LS-DTKMS: A Local Search Algorithm for Diversified Top-k MaxSAT Problem

The Maximum Satisfiability (MaxSAT), an important optimization problem, has a range of applications, including network routing, planning and scheduling, and combinatorial auctions. Among these applications, one usually benefits from having not just one single solution, but k diverse solutions. Motivated by this, we study an extension of MaxSAT, named Diversified Top-k MaxSAT (DTKMS) problem, which is to find k feasible assignments of a given formula such that each assignment satisfies all hard clauses and all of them together satisfy the maximum number of soft clauses. This paper presents a local search algorithm, LS-DTKMS, for DTKMS problem, which exploits novel scoring functions to select variables and assignments. Experiments demonstrate that LS-DTKMS outperforms the top-k MaxSAT based DTKMS solvers and state-of-the-art solvers for diversified top-k clique problem

Partitionnement d’instances de processus basé sur les techniques de conformité de modèles

As event data becomes an ubiquitous source of information, data science techniques represent an unprecedented opportunity to analyze and react to the processes that generate this data. Process Mining is an emerging field that bridges the gap between traditional data analysis techniques, like Data Mining, and Business Process Management. One core value of Process Mining is the discovery of formal process models like Petri nets or BPMN models which attempt to make sense of the events recorded in logs. Due to the complexity of event data, automated process discovery algorithms tend to create dense process models which are hard to interpret by humans. Fortunately, Conformance Checking, a sub-field of Process Mining, enables relating observed and modeled behavior, so that humans can map these two pieces of process information. Conformance checking is possible through alignment artefacts, which associate process models and event logs. Different types of alignment artefacts exist, namely alignments, multi-alignments and anti-alignments. Currently, only alignment artefacts are deeply addressed in the literature. It allows to relate the process model to a given process instance. However, because many behaviors exist in logs, identifying an alignment per process instance hinders the readability of the log-to-model relationships.The present thesis proposes to exploit the conformance checking artefacts for clustering the process executions recorded in event logs, thereby extracting a restrictive number of modeled representatives. Data clustering is a common method for extracting information from dense and complex data. By grouping objects by similarities into clusters, data clustering enables to mine simpler datasets which embrace the similarities and the differences contained in data. Using the conformance checking artefacts in a clustering approach allows to consider a reliable process model as a baseline for grouping the process instances. Hence, the discovered clusters are associated with modeled artefacts, that we call model-based trace variants, which provides opportune log-to-model explanations.From this motivation, we have elaborated a set of methods for computing conformance checking artefacts. The first contribution is the computation of a unique modeled behavior that represents of a set of process instances, namely multi-alignment. Then, we propose several alignment-based clustering approaches which provide clusters of process instances associated to a modeled artefact. Finally, we highlight the interest of anti-alignment for extracting deviations of process models with respect to the log. This latter artefact enables to estimate model precision, and we show its impact in model-based clustering. We provide SAT encoding for all the proposed techniques. Heuristic algorithms are then added to deal with computing capacity of today’s computers, at the expense of loosing optimality.Les données d'événements devenant une source d'information omniprésente, les techniques d'analyse de données représentent une opportunité sans précédent pour étudier et réagir aux processus qui génèrent ces données. Le Process Mining est un domaine émergent qui comble le fossé entre les techniques d'analyse de données, comme le Data Mining, et les techniques de management des entreprises, à savoir, le Business Process Management. L'une des bases fondamentales du Process Mining est la découverte de modèles de processus formels tels que les réseaux de Petri ou les modèles BPMN qui tentent de donner un sens aux événements enregistrés dans les journaux. En raison de la complexité des données d'événements, les algorithmes de découverte de processus ont tendance à créer des modèles de processus denses, qui sont difficiles à interpréter par les humains. Heureusement, la Vérification de Conformité, un sous-domaine du Process Mining, permet d'établir des liens entre le comportement observé et le comportement modélisé, facilitant ainsi la compréhension des correspondance entre ces deux éléments d'information sur les processus. La Vérification de Conformité est possible grâce aux artefacts d'alignement, qui associent les modèles de processus et les journaux d'événements. Il existe différents types d'artefacts d'alignement, à savoir les alignements, les multi-alignements et les anti-alignements. Actuellement, seuls les alignements sont traités en profondeur dans la littérature scientifique. Un alignement permet de relier le modèle de processus à une instance de processus donnée. Cependant, étant donné que de nombreux comportements existent dans les logs, l'identification d'un alignement par instance de processus nuit à la lisibilité des relations log-modèle.La présente thèse propose d'exploiter les artefacts de conformité pour regrouper les exécutions de processus enregistrées dans les journaux d'événements, et ainsi extraire un nombre restrictif de représentations modélisées. Le regroupement de données, communément appelé partitionnement, est une méthode courante pour extraire l'information de données denses et complexes. En regroupant les objets par similarité dans des clusters, le partitionnement permet d'extraire des ensembles de données plus simples qui englobent les similarités et les différences contenues dans les données. L'utilisation des artefacts de conformité dans une approche de partitionnement permet de considérer un modèle de processus fiable comme une base de référence pour le regroupement des instances de processus. Ainsi, les clusters découverts sont associés à des artefacts modélisés, que nous appelons variantes modélisées des traces, ce qui fournit des explications opportunes sur les relations entre le journal et le modèle.Avec cette motivation, nous avons élaboré un ensemble de méthodes pour calculer les artefacts de conformité. La première contribution est le calcul d'un comportement modélisé unique qui représente un ensemble d'instances de processus, à savoir le multi-alignement. Ensuite, nous proposons plusieurs approches de partitionnement basées sur l'alignement qui fournissent des clusters d'instances de processus associés à un artefact modélisé. Enfin, nous soulignons l'intérêt de l'anti-alignement pour extraire les déviations des modèles de processus par rapport au journal. Ce dernier artefact permet d'estimer la précision du modèle. Nous montrons son impact sur nos approches de partitionnement basées sur des modèles. Nous fournissons un encodage SAT pour toutes les techniques proposées. Des heuristiques sont ensuite ajoutées pour tenir compte de la capacité de calcul des ordinateurs actuels, au prix d'une perte d'optimalité

