Multi-Context Reasoning in Continuous Data-Flow Environments

The field of artificial intelligence, research on knowledge representation and reasoning has originated a large variety of formats, languages, and formalisms. Over the decades many different tools emerged to use these underlying concepts. Each one has been designed with some specific application in mind and are even used nowadays, where the internet is seen as a service to be sufficient for the age of Industry 4.0 and the Internet of Things. In that vision of a connected world, with these many different formalisms and systems, a formal way to uniformly exchange information, such as knowledge and belief is imperative. That alone is not enough, because even more systems get integrated into the online world and nowadays we are confronted with a huge amount of continuously flowing data. Therefore a solution is needed to both, allowing the integration of information and dynamic reaction to the data which is provided in such continuous data-flow environments. This work aims to present a unique and novel pair of formalisms to tackle these two important needs by proposing an abstract and general solution. We introduce and discuss reactive Multi-Context Systems (rMCS), which allow one to utilise different knowledge representation formalisms, so-called contexts which are represented as an abstract logic framework, and exchange their beliefs through bridge rules with other contexts. These multiple contexts need to mutually agree on a common set of beliefs, an equilibrium of belief sets. While different Multi-Context Systems already exist, they are only solving this agreement problem once and are neither considering external data streams, nor are they reasoning continuously over time. rMCS will do this by adding means of reacting to input streams and allowing the bridge rules to reason with this new information. In addition we propose two different kind of bridge rules, declarative ones to find a mutual agreement and operational ones for adapting the current knowledge for future computations. The second framework is more abstract and allows computations to happen in an asynchronous way. These asynchronous Multi-Context Systems are aimed at modelling and describing communication between contexts, with different levels of self-management and centralised management of communication and computation. In this thesis rMCS will be analysed with respect to usability, consistency management, and computational complexity, while we will show how asynchronous Multi-Context Systems can be used to capture the asynchronous ideas and how to model an rMCS with it. Finally we will show how rMCSs are positioned in the current world of stream reasoning and that it can capture currently used technologies and therefore allows one to seamlessly connect different systems of these kinds with each other. Further on this also shows that rMCSs are expressive enough to simulate the mechanics used by these systems to compute the corresponding results on its own as an alternative to already existing ones. For asynchronous Multi-Context Systems, we will discuss how to use them and that they are a very versatile tool to describe communication and asynchronous computation

Proceedings of the International Workshop on Reactive Concepts in Knowledge Representation 2014

These are the proceedings of the International Workshop on Reactive Concepts in Knowledge Representation (ReactKnow 2014), which took place on August 19th, 2014 in Prague, co-located with the 21st European Conference on Artificial Intelligence (ECAI 2014)

Heterogeneous reasoning in dynamic environments

We would like to thank K. Schekotihin and the anonymous reviewers for their comments, which helped improving this paper. G. Brewka, S. Ellmauthaler, and J. Puhrer were partially supported by the German Research Foundation (DFG) under grants BR-1817/7-1/2 and FOR 1513. R. Goncalves, M. Knorr and J. Leite were partially supported by Fundacao para a Ciencia e a Tecnologia (FCT) under project NOVA LINCS (UID/CEC/04516/2013). Moreover, R. Goncalves was partially supported by FCT grant SFRH/BPD/100906/2014 and M. Knorr by FCT grant SFRH/BPD/86970/2012.Managed multi-context systems (mMCSs) allow for the integration of heterogeneous knowledge sources in a modular and very general way. They were, however, mainly designed for static scenarios and are therefore not well-suited for dynamic environments in which continuous reasoning over such heterogeneous knowledge with constantly arriving streams of data is necessary. In this paper, we introduce reactive multi-context systems (rMCSs), a framework for reactive reasoning in the presence of heterogeneous knowledge sources and data streams. We show that rMCSs are indeed well-suited for this purpose by illustrating how several typical problems arising in the context of stream reasoning can be handled using them, by showing how inconsistencies possibly occurring in the integration of multiple knowledge sources can be handled, and by arguing that the potential non-determinism of rMCSs can be avoided if needed using an alternative, more skeptical well-founded semantics instead with beneficial computational properties. We also investigate the computational complexity of various reasoning problems related to rMCSs. Finally, we discuss related work, and show that rMCSs do not only generalize mMCSs to dynamic settings, but also capture/extend relevant approaches w.r.t. dynamics in knowledge representation and stream reasoning.publishe

