1,721,192 research outputs found
The IDP system: A model expansion system for an extension of classical logic
Full text linkThe model expansion (MX) search problem consists of finding models of a given theory that expand a given finite interpretation. Model expansion in classical first-order logic (FO) has been proposed as the basis for an Answer Set Programming-like declarative programming framework for solving NP problems. In this paper, we present IDP, a system for solving MX problems that integrates technology from ASP and SAT. Its strenght lies both in its rich input language and its efficiency. IDP is the first model expansion system that can handle full FO, but its language extends FO with many other primitives such as inductive definitions, aggregates, quantifiers with numerical constraints, order-sorted types, arithmetic, partial functions, etc. We show that this allows for a natural, compact representation of many interesting search problems. Despite the generality of its language, our experiments show that the IDP system belongs to the most efficient ASP and MX systems.sponsorship: Johan Wittocx is research assistant of the fund for scientific research Flanders (FWO-Vlaanderen)status: Publishe
On the difference between abduction and induction: a model theoretic perspective
Full text linkstatus: Publishe
Meta-level representations in the IDP knowledge base system: Towards bootstrapping inference engine development
No full textDeclarative systems aim at solving tasks by running inference engines on a specification, to free its users from having to specify how a task should be tackled.
In order to provide such functionality, declarative systems themselves apply complex reasoning techniques, and, as a consequence, the development of such systems can be laborious work.
In this paper, we demonstrate that the declarative approach can be applied to develop such systems, by tackling the tasks solved inside a declarative system declaratively.
In order to do this, in many cases a meta-level representation of those specifications is required. Furthermore, by using the language of the system for the meta-level representation, it opens the door to bootstrapping: an inference engine can be implemented using the inference it performs itself.
One such declarative system is the IDP knowledge base system, based on the language FO(.)IDP, a rich extension of first-order logic. In this paper, we discuss how FO(.)IDP can support meta-level representations in general and which language constructs make those representations even more natural. Afterwards, we show how meta-FO(.)IDP can be applied to bootstrap its model expansion inference engine. We discuss the advantages of this approach: the resulting program is easier to understand, easier to maintain and more flexible.status: Publishe
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Applications of Feasible Inference for Expressive Logics
No full textKnowledge Representation and Reasoning is the area of artificial intelligence that is concerned with how knowledge can be represented symbolically in a formal language, and how computer programs can reason about knowledge in an automatic way. One candidate for such a knowledge representation language is first-order logic (FO). Historically people have been doubtful, however, about the usefulness of first-order logic as a knowledge representation language. Indeed, the discovery that first-order logic is undecidable led a whole part of the field into believing that in order to be of any practical use, first-order logic needs to be severely restricted. However, recent trends in the field of computational logic have shown that there do exists feasible inference methods for first-order logic, many of which have been demonstrated to be practically useful. On a different theme, fields such as non-monotonic reasoning and answer set programming have their origin in a conviction that first-order logic is not expressive enough as a KR language. Indeed, several important concepts can not ---or at least not naturally--- be represented in first-order logic. Instead of turning away from FO and taking a different approach as was done, for example, by the answer set programming community, several researchers have tried to extend FO with different constructs, in order to overcome some of these shortcomings. The language FO(.) is the extension of FO with a number of such constructs. It has been demonstrated that a large variety of practical problems can be represented in FO(.) in an intuitive way. In this text, we explore in a number of settings how the relevant problems can be solved in a feasible way, while relying on FO(.) as the main knowledge representation language. Often, the answer will be to use cheap forms of inference, such as propagation. In the first part of this dissertation we study a propagation method for first-order logic, and extend it to first-order logic with inductive definitions, which is denoted by FO(ID). In the rest of this dissertation we start by an exploration of a Knowledge Base System based on FO(.), through a case study about configuration software. We catalogue a number of key reasoning tasks that arise naturally within the context of configuration software, and investigate how they can be handled efficiently.When inference tasks become too hard to be of practical use, a common solution is to approximately solve that task. In the literature, approximative approaches to a number of problems can be found. We propose a framework to approximately solve finite domain ∃∀SO(ID) satisfiability problems. Our approach provides a general framework for these approximative approaches in the literature, and can also be used for solving useful practical problems. Next we turn our attention to