abstract: Knowledge Representation (KR) is one of the prominent approaches to Artificial Intelligence (AI) that is concerned with representing knowledge in a form that computer systems can utilize to solve complex problems. Answer Set Programming (ASP), based on the stable model semantics, is a widely-used KR framework that facilitates elegant and efficient representations for many problem domains that require complex reasoning. However, while ASP is effective on deterministic problem domains, it is not suitable for applications involving quantitative uncertainty, for example, those that require probabilistic reasoning. Furthermore, it is hard to utilize information that can be statistically induced from data with ASP problem modeling. This dissertation presents the language LP^MLN, which is a probabilistic extension of the stable model semantics with the concept of weighted rules, inspired by Markov Logic. An LP^MLN program defines a probability distribution over "soft" stable models, which may not satisfy all rules, but the more rules with the bigger weights they satisfy, the bigger their probabilities. LP^MLN takes advantage of both ASP and Markov Logic in a single framework, allowing representation of problems that require both logical and probabilistic reasoning in an intuitive and elaboration tolerant way. This dissertation establishes formal relations between LP^MLN and several other formalisms, discusses inference and weight learning algorithms under LP^MLN, and presents systems implementing the algorithms. LP^MLN systems can be used to compute other languages translatable into LP^MLN. The advantage of LP^MLN for probabilistic reasoning is illustrated by a probabilistic extension of the action language BC+, called pBC+, defined as a high-level notation of LP^MLN for describing transition systems. Various probabilistic reasoning about transition systems, especially probabilistic diagnosis, can be modeled in pBC+ and computed using LP^MLN systems. pBC+ is further extended with the notion of utility, through a decision-theoretic extension of LP^MLN, and related with Markov Decision Process (MDP) in terms of policy optimization problems. pBC+ can be used to represent (PO)MDP in a succinct and elaboration tolerant way, which enables planning with (PO)MDP algorithms in action domains whose description requires rich KR constructs, such as recursive definitions and indirect effects of actions. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2019

Many real-world problems involve data that both have complex structures and uncertainty. Statistical relational learning (SRL) is an emerging area of research that addresses the problem of learning from these noisy structured/relational data. Markov logic networks (MLNs), sets of weighted first-order logic formulae, are a simple but powerful SRL formalism that generalizes both first-order logic and Markov networks. MLNs have been successfully applied to a variety of real-world problems ranging from extraction knowledge from text to visual event recognition. Most of the existing learning algorithms for MLNs are in the generative setting: they try to learn a model that is equally capable of predicting the values of all variables given an arbitrary set of evidence; and they do not scale to problems with thousands of examples. However, many real-world problems in structured/relational data are discriminative--where the variables are divided into two disjoint sets input and output, and the goal is to correctly predict the values of the output variables given evidence data about the input variables. In addition, these problems usually involve data that have thousands of examples. Thus, it is important to develop new discriminative learning methods for MLNs that are more accurate and more scalable, which are the topics addressed in this thesis. First, we present a new method that discriminatively learns both the structure and parameters for a special class of MLNs where all the clauses are non-recursive ones. Non-recursive clauses arise in many learning problems in Inductive Logic Programming. To further improve the predictive accuracy, we propose a max-margin approach to learning weights for MLNs. Then, to address the issue of scalability, we present CDA, an online max-margin weight learning algorithm for MLNs. Ater [sic] that, we present OSL, the first algorithm that performs both online structure learning and parameter learning. Finally, we address an issue arising in applying MLNs to many real-world problems: learning in the presence of many hard constraints. Including hard constraints during training greatly increases the computational complexity of the learning problem. Thus, we propose a simple heuristic for selecting which hard constraints to include during training. Experimental results on several real-world problems show that the proposed methods are more accurate, more scalable (can handle problems with thousands of examples), or both more accurate and more scalable than existing learning methods for MLNs. / text