Frameworks for logically classifying polynomial-time optimisation problems.

We show that a logical framework, based around a fragment of existential second-order logic formerly proposed by others so as to capture the class of polynomially-bounded P-optimisation problems, cannot hope to do so, under the assumption that P ≠ NP. We do this by exhibiting polynomially-bounded maximisation and minimisation problems that can be expressed in the framework but whose decision versions are NP-complete. We propose an alternative logical framework, based around inflationary fixed-point logic, and show that we can capture the above classes of optimisation problems. We use the inductive depth of an inflationary fixed-point as a means to describe the objective functions of the instances of our optimisation problems

Structure discovery techniques for circuit design and process model visualization

Graphs are one of the most used abstractions in many knowledge fields because of the easy and flexibility by which graphs can represent relationships between objects. The pervasiveness of graphs in many disciplines means that huge amounts of data are available in graph form, allowing many opportunities for the extraction of useful structure from these graphs in order to produce insight into the data. In this thesis we introduce a series of techniques to resolve well-known challenges in the areas of digital circuit design and process mining. The underlying idea that ties all the approaches together is discovering structures in graphs. We show how many problems of practical importance in these areas can be solved utilizing both common and novel structure mining approaches. In the area of digital circuit design, this thesis proposes automatically discovering frequent, repetitive structures in a circuit netlist in order to improve the quality of physical planning. These structures can be used during floorplanning to produce regular designs, which are known to be highly efficient and economical. At the same time, detecting these repeating structures can exponentially reduce the total design time. The second focus of this thesis is in the area of the visualization of process models. Process mining is a recent area of research which centers on studying the behavior of real-life systems and their interactions with the environment. Complicated process models, however, hamper this goal. By discovering the important structures in these models, we propose a series of methods that can derive visualization-friendly process models with minimal loss in accuracy. In addition, and combining the areas of circuit design and process mining, this thesis opens the area of specification mining in asynchronous circuits. Instead of the usual design flow, which involves synthesizing circuits from specifications, our proposal discovers specifications from implemented circuits. This area allows for many opportunities for verification and re-synthesis of asynchronous circuits. The proposed methods have been tested using real-life benchmarks, and the quality of the results compared to the state-of-the-art.Els grafs són una de les representacions abstractes més comuns en molts camps de recerca, gràcies a la facilitat i flexibilitat amb la que poden representar relacions entre objectes. Aquesta popularitat fa que una gran quantitat de dades es puguin trobar en forma de graf, i obre moltes oportunitats per a extreure estructures d'aquest grafs, útils per tal de donar una intuïció millor de les dades subjacents. En aquesta tesi introduïm una sèrie de tècniques per resoldre reptes habitualment trobats en les àrees de disseny de circuits digitals i mineria de processos industrials. La idea comú sota tots els mètodes proposats es descobrir automàticament estructures en grafs. En la tesi es mostra que molts problemes trobats a la pràctica en aquestes àrees poden ser resolts utilitzant nous mètodes de descobriment d'estructures. En l'àrea de disseny de circuits, proposem descobrir, automàticament, estructures freqüents i repetitives en les definicions del circuit per tal de millorar la qualitat de les etapes posteriors de planificació física. Les estructures descobertes poden fer-se servir durant la planificació per produir dissenys regulars, que son molt més econòmics d'implementar. Al mateix temps, la descoberta i ús d'aquestes estructures pot reduir exponencialment el temps total de disseny. El segon punt focal d'aquesta tesi és en l'àrea de la visualització de models de processos industrials. La mineria de processos industrials es un tema jove de recerca que es centra en estudiar el comportament de sistemes reals i les interaccions d'aquests sistemes amb l'entorn. No obstant, quan d'aquest anàlisi s'obtenen models massa complexos visualment, l'estudi n'és problemàtic. Proposem una sèrie de mètodes que, gràcies al descobriment automàtic de les estructures més importants, poden generar models molt més fàcils de visualitzar que encara descriuen el comportament del sistema amb gran precisió. Combinant les àrees de disseny de circuits i mineria de processos, aquesta tesi també obre un nou tema de recerca: la mineria d'especificacions per circuits asíncrons. En l'estil de disseny asíncron habitual, sintetitzadors automàtics generen circuits a partir de les especificacions. En aquesta tesi proposem el pas invers: descobrir automàticament les especificacions de circuits ja implementats. Així, creem noves oportunitats per a la verificació i la re-síntesi de circuits asíncrons. Els mètodes proposats en aquesta tesi s'han validat fent servir dades obtingudes d'aplicacions pràctiques, i en comparem els resultats amb els mètodes existents

