arXiv subjects

Matthias Knorr

Publications and source records attributed to Matthias Knorr.

15 recordsLinked to original sources

arXiv2026

EZASP - Facilitating the Usage of ASP

Answer Set Programming (ASP) is a declarative programming language used for modeling and solving complex combinatorial problems. It has been successfully applied to a number of different real-world problems. However, learning its usage can prove challenging as the declarative language, from a conceptual perspective, differs substantially from imperative programming, and programs are not required to adhere to any particular structure, offering arguably almost too much freedom for a beginner. Recently, a new methodology called Easy Answer Set Programming (Easy ASP) has been introduced that aims to aid in this learning process by focussing on a well-defined fragment of the ASP language and introducing additional structure to the programs. However, while this methodology can indeed be employed, to the best of our knowledge, no tool integrates its features currently. In this paper, we present EZASP, a Visual Studio Code extension designed to support the development of ASP programs following the Easy ASP methodology. It covers and extends the language fragment of Easy ASP and provides the user with warnings in the case of deviations from the methodology as well as the possibility to automatically reorder the program. Complementarily, it also adds syntax error highlighting, including detection of non-safe variables directly while editing, and configurability, as all features can be optionally disabled. A small user study in the context of university teaching suggests that these features are benefitial for both new and experienced users.

cs.SE↗

arXiv2025

Interpolation in Knowledge Representation

Craig interpolation and uniform interpolation have many applications in knowledge representation, including explainability, forgetting, modularization and reuse, and even learning. At the same time, many relevant knowledge representation formalisms do in general not have Craig or uniform interpolation, and computing interpolants in practice is challenging. We have a closer look at two prominent knowledge representation formalisms, description logics and logic programming, and discuss theoretical results and practical methods for computing interpolants.

cs.AI↗

arXiv2024

Abstract Dialectical Frameworks are Boolean Networks (full version)

Dialectical frameworks are a unifying model of formal argumentation, where argumentative relations between arguments are represented by assigning acceptance conditions to atomic arguments. Their generality allow them to cover a number of different approaches with varying forms of representing the argumentation structure. Boolean regulatory networks are used to model the dynamics of complex biological processes, taking into account the interactions of biological compounds, such as proteins or genes. These models have proven highly useful for comprehending such biological processes, allowing to reproduce known behaviour and testing new hypotheses and predictions in silico, for example in the context of new medical treatments. While both these approaches stem from entirely different communities, it turns out that there are striking similarities in their appearence. In this paper, we study the relation between these two formalisms revealing their communalities as well as their differences, and introducing a correspondence that allows to establish novel results for the individual formalisms.

cs.AI↗

arXiv2022

Analytical Performance Estimation during Code Generation on Modern GPUs

Automatic code generation is frequently used to create implementations of algorithms specifically tuned to particular hardware and application parameters. The code generation process involves the selection of adequate code transformations, tuning parameters, and parallelization strategies. We propose an alternative to time-intensive autotuning, scenario-specific performance models, or black-box machine learning to select the best-performing configuration. This paper identifies the relevant performance-defining mechanisms for memory-intensive GPU applications through a performance model coupled with an analytic hardware metric estimator. This enables a quick exploration of large configuration spaces to identify highly efficient code candidates with high accuracy. We examine the changes of the A100 GPU architecture compared to the predecessor V100 and address the challenges of how to model the data transfer volumes through the new memory hierarchy. We show how our method can be coupled to the pystencils stencil code generator, which is used to generate kernels for a range-four 3D-25pt stencil and a complex two-phase fluid solver based on the Lattice Boltzmann Method. For both, it delivers a ranking that can be used to select the best-performing candidate. The method is not limited to stencil kernels but can be integrated into any code generator that can generate the required address expressions.

cs.DC↗

arXiv2021

Forgetting in Answer Set Programming -- A Survey

Forgetting - or variable elimination - is an operation that allows the removal, from a knowledge base, of middle variables no longer deemed relevant. In recent years, many different approaches for forgetting in Answer Set Programming have been proposed, in the form of specific operators, or classes of such operators, commonly following different principles and obeying different properties. Each such approach was developed to address some particular view on forgetting, aimed at obeying a specific set of properties deemed desirable in such view, but a comprehensive and uniform overview of all the existing operators and properties is missing. In this paper, we thoroughly examine existing properties and (classes of) operators for forgetting in Answer Set Programming, drawing a complete picture of the landscape of these classes of forgetting operators, which includes many novel results on relations between properties and operators, including considerations on concrete operators to compute results of forgetting and computational complexity. Our goal is to provide guidance to help users in choosing the operator most adequate for their application requirements.