Real-world learning tasks present a range of issues for learning systems. Learning tasks can be complex and the training data noisy. When operating as part of a larger system, there may be limitations on available memory and computational resources. Learners may also be required to provide results from a stream. This thesis investigates the problem of incremental, relational learning from imperfect data with constrained time and memory resources. The learning process involves incremental update of a theory when an example is presented that contradicts the theory. Contradictions occur if there is an incorrect theory or noisy data. The learner cannot discriminate between the two possibilities, so both are considered and the better possibility used. Additionally, all changes to the theory must have support from multiple examples. These two principles allow learning from imperfect data. The Minimum Description Length principle is used for selection between possible worlds and determining appropriate levels of additional justification. A new encoding scheme allows the use of MDL within the framework of Inductive Logic Programming. Examples must be stored to provide additional justification for revisions without violating resource requirements. A new algorithm determines when to discard examples, minimising total usage while ensuring sufficient storage for justifications. Searching for revisions is the most computationally expensive part of the process, yet not all searches are successful. Another new algorithm uses a notion of theory stability as a guide to occasionally disallow entire searches to reduce overall time. The approach has been implemented as a learner called NILE. Empirical tests include two challenging domains where this type of learner acts as one component of a larger task. The first of these involves recognition of behavior activation conditions in another agent as part of an opponent modeling task. The second, more challenging task is learning to identify objects in visual images by recognising relationships between image features. These experiments highlight NILE'S strengths and limitations as well as providing new n domains for future work in ILP.

Un réseau logique de Markov est formé de clauses en logique du premier ordre auxquelles sont associés des poids. Cette thèse propose plusieurs méthodes pour l’apprentissage de la structure de réseaux logiques de Markov (MLN) à partir de données relationnelles. Ces méthodes sont de deux types, un premier groupe reposant sur les techniques de propositionnalisation et un second groupe reposant sur la notion de Graphe des Prédicats. L’idée sous-jacente aux méthodes à base de propositionnalisation consiste à construire un jeu de clauses candidates à partir de jeux de littéraux dépendants. Pour trouver de tels jeux, nous utilisons une méthode de propositionnalisation afin de reporter les informations relationnelles dans des tableaux booléens, qui serviront comme tables de contingence pour des test de dépendance. Nous avons proposé deux méthodes de propositionnalisation, pour lesquelles trois algorithmes ont été développés, qui couvrent les problèmes d’appprentissage génératif et discriminant. Nous avons ensuite défini le concept de Graphe des Prédicats qui synthétise les relations binaires entre les prédicats d’un domaine. Des clauses candidates peuvent être rapidement et facilement produites en suivant des chemins dans le graphe puis en les variabilisant. Nous avons développé deux algorithmes reposant sur les Graphes des Prédicats, qui couvrent les problèmes d’appprentissage génératif et discriminant. / A Markov Logic Network is composed of a set of weighted first-order logic formulas. In this dissertation we propose several methods to learn a MLN structure from a relational dataset. These methods are of two kinds: methods based on propositionalization and methods based on Graph of Predicates. The methods based on propositionalization are based on the idea of building a set of candidate clauses from sets of dependent variable literals. In order to find such sets of dependent variable literals, we use a propositionalization technique to transform relational information in the dataset into boolean tables, that are then provided as contingency tables for tests of dependence. Two propositionalization methods are proposed, from which three learners have been developed, that handle both generative and discriminative learning. We then introduce the concept of Graph of Predicates, which synthethises the binary relations between the predicates of a domain. Candidate clauses can be quickly and easily generated by simply finding paths in the graph and then variabilizing them. Based on this Graph, two learners have been developed, that handle both generative and discriminative learning.

abstract: With the rise of social media, hundreds of millions of people spend countless hours all over the globe on social media to connect, interact, share, and create user-generated data. This rich environment provides tremendous opportunities for many different players to easily and effectively reach out to people, interact with them, influence them, or get their opinions. There are two pieces of information that attract most attention on social media sites, including user preferences and interactions. Businesses and organizations use this information to better understand and therefore provide customized services to social media users. This data can be used for different purposes such as, targeted advertisement, product recommendation, or even opinion mining. Social media sites use this information to better serve their users. Despite the importance of personal information, in many cases people do not reveal this information to the public. Predicting the hidden or missing information is a common response to this challenge. In this thesis, we address the problem of predicting user attributes and future or missing links using an egocentric approach. The current research proposes novel concepts and approaches to better understand social media users in twofold including, a) their attributes, preferences, and interests, and b) their future or missing connections and interactions. More specifically, the contributions of this dissertation are (1) proposing a framework to study social media users through their attributes and link information, (2) proposing a scalable algorithm to predict user preferences; and (3) proposing a novel approach to predict attributes and links with limited information. The proposed algorithms use an egocentric approach to improve the state of the art algorithms in two directions. First by improving the prediction accuracy, and second, by increasing the scalability of the algorithms. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2014