Improving Model Finding for Integrated Quantitative-qualitative Spatial Reasoning With First-order Logic Ontologies

Many spatial standards are developed to harmonize the semantics and specifications of GIS data and for sophisticated reasoning. All these standards include some types of simple and complex geometric features, and some of them incorporate simple mereotopological relations. But the relations as used in these standards, only allow the extraction of qualitative information from geometric data and lack formal semantics that link geometric representations with mereotopological or other qualitative relations. This impedes integrated reasoning over qualitative data obtained from geometric sources and “native” topological information – for example as provided from textual sources where precise locations or spatial extents are unknown or unknowable. To address this issue, the first contribution in this dissertation is a first-order logical ontology that treats geometric features (e.g. polylines, polygons) and relations between them as specializations of more general types of features (e.g. any kind of 2D or 1D features) and mereotopological relations between them. Key to this endeavor is the use of a multidimensional theory of space wherein, unlike traditional logical theories of mereotopology (like RCC), spatial entities of different dimensions can co-exist and be related. However terminating or tractable reasoning with such an expressive ontology and potentially large amounts of data is a challenging AI problem. Model finding tools used to verify FOL ontologies with data usually employ a SAT solver to determine the satisfiability of the propositional instantiations (SAT problems) of the ontology. These solvers often experience scalability issues with increasing number of objects and size and complexity of the ontology, limiting its use to ontologies with small signatures and building small models with less than 20 objects. To investigate how an ontology influences the size of its SAT translation and consequently the model finder’s performance, we develop a formalization of FOL ontologies with data. We theoretically identify parameters of an ontology that significantly contribute to the dramatic growth in size of the SAT problem. The search space of the SAT problem is exponential in the signature of the ontology (the number of predicates in the axiomatization and any additional predicates from skolemization) and the number of distinct objects in the model. Axiomatizations that contain many definitions lead to large number of SAT propositional clauses. This is from the conversion of biconditionals to clausal form. We therefore postulate that optional definitions are ideal sentences that can be eliminated from an ontology to boost model finder’s performance. We then formalize optional definition elimination (ODE) as an FOL ontology preprocessing step and test the simplification on a set of spatial benchmark problems to generate smaller SAT problems (with fewer clauses and variables) without changing the satisfiability and semantic meaning of the problem. We experimentally demonstrate that the reduction in SAT problem size also leads to improved model finding with state-of-the-art model finders, with speedups of 10-99%. Altogether, this dissertation improves spatial reasoning capabilities using FOL ontologies – in terms of a formal framework for integrated qualitative-geometric reasoning, and specific ontology preprocessing steps that can be built into automated reasoners to achieve better speedups in model finding times, and scalability with moderately-sized datasets