cs.AI↗

arXiv2021

Deep Neural Networks for Approximating Stream Reasoning with C-SPARQL

The amount of information produced, whether by newspapers, blogs and social networks, or by monitoring systems, is increasing rapidly. Processing all this data in real-time, while taking into consideration advanced knowledge about the problem domain, is challenging, but required in scenarios where assessing potential risks in a timely fashion is critical. C-SPARQL, a language for continuous queries over streams of RDF data, is one of the more prominent approaches in stream reasoning that provides such continuous inference capabilities over dynamic data that go beyond mere stream processing. However, it has been shown that, in the presence of huge amounts of data, C-SPARQL may not be able to answer queries in time, in particular when the frequency of incoming data is higher than the time required for reasoning with that data. In this paper, we investigate whether reasoning with C-SPARQL can be approximated using Recurrent Neural Networks and Convolutional Neural Networks, two neural network architectures that have been shown to be well-suited for time series forecasting and time series classification, to leverage on their higher processing speed once the network has been trained. We consider a variety of different kinds of queries and obtain overall positive results with high accuracies while improving processing time often by several orders of magnitude.

cs.AI↗

arXiv2021

Opening the Black Box: Performance Estimation during Code Generation for GPUs

Automatic code generation is frequently used to create implementations of algorithms specifically tuned to particular hardware and application parameters. The code generation process involves the selection of adequate code transformations, tuning parameters, and parallelization strategies. To cover the huge search space, code generation frameworks may apply time-intensive autotuning, exploit scenario-specific performance models, or treat performance as an intangible black box that must be described via machine learning. This paper addresses the selection problem by identifying the relevant performance-defining mechanisms through a performance model coupled with an analytic hardware metric estimator. This enables a quick exploration of large configuration spaces to identify highly efficient candidates with high accuracy. Our current approach targets memory-intensive GPGPU applications and focuses on the correct modeling of data transfer volumes to all levels of the memory hierarchy. We show how our method can be coupled to the pystencils stencil code generator, which is used to generate kernels for a range four 3D25pt stencil and a complex two phase fluid solver based on the Lattice Boltzmann Method. For both, it delivers a ranking that can be used to select the best performing candidate. The method is not limited to stencil kernels, but can be integrated into any code generator that can generate the required address expressions.

cs.PF↗

arXiv2021

Faster than LASER -- Towards Stream Reasoning with Deep Neural Networks

With the constant increase of available data in various domains, such as the Internet of Things, Social Networks or Smart Cities, it has become fundamental that agents are able to process and reason with such data in real time. Whereas reasoning over time-annotated data with background knowledge may be challenging, due to the volume and velocity in which such data is being produced, such complex reasoning is necessary in scenarios where agents need to discover potential problems and this cannot be done with simple stream processing techniques. Stream Reasoners aim at bridging this gap between reasoning and stream processing and LASER is such a stream reasoner designed to analyse and perform complex reasoning over streams of data. It is based on LARS, a rule-based logical language extending Answer Set Programming, and it has shown better runtime results than other state-of-the-art stream reasoning systems. Nevertheless, for high levels of data throughput even LASER may be unable to compute answers in a timely fashion. In this paper, we study whether Convolutional and Recurrent Neural Networks, which have shown to be particularly well-suited for time series forecasting and classification, can be trained to approximate reasoning with LASER, so that agents can benefit from their high processing speed.

cs.AI↗

arXiv2019

A Syntactic Operator for Forgetting that Satisfies Strong Persistence

Whereas the operation of forgetting has recently seen a considerable amount of attention in the context of Answer Set Programming (ASP), most of it has focused on theoretical aspects, leaving the practical issues largely untouched. Recent studies include results about what sets of properties operators should satisfy, as well as the abstract characterization of several operators and their theoretical limits. However, no concrete operators have been investigated. In this paper, we address this issue by presenting the first concrete operator that satisfies strong persistence - a property that seems to best capture the essence of forgetting in the context of ASP - whenever this is possible, and many other important properties. The operator is syntactic, limiting the computation of the forgetting result to manipulating the rules in which the atoms to be forgotten occur, naturally yielding a forgetting result that is close to the original program. This paper is under consideration for acceptance in TPLP.

cs.AI↗

arXiv2017

Phase-locked multi-terahertz electric fields exceeding 13 MV/cm at 190 kHz repetition rate

We demonstrate a compact source of energetic and phase-locked multi-terahertz pulses at a repetition rate of 190 kHz. Difference frequency mixing of the fundamental output of an Yb:KGW amplifier with the idler of an optical parametric amplifier in GaSe and LiGaS2 crystals yields a passively phase-locked train of waveforms tunable between 12 and 42 THz. The shortest multi-terahertz pulses contain 1.8 oscillation cycles within the intensity FWHM. Pulse energies of up to 0.16 μJ and peak electric fields of 13 MV/cm are achieved. Electro-optic sampling reveals a phase stability better than 0.1 $π$ over multiple hours combined with free CEP tunability. The scalable scheme opens the door to strong-field terahertz optics at unprecedented repetition rates.

