice. A set of evaluation criteria are grouped per phase model, and the testing framework aims to test the interface, intersection and integration of all phase models. The empirical evaluations demonstrate that the CSA is a proof-of-concept, demonstrating RRG’s fitness for purpose for describing, and explaining phenomena, language processing and knowledge, and computational adequacy. Contrastingly, evaluations identify the complexity of lower level computational mappings of NL – agent to ontology with semantic gaps, and further addressed by a lexical bridging solution.

The role-based paradigm has been introduced for the design of adaptive and context sensitive software systems. Naturally, a system built on top of the paradigm is expected to thrive in dynamic environments. In consequence, reasoning services over temporal aspect are essential in such a system. To represent context-dependent domains, various extensions of Description Logics (DLs) with context are introduced and studied. We focus on the family of Contextualized Description Logics (ConDLs) that have been shown capable to represent role-based modelling languages while retaining decidability. However, reasoning problems over dynamic settings under the logics are rather unexplored.

The structure of education has changed dramatically in the last few decades. Despite major changes in how students are learning, there has not been as dramatic of a shift in how student learning is assessed. Standard letter grades are still the paradigm for evaluating a student's mastery of course content and the grade point average is still one of the largest determining factors in judging a graduate's academic aptitude. This research presents a modern approach to modeling knowledge and evaluating students. Based upon the model of a closed-loop feedback controller it considers education as a system with an instructor determining the set of knowledge he or she wishes to impart to students, the instruction method as a transfer function, and evaluation methods serving as sensors to provide feedback determining the subset of the information students have learned. This method uses comprehensive concept maps to depict all of the concepts and relationships an educator intends to cover and student maps to depict the subset of knowledge that students have mastered. Concept inventories are used as an assessment tool to determine, at the conceptual level, what students have learned. Each question in the concept inventory is coupled with one or more components of a comprehensive concept map and based upon the answers students give to concept inventory questions those components may or may not appear in a student's knowledge map. The level of knowledge a student demonstrates of each concept and relationship is presented in his or her student map using a color scheme tied to the levels of learning in Bloom's taxonomy. Topological principles are used to establish metrics to quantify the distance between two students' knowledge maps and the distance between a student's knowledge map and the corresponding comprehensive concept map. A method is also developed for forming aggregate maps representative of the knowledge of a group of students. Aggregate maps can be formed for entire classes of students or based upon various demographics including race and gender. XML schemas have been used throughout this research to encapsulate the information in both comprehensive maps and student maps and to store correlations between concept inventory questions and corresponding comprehensive map components. Three software packages have been developed to store concept inventories into an XML Schema, to process student responses to concept inventory questions and generate student maps as a result, and to generate aggregate maps. The methods presented herein have been applied to two learning units that are part of two freshman engineering courses at Virginia Tech. Example student maps and aggregate maps are included for these course units. / Ph. D.

Yes / This chapter discusses a linguistically orientated model of a conversational software agent (CSA) (Panesar 2017) framework sensitive to natural language processing (NLP) concepts and the levels of adequacy of a functional linguistic theory (LT). We discuss the relationship between NLP and knowledge representation (KR), and connect this with the goals of a linguistic theory (Van Valin and LaPolla 1997), in particular Role and Reference Grammar (RRG) (Van Valin Jr 2005). We debate the advantages of RRG and consider its fitness and computational adequacy. We present a design of a computational model of the linking algorithm that utilises a speech act construction as a grammatical object (Nolan 2014a, Nolan 2014b) and the sub-model of belief, desire and intentions (BDI) (Rao and Georgeff 1995). This model has been successfully implemented in software, using the resource description framework (RDF), and we highlight some implementation issues that arose at the interface between language and knowledge representation (Panesar 2017).