Group Recommendations with Responsibility Constraints

Sosiaalisen median laajeneminen on johtanut siihen, että yhä useammin ihmiset muodostavat ryhmiä erilaisia aktiviteetteja varten, ja peräkkäisiä ryhmäsuositteluja tuottavat järjestelmät ovat nousseet suosituksi tutkimusalueeksi. Ryhmälle tehtävät suositukset ovat huomattavasti monimutkaisempia kuin yksittäiset suositukset, koska suosittelujärjestelmät joutuvat vastaamaan kaikkien ryhmän jäsenten usein ristiriitaisten etujen tasapainottamisesta. Ottaen huomioon suositusten vaikutus käyttäjien kokemaan järjestelmän suorituskykyyn (esim. elokuvasuositukset) ja suositustehtävien usein varsin arkaluontoinen luonne (esim. sähköisen terveydenhuollon suositukset), suositusten luomisprosessia tulee harkita huolellisesti. Näistä seikoista johtuen on tullut entistä tarpeellisemmaksi kehittää erilaisia vastuullisuusrajoitteita noudattavia suosituksia. Tällaisia vastuullisuusrajoitteita ovat muun muassa reiluus eli puolueettomuus, ja läpinäkyvyys , joka helpottaa järjestelmän prosessien ymmärtämistä. Jos näitä rajoituksia noudatetaan, niin ryhmäsuosittelijoista tulee monimutkaisempia. On edelleen haastavampaa, jos suosittelijat käsittelevät suositusten jonoa sen sijaan, että jokainen suositus käsitellään erillään muista. Intuitiivisesti järjestelmän tulee ottaa huomioon itsensä ja ryhmän välisen vuorovaikutuksen historia ja mukauttaa suosituksiaan aikaisempien suositusten vaikutuksen mukaisesti. Tämä havainto johtaa uuden suositusjärjestelmätyypin, peräkkäisten ryhmäsuositusjärjestelmien , syntymiseen. Tavalliset ryhmäsuositusmenetelmät ovat tehottomia, kun niitä käytetään peräkkäisessä skenaariossa. Ne tuottavat usein suosituksia, joita ei ole edes tarkoitettu reiluksi kaikkia ryhmän jäseniä kohtaan, eli kaikki ryhmän jäsenet eivät ole yhtä tyytyväisiä suosituksiin. Käytännössä, kun jokaista suositusprosessia tarkastellaan erikseen, aina löytyy vähiten tyytyväinen jäsen. Vähiten tyytyväisimmän jäsenen ei kuitenkaan pitäisi aina olla sama, kun järjestelmän käyttö kattaa useamman kuin yhden suosituskierroksen. Tämä johtaisi oikeudenmukaisuuden rajoitteen rikkomiseen, koska järjestelmä olisi puolueellinen yhtä ryhmän jäsentä vastaan. Suositusjärjestelmien monimutkaisuuden vuoksi käyttäjät eivät ehkä pysty ymmärtämään ehdotuksen perusteluja. Tämän torjumiseksi monet järjestelmät tarjoavat selityksiä ja suosituksia avoimuusrajoituksen mukaisesti. Keskustelu siitä, miksi kohdetta ei ehdoteta, on arvokasta erityisesti järjestelmänvalvojille. Selitykset tällaisiin kyselyihin ovat heille korvaamatonta palautetta, kun he ovat kalibroimassa tai korjaamassa järjestelmäänsä. Kaiken kaikkiaan tämän opinnäytetyön tavoitteena on vastata seuraaviin tutkimuskysymyksiin (RQ). RQ1. Kuinka määritellä peräkkäiset ryhmäsuositukset ja miksi niitä tarvitaan? Kuinka suunnitella ryhmäsuositusmenetelmiä niiden pohjalta? Tässä opinnäytetyössä määritellään formaalisti peräkkäinen ryhmäsuositusjärjestelmä ja mitä tavoitteita sen tulee noudattaa. Lisäksi ehdotetaan kolmea uutta ryhmäsuositusmenetelmää oikeudenmukaisten peräkkäisten ryhmäsuositusten tuottamiseksi. RQ2. Kuinka hyödyntää vahvistusoppimista ryhmäsuositusmenetelmän valinnassa, kun järjestelmän ympäristö muuttuu jokaisen suosituskierroksen jälkeen? RQ1:n laajennuksessa tässä opinnäytetyössä ehdotetaan vahvistukseen perustuvaa mallia, joka valitsee sopivimman ryhmäsuositusmenetelmän käytettäväksi koko sarjassa, samalla pyrkien reiluuteen. RQ3. Kuinka suunnitella kysymyksiä ja tuottaa selityksiä sille, miksi jokin joukko ei näkynyt suosituslistalla tai tietyssä paikassa? Tässä väitöskirjassa määritellään miksi-ei- kysymys ja esitetään näiden kysymysten rakenne. Lisäksi työssä ehdotetaan mallia, jolla luodaan selityksiä näihin miksi-ei-kysymyksiin. RQ4. Kuinka sisällyttää erilaisia terveyteen liittyviä näkökohtia ryhmäsuosituksiin? Näissä on tärkeää antaa oikeudenmukaisia suosituksia, koska terveyssuositukset ovat erittäin arkaluontoisia. Mahdollisimman oikeudenmukaisen suosituksen tuottamiseksi tässä opinnäytetyössä ehdotetaan mallia, joka sisältää erilaisia terveysnäkökohtia.The expansion of social media has led more people to form groups for specific activities, and, consecutively, group recommender systems have emerged as popular research. In contrast to single recommendations, group recommendations involve a much greater degree