Mobila enheter är idag en stor del av våra liv och har förändrat hur vi gör många av våra vardagsaktiviteter, såsom att läsa en bok eller att se sitt favoritprogram. Detta har också lett till förändringar i våra utbildningar eller mer specifikt hur vi lär oss och lär ut till andra [1]. Den snabba tillväxten av teknologi har haft en stor påverkan på vår utbildning, vilket har lett till en anpassning av både strukturen av utbildningar och dess material [1, 2]. Teknologins utveckling har också möjliggjort den att kombineras med inlärningstekniker, som kollaborativ inlärning. Kollaborativ inlärning är baserat på konceptet att det naturliga sättet att lära sig är genom att kommunicera med varandra [4]. Kollaborativ inlärning är sammankopplat med de sociala aspekterna, där den stora uppgången av sociala plattformar har visat att de kan vara ett nytt och mer modernt utbildningsområde. Interaktionerna på dessa plattformarna har visat sig kunna skapa ett nytt digitalt område av kunskap [29].I denna uppsats så fortsatte utvecklingen av en Android-applikation kallad Vault. Vault är byggd på filosofierna mobile learning, relational learning och parkour. Mobile learning tillåter människor att lära sig var de än befinner sig [3]. I relational learning så existerar inte den traditionella student-lärarrelationen, där alla istället lär sig av varandra genom att dela med sig av sina idéer och erfarenheter [6]. Parkour, vilket även är känt som konsten av rörelse, är en filosofi där människor delar med sig av upplevelser medans de lär sig och hittar nya vägar att visa sina färdigheter [28]. Parkour har även ett fokus på att upprepa vad andra har gjort. Funktionerna som implementerades i Vault var baserade på en applikationsanalys där sociala och kollaborativa plattformar blev analyserade. Det undersöktes sedan hur dessa funktionerna kunde integreras med filosofierna som Vault är byggd på.Vault testades sedan av två idrottslärare under 10 dagar för att utvärdera applikationen med dess nya funktioner där de sedan intervjuades. Målet med intervjuerna var att få information om hur de nya funktionerna påverkade inlärningen samt användarupplevelsen jämfört med den gamla versionen som var producerad av Lindermans uppsats [5]. / Mobile devices is today a big part of our lives and it has changed how we do our everyday activities, such as reading a book or watching our favorite show. This has also caused changes in our education or more specifically how we learn and teach others [1]. The fast growth of technology has had a big effect on our education, which has led to the adaption of both the structure of education as well as the educational materials [1,2]. The development of technology has also allowed for it to combine with learning techniques, such as collaborative learning. Collaborative learning is based on the concept that the natural way to learn is by communicating with each other [4]. Collaborative learning is connected to the social aspects, where the big rise of social platforms have shown that they can be a new and more present-day education area. The interactions on these platforms have proved to be able to help create a new digital area of knowledge [29].In this thesis there was a continuation of development of the Android application Vault. Vault is built upon the philosophies of mobile learning, relational learning and parkour. Mobile learning allows for people to learn at any time wherever they are [3]. In relational learning the traditional student and teacher relationship doesn’t exist, where instead everyone learns from each other by sharing ideas and experiences [6]. Parkour, also known as the art of movement [27], is a philosophy where people share experiences as they learn and find different paths to display their skills [28]. Parkour also have a focus on repeating what other people have done. The features implemented in the further development of Vault was decided by an application analysis where social and collaborative platforms were analysed. It was then examined how these features could be integrated with the philosophies that Vault was built upon. Vault was later tested on two physical education teachers which both had ten days to test out the application with its new features after which a interview followed. The goal of the interviews were to get information about how the new features affected the learning and user experience compared to the old version produced by Linderman’s thesis [5].

