21st International Conference on
Applications of Declarative Programming
and Knowledge Management
19th to 22nd September 2017
INAP is a communicative conference for intensive discussion of applications of important technologies around declarative programming, constraint problem solving, and related computing paradigms. It comprehensively covers the impact of data and knowledge engineering, programmable logic solvers in the internet society, its underlying technologies, and leading edge applications in industry, commerce, government, and societal services.
Previous INAP conferences have been held in Japan, Germany, Portugal, and Austria.
We invite high quality contributions on different aspects of declarative programming, data and knowledge management and constraint processing, as well as their use for distributed systems and the web, including, but not limited to the following areas (the order does not reflect any priorities):
deductive databases, rule bases,
decision support, expert systems,
logic programming, nonmonotonic reasoning,
agents and concurrent engineering,
ontologies, semantic web, internet of things
(extensions of) constraint (logic) programming
tools for academic and industrial use,
knowledge-based web services,
logic solvers and applications
Frank Richter, Daniel Gall and Thom Frühwirth
In the abstract operational semantics of Constraint Handling Rules (CHR), propagation rules, i.e. rules that only add information, can be applied again and again. This trivial non-termination is typically avoided by a propagation history. A more declarative approach are persistent constraints. Constraints that are introduced by propagation rules are made persistent and cannot be removed. Now a propagation rule is only applied, if its derived constraints are not already persistent.
The operational semantics with persistent constraints Ω! differs substantially from other operational semantics, hence the standard confluence test cannot be applied. In this paper, a confluence test for Ω! is presented. Since Ω! breaks monotonicity of CHR, a weaker property is established that is shown to suffice for a decidable confluence criterion for terminating Ω! programs. The confluence test is implemented using a source to source transformation.
Ke Liu, Sven Löffler and Petra Hofstedt
Parallel constraint solving is a promising way to enhance the performance of constraint programming. Yet, current solutions for parallel constraint solving ignore the importance of hypergraph decomposition when mapping constraints onto cores. This paper explains why and how the hypergraph decomposition can be employed to relatively evenly distribute workload in parallel constraint solving. We present our dedicated hypergraph decomposition method det-k-CP for parallel constraint solving. The result of det-k-CP, which conforms with four conditions of hypertree decomposition, can be used to allocate constraints of a given constraint network to cores for parallel constraint solving. Our benchmark evaluations have shown that det-k-CP can relatively evenly decompose a hypergraph for specific scale of constraint networks. Besides, we obtained competitive execution time as long as the hypergraphs are sufficiently simple.
The programming paradigm of constrained objects is a declarative variant of the object-oriented paradigm wherein objects define the structure of a system and declarative constraints (rather than imperative methods) define its behavior. Constrained objects have many uses in the engineering domain and computation in this paradigm is essentially constraint solving. This paper is concerned with an extension of constrained objects called temporal constrained objects, which are especially appropriate for modeling dynamical systems. The main extensions are series variables and metric temporal operators to declaratively specify time-varying behavior. The language TCOB exemplifies this paradigm and the execution of TCOB programs consists of constraint solving within a time-based simulation framework. One of the challenges in TCOB is identifying errors owing both to the complexity of programs and the underlying constraint solving methods. We address this problem by extracting a run-time trace of the execution of a TCOB program and providing an analysis of the cause of error. The run-time trace also serves as a basis, in many cases, for constructing a finite-state machine which in turn can be used for "model-checking" properties of the system. The paper also presents abstraction techniques for dealing with simulations that result in large state spaces.
Pedro Roque, Vasco Pedro and Salvador Abreu
Applying parallelism to constraint solving seems a promising approach and it has been done with varying degrees of success. Early attempts to parallelize constraint propagation, which constitutes the core of traditional interleaved propagation and search constraint solving, were hindered by its essentially sequential nature.
Recently, parallelization efforts have focussed mainly on the search part of constraint solving, as well as on local-search based solving. Lately, a particular source of parallelism has become pervasive, in the guise of GPUs, able to run thousands of parallel threads, and they have naturally drawn the attention of researchers in parallel constraint solving.
We address challenges faced when using multiple devices for constraint solving, especially GPUs, such as deciding on the appropriate level of parallelism to employ, load balancing and inter-device communication, and present our current solutions.
Lorenz Leutgeb and Antonius Weinzierl
Answer-Set Programming (ASP) is a well-known and expressive logic programming paradigm based on efficient solvers. State-of-the-art ASP solvers require the ASP program to be variable-free, they thus ground the program upfront at the cost of a potential exponential explosion of the space required. Lazy-grounding, where solving and grounding are interleaved, circumvents this grounding bottleneck, but the resulting solvers lack many important search techniques and optimizations. The recently introduced ASP solver Alpha combines lazy-grounding with conflict-driven nogood learning (CDNL), a core technique of efficient ASP solving. This work presents how techniques for efficient propagation can be lifted to the lazy-grounding setting. The Alpha solver and its components are presented and detailed benchmarks comparing Alpha to other ASP solvers demonstrate the feasibility of this approach.
Emmanuelle-Anna Dietz, Steffen Hölldobler and Richard Mörbitz
It seems widely accepted that human reasoning cannot be modeled by means of classical logic. Psychological experiments have repeatedly shown that participants' answers systematically deviate from the classical logically correct answers. Recently, a new computational logic approach to modeling human syllogistic reasoning has been developed which seems to perform better than other state-of-the-art cognitive theories. We take this approach as starting point, yet instead of trying to model the human reasoner, we aim at identifying clusters of reasoners, which can be characterized by reasoning principles or by heuristic strategies.
