Stefan Ellmauthaler

Multi-context systems for reactive reasoning in dynamic environments

We show in this paper how managed multi-context systems (mMCSs) can be turned into a reactive formalism suitable for continuous reasoning in dynamic environments. We extend mMCSs with (abstract) sensors and define the notion of a run of the extended systems. We then show how typical problems arising in online reasoning can be addressed: handling potentially inconsistent sensor input, modeling intelligent forms of forgetting, selective integration of knowledge, and controlling the reasoning effort spent by contexts, like setting contexts to an idle mode. We also investigate the complexity of some important related decision problems and discuss different design choices which are given to the knowledge engineer.

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.

Asynchronous Multi-Context Systems

We present asynchronous multi-context systems aMCSs, a framework for loosely coupling different knowledge representation formalisms that allows for online reasoning in a dynamic environment. An aMCS interacts with the outside world via input and output streams and may therefore react to a continuous flow of external information. In contrast to recent proposals, contexts in an aMCS communicate with each other in an asynchronous way which fits the needs of many application domains and is beneficial for scalability. The federal semantics of aMCSs renders our framework an integration approach rather than a knowledge representation formalism itself. We illustrate the introduced concepts by means of an example scenario dealing with rescue services. In addition, we compare aMCSs to reactive multi-context systems and describe how to simulate the latter with our novel approach.

Inconsistency Management in Reactive Multi-context Systems

We 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.

Reactive multi-context systems: Heterogeneous reasoning in dynamic environments

Abstract 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.

Advanced Solving Technology for Dynamic and Reactive Applications

The project Advanced Solving Technology for Dynamic and Reactive Applications (henceforth called ASTRA) is part of the DFG-funded Research Unit HYBRIS: Hybrid Reasoning for Intelligent Systems (www.hybrid-reasoning.org/). The Unit started in 2012 with the aim of investigating different combinations of both qualitative and quantitative reasoning. Among the quantitative aspects addressed are time, uncertainty, preferences, continuous state spaces, and quantitative data such as point clouds or text, from which meaningful symbolic descriptions can be extracted. The principal investigators of ASTRA are Gerhard Brewka (Leipzig), Gabriele Kern-Isberner (Dortmund) and Torsten Schaub (Potsdam). In a nutshell, the project aims to provide hybrid reasoning methods that are sufficiently expressive to handle complex decision-making problems. So far our research focused on answer set solving technology for incremental and reactive reasoning, preferential reasoning, and finite linear constraint solving. Also, basic techniques for reactive multi-context systems and argumentative reasoning were developed. Currently we are realizing new methods to be built on top of the existing systems, extending the range of reasoning methods and addressing in particular uncertain reasoning. In particular, we study combinations of uncertain reasoning and answer set programming (ASP), respectively argumentation. In addition we want to substantially generalize existing preferential reasoning methods. We also study new forms of theory-based reasoning, and further investigate reactive and interactive forms of reasoning. On top of the advanced reasoning methods, we will build a general framework for complex hybrid problem solving, focusing on interactive, hybrid methods for decision making and for argumentation. The developed methods and frameworks will be tested in applications from the field of logistics, namely logistic systems design, autonomous logistic vehicles, and RoboCup logistics. To provide a clearer idea of our research we focus in what follows on two of the various aspects of the project, namely on extensions of answer set programming with constraints and on applications in logistics.