Le cancer du sein est le cancer le plus répandu chez les femmes de plus de 40 ans. En effet, des études ont montré qu'une détection précoce et un traitement approprié du cancer du sein augmentent de manière significative les chances de survie. La mammographie constitue le moyen d'investigation le plus utilisé dans le diagnostic des lésions mammaires. Cependant, cette technique peut être insuffisante pour montrer les structures du sein et faire apparaître les anomalies présentes et le médecin peut faire appel à d'autres modalités d'imagerie telle que l'imagerie IRM. Ces modalités sont généralement complémentaires. Par conséquent, le médecin procède à une fusion mentale des différentes informations sur les deux images dans le but d'effectuer le diagnostic adéquat. Pour assister le médecin et l'aider dans ce processus, nous proposons une solution permettant de fusionner les deux images. Bien que l'idée de la fusion paraisse simple, sa mise en oeuvre pose de nombreux problèmes liés non seulement au problème de fusion en général mais aussi à la nature des images médicales qui sont généralement des images mal contrastées et présentant des données hétérogènes, imprécises et ambigües. Notons que les images mammographiques et les images IRM présentent des représentations très différentes des informations, étant donnée qu'elles sont prises dans des conditions distinctes. Ce qui nous amène à poser la question suivante: Comment passer de la représentation hétérogène des informations dans l'espace image, à un autre espace de représentation uniforme. Afin de traiter cette problématique, nous optons pour une approche de traitement multi-niveaux : niveau pixel, niveau primitives, niveau objet et niveau scène. Nous modélisons les objets pathologiques extraits des différentes images par des ontologies locales. La fusion est ensuite effectuée sur ces ontologies locales et résulte en une ontologie globale contenant les différentes connaissances sur les objets pathologiques du cas étudié. Cette ontologie globale sert à instancier une ontologie de référence modélisant les connaissances du diagnostic médical des lésions mammaires. Un raisonnement à base de cas est exploité pour fournir les rapports diagnostic des cas les plus similaires pouvant aider le médecin à prendre la meilleure décision. Dans le but de modéliser l'imperfection des informations traitées, nous utilisons la théorie des possibilités avec les différentes ontologies. Le résultat fourni est présenté sous forme de rapports diagnostic comportant les cas les plus similaires au cas étudié avec des degrés de similarité exprimés en mesures de possibilité. Un modèle virtuel 3D complète le rapport diagnostic par un aperçu simplifié de la scène étudiée. / The breast cancer is the most prevalent cancer among women over 40 years old. Indeed, studies evinced that an early detection and an appropriate treatment of breast cancer increases significantly the chances of survival. The mammography is the most tool used in the diagnosis of breast lesions. However, this technique may be insufficient to evince the structures of the breast and reveal the anomalies present. The doctor can use additional imaging modalities such as MRI (Magnetic Reasoning Image). Therefore, the doctor proceeds to a mental fusion of the different information on the two images in order to make the adequate diagnosis. To assist the doctor in this process, we propose a solution to merge the two images. Although the idea of the fusion seems simple, its implementation poses many problems not only related to the paradigm of fusion in general but also to the nature of medical images that are generally poorly contrasted images, and presenting heterogeneous, inaccurate and ambiguous data. Mammography images and IRM images present very different information representations, since they are taken under different conditions. Which leads us to pose the following question: How to pass from the heterogeneous representation of information in the image space, to another space of uniform representation from the two modalities? In order to treat this problem, we opt a multilevel processing approach : the pixel level, the primitive level, the object level and the scene level. We model the pathological objects extracted from the different images by local ontologies. The fusion is then performed on these local ontologies and results in a global ontology containing the different knowledge on the pathological objects of the studied case. This global ontology serves to instantiate a reference ontology modeling knowledge of the medical diagnosis of breast lesions. Case-based reasoning (CBR) is used to provide the diagnostic reports of the most similar cases that can help the doctor to make the best decision. In order to model the imperfection of the treated information, we use the possibility theory with the ontologies. The final result is a diagnostic reports containing the most similar cases to the studied case with similarity degrees expressed with possibility measures. A 3D symbolic model complete the diagnostic report with a simplified overview of the studied scene.

<p>Detecting crop rows from video frames in real time is a fundamental challenge in the field of precision agriculture. Deep learning based semantic segmentation method, namely U-net, although successful in many tasks related to precision agriculture, performs poorly for solving this task. The reasons include paucity of large scale labeled datasets in this domain, diversity in crops, and the diversity of appearance of the same crops at various stages of their growth. In this work, we discuss the development of a practical real-life crop row</p> <p>detection system in collaboration with an agricultural sprayer company. Our proposed method takes the output of semantic segmentation using U-net, and then apply a clustering based probabilistic temporal calibration which can adapt to different fields and crops without the need for retraining the network. Experimental results validate that our method can be used for both refining the results of the U-net to reduce errors and also for frame interpolation of the input video stream. Upon the availability of more labeled data, we switched our approach from a semi-supervised model to a fully supervised end-to-end crop row detection model using a Feature Pyramid Network or FPN. Central to the FPN is a pyramid pooling module that extracts features from the input image at multiple resolutions. This results in the network’s ability to use both local and global features in classifying pixels to be crop rows. After training the FPN on the labeled dataset, our method obtained a mean IoU or Jaccard Index score of over 70% as reported on the test set. We trained our method on only a subset of the corn dataset and tested its performance on multiple variations of weed pressure and crop growth stages to verify that the performance does translate over the variations and is consistent across the entire dataset.</p>

In my Ph.D. dissertation, I have studied problems arising in various aspects of preferences: preference modeling, preference learning, and preference reasoning, when preferences concern outcomes ranging over combinatorial domains. Preferences is a major research component in artificial intelligence (AI) and decision theory, and is closely related to the social choice theory considered by economists and political scientists. In my dissertation, I have exploited emerging connections between preferences in AI and social choice theory. Most of my research is on qualitative preference representations that extend and combine existing formalisms such as conditional preference nets, lexicographic preference trees, answer-set optimization programs, possibilistic logic, and conditional preference networks; on learning problems that aim at discovering qualitative preference models and predictive preference information from practical data; and on preference reasoning problems centered around qualitative preference optimization and aggregation methods. Applications of my research include recommender systems, decision support tools, multi-agent systems, and Internet trading and marketing platforms.