Processing programs as data is one of the successes of functional and logic programming. Higher-order functions, as program-processing programs are called in functional programming, and meta-programs, as they are called in logic programming, are widespread declarative programming techniques. In logic programming, there is a gap between the meta-programming practice and its theory: Meta-programming's formalisations do not explicitly address meta-programming's impredicativity and are cumbersome. This article aims at overcoming this unsatisfactory situation by discussing the relevance of impredicativity to meta-programming and by revisiting Ambivalent Logic's syntax and model theory. The impredicative language and model theory proposed in this article are conservative extensions of the language and model theory of first-order logic.
Thom Frühwirth (Talk by Daniel Gall)
In previous work we added justifications to Constraint Handling Rules (CHR) to enable logical retraction of constraints for dynamic algorithms. We presented a straightforward source-to-source transformation to implement this conservative extension. In this companion paper, we improve the performance of the transformation.
We discuss its worst-case time complexity in general. Then we perform experiments. We benchmark the dynamic problem of maintaining shortest paths under addition and retraction of paths. The results validate our complexity considerations.
Gonçalo Carnaz, Roy Bayot, Vitor Beires Nogueira, Teresa Gonçalves and Paulo Quaresma
The Agatha project aims to develop an intelligent system that resorts to open sources (video, audio and text) of information for surveillance and crime control. Named-entity recognition combined with ontologies is the approach followed for the textual sources. This work describes the theoretical basis together with the system implementations for the text analysis component of the Agatha framework.
The Push Method for Bottom-Up Evaluation in deductive databases was previously defined as a translation from Datalog to C++. Performance tests on some benchmarks from the OpenRuleBench collection gave very encouraging results. However, most of the systems used for comparison compile the query into code of an abstract machine and then use an emulator for this code. Therefore, runtimes cannot be directly compared. In this paper, we propose an abstract machine for bottom-up evaluation of Datalog based on the Push Method. This also helps to clarify some optimizations we previously expected from the C++ compiler. Since the interpreted code of the abstract machine must do something useful "standalone", we also consider declarative output with templates.
Min Fang and Hans Tompits
In recent years, different methods for supporting the development of answer-set programming (ASP) code have been introduced. During such a development process, often it would be desirable to have a natural-language representation of answer sets, e.g., when dealing with domain experts unfamiliar with ASP.
In this paper, we address this point and provide an approach for such a representation, defined in terms of a controlled natural language (CNL), which in turn relies on the annotation language LANA for the specification of meta-information for answer-set programs. Our approach has been implemented as an Eclipse plug-in for SeaLion, a dedicated IDE for ASP.
Authors are invited to submit long papers (no longer than 15 pages) or short papers (no longer than 6 pages) in the following categories: technical papers; application papers; system descriptions.
We also encourage submissions of PhD students (no longer than 6 pages), submissions describing historical aspects of declarative and logic programming, as well as personal reminiscences about their early days.
Submissions must be unpublished original work and not submitted for publication elsewhere. However, work that already appeared in informally published workshop proceedings may be submitted too. All submissions must be in PDF format using LaTeX2e and the Springer llncs.cls class file. Paper submission is electronic via the Easychair submission system.
All accepted papers will be published in a technical report. As for previous joint INAP/WLP/WFLP events, it is planned to publish selected papers in a post-conference proceedings volume in the Springer Lecture Notes in Artificial Intelligence (LNAI) series.
The previous proceedings have been published as LNAI 5437, 6547, 7773, and 8439.
Notification of Authors
24th July 2017
15th August 2017
Conference & Workshop
19th to 22nd September 2017
Dietmar Seipel (University of Würzburg, Germany)
Salvador Abreu (Universidade de Évora, Portugal)
Slim Abdennadher (German University of Cairo, Egypt)
Salvador Pinto Abreu (Universidade de Évora, Portugal)
Molham Aref (Logic Blox Inc, Atlanta, USA)
Chitta Baral (Arizona State University, Tempe, USA)
Joachim Baumeister (University of Würzburg)
Stefan Brass (University of Halle, Germany)
François Bry (Ludwig-Maximilian University of Munich, Germany)
Philippe Codognet (UPMC, Paris, France)
Vitor Santos Costa (University of Porto, Portugal)
Agostino Dovier (University of Udine, Italy)
Thomas Eiter (Vienna University of Technology, Austria)
Thom Fruehwirth (University of Ulm, Germany)
Parke Godfrey (York University, Toronto, Canada)
Gopal Gupta (UT, Dallas, USA)
Michael Hanus (University of Kiel, Germany)
Jorge Lobo (ICREA and Universitat Pompeu Fabra, Barcelona, Spain)
Grzegorz J. Nalepa (AGH University, Kraków, Poland)
Vitor Nogueira (Universidade de Évora, Portugal)
Enrico Pontelli (New Mexico State University, Las Cruces, USA)
Dietmar Seipel (University of Würzburg, Germany)
Hans Tompits (Vienna University of Technology, Austria)
Masanobu Umeda (Kyushu Institute of Technology, Japan)
Universität Würzburg – Franconia – Bavaria – Germany
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Part of Declare'17