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第18613号 / 情博第537号 / 新制||情||95(附属図書館) / 31513 / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 山本 章博, 教授 黒橋 禎夫, 教授 阿久津 達也 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

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This dissertation addresses a fundamental question of multi-agent coordination: what infor- mation should be sent to whom and when, with the limited resources available to each agent? Communication requirements for multi-agent systems can be rather high when an accurate picture of the environment and the state of other agents must be maintained. To reduce the impact of multi-agent coordination on networked systems, e.g., power and bandwidth, this dissertation introduces new concepts to enable Intelligent Knowledge Distribution (IKD), including Constrained-action POMDPs (CA-POMDP) and concurrent decentralized (CoDec) POMDPs for an agnostic plug-and-play capability for fully autonomous systems. Each agent runs a CoDec POMDP where all the decision making (motion planning, task allocation, asset monitoring, and communication) are separated into concurrent individual MDPs to reduce the combinatorial explosion of the action and state space while maintaining dependencies between the models. We also introduce the CA-POMDP with action-based constraints on partially observable Markov decision processes, rewards driven by the value of information, and probabilistic constraint satisfaction through discrete optimization and Markov chain Monte Carlo analysis. IKD is adapted real-time through machine learning of the actual environmental impacts on the behavior of the system, including collaboration strategies between autonomous agents, the true value of information between heterogeneous systems, observation probabilities and resource utilization. / Doctor of Philosophy / This dissertation addresses a fundamental question behind when multiple autonomous sys- tems, like drone swarms, in the field need to coordinate and share data: what information should be sent to whom and when, with the limited resources available to each agent? Intelligent Knowledge Distribution is a framework that answers these questions. Communication requirements for multi-agent systems can be rather high when an accurate picture of the environment and the state of other agents must be maintained. To reduce the impact of multi-agent coordination on networked systems, e.g., power and bandwidth, this dissertation introduces new concepts to enable Intelligent Knowledge Distribution (IKD), including Constrained-action POMDPs and concurrent decentralized (CoDec) POMDPs for an agnostic plug-and-play capability for fully autonomous systems. The IKD model was able to demonstrate its validity as a "plug-and-play" library that manages communications between agents that ensures the right information is being transmitted at the right time to the right agent to ensure mission success.

Musik finns i alla kulturer och berör oss alla på olika sätt. Syftet med denna uppsats är att söka en djupare förståelse för vad en musikgenre kan betyda för elever. Studien går ut på att undersöka hur elever använder musiken för att klara av sin vardag både vad gäller skola och fritid. Det har i utredningar visats att musik är viktig för inlärning av kärnämnen. I mitt arbete som musiklärare har jag fått höra från elever att de inte skulle komma till skolan om det inte var för musikämnena. Det är musiken som gör skolan intressant och som gör att de orkar delta även i andra ämnen. Läraren är viktig för elevernas utbildning. En modern lärare använder sig av relationellt lärande. Metoden går ut på att läraren skapar en relation där eleven ges förtroende och stort utrymme i undervisningen. I relationen måste läraren släppa på sin auktoritet och en kamp kan då uppstå mellan lärare och elev, vilket kräver både kunskap och mod av läraren. / Music exists in all cultures and affects us all in different ways. The purpose of this study is to seek a deeper understanding of what a genre of music can mean to students. The study is to investigate how students use music to cope with their daily lives both in school and leisure. Investigations have shown that music is important to the learning of main subjects. In my job as a music teacher I have learnt from students that they would not come to school if it was not for the music subjects. It's the music that makes the school attractive and enables them to have the energy to participate in other subjects. The teacher is important for the students' education. A modern teacher uses ”relational learning”. The approach is that the teacher creates a relationship where students are given freedom and space in class. In the relationship with the student the teacher must let go of his authority and a struggle can then occur between teacher and student, which requires both the knowledge and the courage of the teacher.