reasoning about and representing sensing actions and the knowledge of an agent. We extend FO(.) even further so that we can intuitively represent how the knowledge of an agent changes through sensing actions. This leads to a very general representation language. We show how the propagation method for FO(ID) can be used to define an approximative inference method for solving the projection problem. We conclude this dissertation by looking at Ordered Epistemic Logic. Instead of further extending a language, here we do the opposite. We observe that many existing epistemic languages do not maintain the stratified structure that is inherent in many practical examples. This complicates the semantics and reasoning procedures considerably. By defining a logic that keeps this inherent structure explicit, we have the benefit of still having a very expressive logic, in which most of these examples can be naturally represented, while inference tasks have a lower complexity.status: Publishe
Model Generation for ID-Logic
Full text linkIn the domain of knowledge representation and reasoning, one studies kno wledge: what types of knowledge there are, how often they are used, how they can be expressed in a formal languange, etc. An important goal of k nowledge representation is to develop formal languages (logics) that can be used to express a wide range of problems, to develop automated reaso ning methods for these languages, and to develop efficient implementatio ns of these reasoning methods. ID-logic is a knowledge representation language that extends classical l ogic with inductive definitions. It can express a large variety of pract ical problems in an intuitive way, and has therefore been promoted as a useful knowledge representation language. Model generation is a very general and widely applicable automated reaso ning method. The topic of this dissertation is propositional model gener ation for ID-logic. As such, this work offers an important contribution to the development of automated reasoning methods for ID-logic. The main part of this dissertation is concerned with the propositional f ragment of ID-logic, called PC(ID), and with model generation algorithms for it. We provide two alternative semantical characterizations of PC(I D), both of which yield important insights in the underlying structure o f PC(ID) theories, and both of which therefore contribute to the underst anding of the model generation task for PC(ID). Also, the second charact erization offers a vocabulary-preserving transformation of PC(ID) theori es to propositional logic. We then study practical model generation algorithms for PC(ID). We discu ss a number of possible strategies, provide various propagation rules, a nd present algorithms for these rules. We have also implemented a propos itional model generator for PC(ID). The rest of the dissertation is concerned with ID-logic itself. We discu ss a methodology of knowledge representation in ID-logic and provide som e examples. We also extend ID-logic with aggregate expressions, thereby extending the applicability of model generation for ID-logic. We study p ropositional model generation algorithms for this extension, and have im plemented such algorithms. Finally, we compare ID-logic to a related for malism, namely ASP, and provide a transformation of ID-logic theories to ASP theories.status: Publishe
Finite Domain and Symbolic Inference Methods for Extensions of First-Order Logic
Full text linkHet wetenschappelijk onderzoeksdomein Kennisrepresentatie en redeneren ( KRR) beoogt het ontwikkelen van formele talen (logica's) die geschikt zi jn om kennis uit te drukken en van inferentiemethodes om te redeneren ov er kennis uitgedrukt in die talen. Een van de hoofddoelen van KRR is het bouwen van een kennisbanksysteem (KBS): een systeem waarin een menselij ke expert zijn kennis over een bepaald domein opslaat en waarmee verschi llende taken in dat domein opgelost worden door het toepassen van infere ntiemethodes. In deze thesis stellen we een uitbreiding van klassieke lo gica voor als geschikte logica voor een KBS en onderzoeken we verschille nde vormen van inferentie. De eerste vorm van inferentie die we onderzoeken is propagatie: uit een logische theorie feiten afleiden die zeker waar zijn in elk model van de theorie. We onderzoeken voornamelijk een benaderende maar efficiënte vo rm van propagatie, die bovendien op een symbolische manier uitgevoerd ka n worden. We beschrijven verschillende toepassingen van propagatie. De tweede vorm van inferentie is propositionalisatie: het omzetten van e en logische theorie die variabelen mag bevatten naar een equivalente pro positionele theorie. Propositionalisatie wordt vaak gebruikt als eerste stap in systemen voor eindige model generatie. Eindige model generatie v ormt op zich ook een belangrijke vorm van inferentie. We tonen aan hoe p ropositionalisatie verbeterd kan worden door overtollige informatie toe te voegen aan een theorie. Deze overtollige informatie kan berekend word en met behulp van symbolische propagatie. Ten derde bestuderen we hoe fouten in een logische theorie opgespoord ku nnen worden. De methode die we voorstellen bestaat uit het interactief o verlopen van formele bewijzen van de inconsistentie van een theorie. We tonen dat model generatoren die gebaseerd zijn op propagatie gebruikt ku nnen worden om automatisch zulke formele bewijzen op te stellen. Tenslotte bestuderen we model revisie: het aanpassen van een model van e en theorie wanneer nieuwe vereisten gesteld worden. Model revisie heeft onder andere toepassingen in herconfiguratie en herplanningsproblemen. W e laten zien hoe model revisie problemen aangepakt kunnen worden door he t oplossen van opeenvolgende model generatie problemen.status: Publishe
- …