of complexity since the systems are responsible for balancing the often conflicting interests of all group members. Due to the impact of recommendations on users’ perceived performance (e.g., movie recommendations) and the often inherently sensitive nature of recommendation tasks (e.g., e-health recommendations), the process by which recommendations are generated should be carefully considered. As a result, it has become increasingly necessary to develop recommendations that adhere to various responsibility constraints. Such responsibility constraints include fairness , which corresponds to a lack of bias, and transparency , which facilitates an understanding of the processes of the system. Nevertheless, if these constraints are followed, group recommender systems be- come more complex. It is even more challenging if they are to consider a sequence of recommendations rather than each recommendation as a separate process. Intuitively, the system should take into account the historical interactions between itself and the group and adjust its recommendations in accordance with the impact of its previous suggestions. This observation leads to the emergence of a new type of recommender system, called sequential group recommendation systems. However, standard group recommendation approaches are ineffective when applied in a sequential scenario. They often produce recommendations that are not even intended to be fair to all group members, i.e., not all group members are equally satisfied with the recommendations. In practice, when each recommendation process is considered in isolation, there is always going to be a least satisfied member. However, the least satisfied member should not always be the same when the scope of the system encompasses more than one recommendation round. This will result in the fairness constraint being broken since the system is biased against one group member. As a result of the complex nature of recommender systems, users may be unable to understand the reasoning behind a suggestion. To counter this, many systems provide explanations along with their recommendations in adherence to the transparency constraint. Discussing why not suggesting an item is valuable, especially for system administrators. Explanations to such queries are invaluable feedback for them when they are in the process of calibrating or debugging their system. Overall, this thesis aims to answer the following Research Questions (RQ). RQ1. How to define sequential group recommendations, and why are they needed? How to de- sign group recommendation methods based on them? This thesis formally defines a sequential group recommender system and what objectives it should observe. Additionally, it proposes three novel group recommendation methods to produce fair sequential group recommendations. RQ2. How to exploit reinforcement learning to select a group recommendation method when the system’s environment changes after each recommendation round? In an extension of the RQ1, this thesis proposes a reinforcement-based model that selects the most appropriate group recommendation method to apply throughout a series of recommendations while aiming for fair recommendations. RQ3. How to design questions and produce explanations for why a set of items did not appear in a recommendation list or at a particular position? This dissertation defines what a Why-not question is, as well as presents a structure for them. Additionally, it proposes a model to generate explanations for these Why-not questions. RQ4. How to incorporate various health-related aspects in group recommendations? It is important to make fair recommendations when dealing with extremely sensitive health-related information. In order to produce as fair a recommendation as possible, this thesis proposes a model that incorporates various health aspects

MinSAT versus MaxSAT for Optimization Problems

Despite their similarities, MaxSAT and MinSAT use different encodings and solving techniques to cope with optimization problems. In this paper we describe a new weighted partial MinSAT solver, define original MinSAT encodings for relevant combinatorial problems, propose a new testbed for evaluating MinSAT, report on an empirical investigation comparing MinSAT with MaxSAT, and provide new insights into the duality between MinSAT and MaxSAT. © 2013 Springer-Verlag.Peer Reviewe