Inconsistency Management in Reactive Multi-context Systems

FCT grant SFRH/BPD/100906/2014 and M. Knorr by FCT grant SFRH/BPD/86970/2012. German Research Foundation (DFG) grants BR-1817/7-1 and FOR 1513We address the problem of global inconsistency in reactive multi-context systems (rMCSs), a framework for reactive reasoning in the presence of heterogeneous knowledge sources that can deal with continuous input streams. Their semantics is given in terms of equilibria streams. The occurrence of inconsistencies, where rMCSs fail to have an equilibria stream, can render the entire system useless. We discuss various methods for handling this problem, following different strategies such as repairing the rMCS, or even relaxing the notion of equilibria stream so that it can go through inconsistent states.proofpublishe

Debating Technology for Dialogical Argument:Sensemaking, Engagement and Analytics

Debating technologies, a newly emerging strand of research into computational technologies to support human debating, offer a powerful way of providing naturalistic, dialogue-based interaction with complex information spaces. The full potential of debating technologies for dialogical argument can, however, only be realized once key technical and engineering challenges are overcome, namely data structure, data availability, and interoperability between components. Our aim in this article is to show that the Argument Web, a vision for integrated, reusable, semantically rich resources connecting views, opinions, arguments, and debates online, offers a solution to these challenges. Through the use of a running example taken from the domain of citizen dialogue, we demonstrate for the first time that different Argument Web components focusing on sensemaking, engagement, and analytics can work in concert as a suite of debating technologies for rich, complex, dialogical argument

Generalizing Multi-Context Systems for Reactive Stream Reasoning Applications

In the field of artificial intelligence (AI), the subdomain of knowledge representation (KR) has the aim to represent, integrate, and exchange knowledge in order to do some reasoning about the given information. During the last decades many different KR-languages were proposed for a variety of certain applications with specific needs. The concept of a managed Multi-Context System (mMCS) was introduced to provide adequate formal tools to interchange and integrate knowledge between different KR-approaches. Another arising field of interest in computer science is the design of online applications, which react directly to (possibly infinite) streams of information. This paper presents a genuine approach to generalize mMCS for online applications with continuous streams of information. Our major goal is to find a good tradeoff between expressiveness and computational complexity