physics.optics↗

arXiv2017

Reactive Multi-Context Systems: Heterogeneous Reasoning in Dynamic Environments

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.

cs.LO↗

arXiv2017

When You Must Forget: beyond strong persistence when forgetting in answer set programming

Among the myriad of desirable properties discussed in the context of forgetting in Answer Set Programming (ASP), strong persistence naturally captures its essence. Recently, it has been shown that it is not always possible to forget a set of atoms from a program while obeying this property, and a precise criterion regarding what can be forgotten has been presented, accompanied by a class of forgetting operators that return the correct result when forgetting is possible. However, it is an open question what to do when we have to forget a set of atoms, but cannot without violating this property. In this paper, we address this issue and investigate three natural alternatives to forget when forgetting without violating strong persistence is not possible, which turn out to correspond to the different possible relaxations of the characterization of strong persistence. Additionally, we discuss their preferable usage, shed light on the relation between forgetting and notions of relativized equivalence established earlier in the context of ASP, and present a detailed study on their computational complexity.

cs.AI↗

arXiv2015

On Minimal Change in Evolving Multi-Context Systems (Preliminary Report)

Managed Multi-Context Systems (mMCSs) provide a general framework for integrating knowledge represented in heterogeneous KR formalisms. However, mMCSs are essentially static as they were not designed to run in a dynamic scenario. Some recent approaches, among them evolving Multi-Context Systems (eMCSs), extend mMCSs by allowing not only the ability to integrate knowledge represented in heterogeneous KR formalisms, but at the same time to both react to, and reason in the presence of commonly temporary dynamic observations, and evolve by incorporating new knowledge. The notion of minimal change is a central notion in dynamic scenarios, specially in those that admit several possible alternative evolutions. Since eMCSs combine heterogeneous KR formalisms, each of which may require different notions of minimal change, the study of minimal change in eMCSs is an interesting and highly non-trivial problem. In this paper, we study the notion of minimal change in eMCSs, and discuss some alternative minimal change criteria.

cs.AI↗

arXiv2015

Towards Efficient Evolving Multi-Context Systems (Preliminary Report)

Managed Multi-Context Systems (mMCSs) provide a general framework for integrating knowledge represented in heterogeneous KR formalisms. Recently, evolving Multi-Context Systems (eMCSs) have been introduced as an extension of mMCSs that add the ability to both react to, and reason in the presence of commonly temporary dynamic observations, and evolve by incorporating new knowledge. However, the general complexity of such an expressive formalism may simply be too high in cases where huge amounts of information have to be processed within a limited short amount of time, or even instantaneously. In this paper, we investigate under which conditions eMCSs may scale in such situations and we show that such polynomial eMCSs can be applied in a practical use case.

cs.AI↗

arXiv2011

Query-driven Procedures for Hybrid MKNF Knowledge Bases

Hybrid MKNF knowledge bases are one of the most prominent tightly integrated combinations of open-world ontology languages with closed-world (non-monotonic) rule paradigms. The definition of Hybrid MKNF is parametric on the description logic (DL) underlying the ontology language, in the sense that non-monotonic rules can extend any decidable DL language. Two related semantics have been defined for Hybrid MKNF: one that is based on the Stable Model Semantics for logic programs and one on the Well-Founded Semantics (WFS). Under WFS, the definition of Hybrid MKNF relies on a bottom-up computation that has polynomial data complexity whenever the DL language is tractable. Here we define a general query-driven procedure for Hybrid MKNF that is sound with respect to the stable model-based semantics, and sound and complete with respect to its WFS variant. This procedure is able to answer a slightly restricted form of conjunctive queries, and is based on tabled rule evaluation extended with an external oracle that captures reasoning within the ontology. Such an (abstract) oracle receives as input a query along with knowledge already derived, and replies with a (possibly empty) set of atoms, defined in the rules, whose truth would suffice to prove the initial query. With appropriate assumptions on the complexity of the abstract oracle, the general procedure maintains the data complexity of the WFS for Hybrid MKNF knowledge bases. To illustrate this approach, we provide a concrete oracle for EL+, a fragment of the light-weight DL EL++. Such an oracle has practical use, as EL++ is the language underlying OWL 2 EL, which is part of the W3C recommendations for the Semantic Web, and is tractable for reasoning tasks such as subsumption. We show that query-driven Hybrid MKNF preserves polynomial data complexity when using the EL+ oracle and WFS.

cs.AI↗