Abstract Dialectical Frameworks : properties, complexity, and implementation

Zsfassung in dt. SpracheAbstract Argumentation konnte im Laufe der letzten zwei Dekaden stetig immer mehr Interesse im Forschungsbereich der Künstlichen Intelligenz gewinnen. Eines der wichtigsten Konzepte ist dabei Dung's Argumentation Framework. Hierbei handelt es sich um einen einfachen, jedoch mächtigen und gut entwickelten Ansatz zum Darstellen von Argumenten und deren Beziehungen. Diese Informationen sind hierbei in Form eines gerichteten Graphen kodiert, wo jede Kante einen Angriff auf ein anderes Argument symbolisiert. Mittels dieses Frameworks ist es ebenfalls möglich, durch Semantiken Mengen von Argumenten auszuwählen und zu prüfen ob sie gewisse Eigenschaften besitzen (z.B. ob die Menge konfliktfrei ist).Vor kurzem wurde in Form von Abstract Dialectical Frameworks (ADFs) eine Verallgemeinerung dieses Konzepts vorgestellt. Dabei werden die Beziehungen mittels einer vollständigen Funktion definiert, wodurch sie nicht mehr nur auf Angriffe beschränkt sind. Durch diese Funktion, welche Akzeptanzbedingung genannt wird, ist es nun möglich sehr komplexe Beziehungen zu beschreiben. Aufgrund dieser Ausdrucksstärke gibt es nun jedoch Probleme gewisse Semantiken für ADFs zu definieren. Daher ist die Unterklasse der bipolaren ADFs eingeführt worden.Im Rahmen dieser Arbeit wird das Konzept der ADFs nochmals genau vorgestellt. Da die Einschränkung auf bipolare ADFs nur eine vorübergehende Lösung darstellt, wird versucht eine allgemeine Form der stabilen Modell-Semantik zu finden. Da dies die grundlegende Semantik für alle anderen ist, welche auf bipolare ADFs beschränkt sind, wird hierfür ein generalisierter Ansatz am meisten benötigt. Um dies zu erreichen werden Konzepte von anderen Bereichen der theoretischen Informatik genutzt um deren Ergebnisse für die Probleme mit bipolaren ADFs zu nutzen. Dadurch können ADFs ebenfalls leichter in jenen Gebieten eingesetzt werden.Hauptsächlich werden die aussagenlogischen ADFs behandelt werden, da es mit deren Hilfe möglich ist ADFs relativ natürlich in bipolare umzuwandeln. Darauf aufbauend wird dann die Entwicklung einer allgemeinen stabilen Modell-Semantik gezeigt. Zusätzlich werden noch einige Eigenschaften zwischen einzelnen Semantiken diskutiert. Eine Zusammenfassung bereits vorhandener komplexitätsanalytischer Resultate wird ebenfalls präsentiert um danach neue Ergebnisse zeigen zu können.Da es derzeit kein adäquates Software-System zum Berechnen von Modellen für Semantiken auf ADFs gibt, wird zusätzlich zu den formalen Ergebnissen noch eine neue ASP (Answer Set Programming) basierte Implementierung präsentiert. Um ein Gefühl für die Effizienz des Systems zu bekommen werden außerdem noch empirische Experimente zur Laufzeit diskutiert.Over the last two decades the interest for Abstract Argumentation steadily raised in the field of Artificial Intelligence.The concept of Dung's Argumentation Frameworks (AFs), where arguments and their relations are represented in a directed graph-structure, is a well-known, simple, and powerful concept. This framework is used to find acceptable sets of arguments, which have specific properties (e.g. being conflict free), defined by several semantics.Recently Abstract Dialectical Frameworks (ADFs) were introduced, a generalization of Dung's approach, to overcome the limitation of attack-relations being the only type of native relations. To reach this goal, in addition to the relations, total functions are used to decide the acceptance of an argument. These functions are so called acceptance conditions. Due to the high expressiveness of this newly proposed theory, some semantics were only generalized for the restricted bipolar ADFs yet.This work will give an exhaustive overview on ADFs. The restriction to bipolar ADFs for some of the semantics is not desired, so we try to develop a solution to gain the generalized stable model semantics. This semantics is particularly important because the other semantics which are restricted to bipolar ADFs, depend on stable models. To gain such a generalization, we will try to connect the foundations of ADFs to other fields of computer science. So we may relate subclasses of these fields to the bipolar ADF to overcome this obstacle. This connection also makes ADFs more accessible to other fields of computer science.We will concentrate mainly on the introduction of the alternative representation of propositional-formula ADFs (pForm-ADFs), but we will also show that ADFs can be represented as hyper-graphs. Based on the new representation a transformation from ADFs to pForm-ADFs, together with a generalization of the stable model semantics will be presented. In addition some properties between semantics will be investigated and an overview of complexity results, enriched with new ones is given.Currently there is no software system available to compute semantics for ADFs. So in addition to the formal results we also present an Answer Set Programming (ASP) based implementation to solve these highly complex computations. We will also present preliminary empirical experiments.11

Multi-Context Reasoning in Continuous Data-Flow Environments

The field of artificial intelligence, research on knowledge representation and reasoning has originated a large variety of formats, languages, and formalisms. Over the decades many different tools emerged to use these underlying concepts. Each one has been designed with some specific application in mind and are even used nowadays, where the internet is seen as a service to be sufficient for the age of Industry 4.0 and the Internet of Things. In that vision of a connected world, with these many different formalisms and systems, a formal way to uniformly exchange information, such as knowledge and belief is imperative. That alone is not enough, because even more systems get integrated into the online world and nowadays we are confronted with a huge amount of continuously flowing data. Therefore a solution is needed to both, allowing the integration of information and dynamic reaction to the data which is provided in such continuous data-flow environments. This work aims to present a unique and novel pair of formalisms to tackle these two important needs by proposing an abstract and general solution. We introduce and discuss reactive Multi-Context Systems (rMCS), which allow one to utilise different knowledge representation formalisms, so-called contexts which are represented as an abstract logic framework, and exchange their beliefs through bridge rules with other contexts. These multiple contexts need to mutually agree on a common set of beliefs, an equilibrium of belief sets. While different Multi-Context Systems already exist, they are only solving this agreement problem once and are neither considering external data streams, nor are they reasoning continuously over time. rMCS will do this by adding means of reacting to input streams and allowing the bridge rules to reason with this new information. In addition we propose two different kind of bridge rules, declarative ones to find a mutual agreement and operational ones for adapting the current knowledge for future computations. The second framework is more abstract and allows computations to happen in an asynchronous way. These asynchronous Multi-Context Systems are aimed at modelling and describing communication between contexts, with different levels of self-management and centralised management of communication and computation. In this thesis rMCS will be analysed with respect to usability, consistency management, and computational complexity, while we will show how asynchronous Multi-Context Systems can be used to capture the asynchronous ideas and how to model an rMCS with it. Finally we will show how rMCSs are positioned in the current world of stream reasoning and that it can capture currently used technologies and therefore allows one to seamlessly connect different systems of these kinds with each other. Further on this also shows that rMCSs are expressive enough to simulate the mechanics used by these systems to compute the corresponding results on its own as an alternative to already existing ones. For asynchronous Multi-Context Systems, we will discuss how to use them and that they are a very versatile tool to describe communication and asynchronous computation

Multi-Context Reasoning in Continuous Data-Flow Environments

The field of artificial intelligence, research on knowledge representation and reasoning has originated a large variety of formats, languages, and formalisms. Over the decades many different tools emerged to use these underlying concepts. Each one has been designed with some specific application in mind and are even used nowadays, where the internet is seen as a service to be sufficient for the age of Industry 4.0 and the Internet of Things. In that vision of a connected world, with these many different formalisms and systems, a formal way to uniformly exchange information, such as knowledge and belief is imperative. That alone is not enough, because even more systems get integrated into the online world and nowadays we are confronted with a huge amount of continuously flowing data. Therefore a solution is needed to both, allowing the integration of information and dynamic reaction to the data which is provided in such continuous data-flow environments. This work aims to present a unique and novel pair of formalisms to tackle these two important needs by proposing an abstract and general solution. We introduce and discuss reactive Multi-Context Systems (rMCS), which allow one to utilise different knowledge representation formalisms, so-called contexts which are represented as an abstract logic framework, and exchange their beliefs through bridge rules with other contexts. These multiple contexts need to mutually agree on a common set of beliefs, an equilibrium of belief sets. While different Multi-Context Systems already exist, they are only solving this agreement problem once and are neither considering external data streams, nor are they reasoning continuously over time. rMCS will do this by adding means of reacting to input streams and allowing the bridge rules to reason with this new information. In addition we propose two different kind of bridge rules, declarative ones to find a mutual agreement and operational ones for adapting the current knowledge for future computations. The second framework is more abstract and allows computations to happen in an asynchronous way. These asynchronous Multi-Context Systems are aimed at modelling and describing communication between contexts, with different levels of self-management and centralised management of communication and computation. In this thesis rMCS will be analysed with respect to usability, consistency management, and computational complexity, while we will show how asynchronous Multi-Context Systems can be used to capture the asynchronous ideas and how to model an rMCS with it. Finally we will show how rMCSs are positioned in the current world of stream reasoning and that it can capture currently used technologies and therefore allows one to seamlessly connect different systems of these kinds with each other. Further on this also shows that rMCSs are expressive enough to simulate the mechanics used by these systems to compute the corresponding results on its own as an alternative to already existing ones. For asynchronous Multi-Context Systems, we will discuss how to use them and that they are a very versatile tool to describe communication and asynchronous computation