237 research outputs found
aspcud: A Linux Package Configuration Tool Based on Answer Set Programming
We present the Linux package configuration tool aspcud based on Answer Set
Programming. In particular, we detail aspcud's preprocessor turning a CUDF
specification into a set of logical facts.Comment: In Proceedings LoCoCo 2011, arXiv:1108.609
Effectively Solving NP-SPEC Encodings by Translation to ASP
NP-SPEC is a language for specifying problems in NP in a declarative way. Despite the fact that the semantics of the language was given by referring to Datalog with circumscription, which is very close to ASP, so far the only existing implementations are by means of ECLiPSe Prolog and via Boolean satisfiability solvers. In this paper, we present translations from NP-SPEC into ASP, and provide an experimental evaluation of existing implementations and the proposed translations to ASP using various ASP solvers. The results show that translating to ASP clearly has an edge over the existing translation into SAT, which involves an intrinsic grounding process. We also argue that it might be useful to incorporate certain language constructs of NPSPEC into mainstream ASP
Controlled Natural Language Processing as Answer Set Programming: an Experiment
Most controlled natural languages (CNLs) are processed with the help of a
pipeline architecture that relies on different software components. We
investigate in this paper in an experimental way how well answer set
programming (ASP) is suited as a unifying framework for parsing a CNL, deriving
a formal representation for the resulting syntax trees, and for reasoning with
that representation. We start from a list of input tokens in ASP notation and
show how this input can be transformed into a syntax tree using an ASP grammar
and then into reified ASP rules in form of a set of facts. These facts are then
processed by an ASP meta-interpreter that allows us to infer new knowledge
Answer Set Programming Modulo `Space-Time'
We present ASP Modulo `Space-Time', a declarative representational and
computational framework to perform commonsense reasoning about regions with
both spatial and temporal components. Supported are capabilities for mixed
qualitative-quantitative reasoning, consistency checking, and inferring
compositions of space-time relations; these capabilities combine and synergise
for applications in a range of AI application areas where the processing and
interpretation of spatio-temporal data is crucial. The framework and resulting
system is the only general KR-based method for declaratively reasoning about
the dynamics of `space-time' regions as first-class objects. We present an
empirical evaluation (with scalability and robustness results), and include
diverse application examples involving interpretation and control tasks
Hybrid ASP-based multi-objective scheduling of semiconductor manufacturing processes (Extended version)
Modern semiconductor manufacturing involves intricate production processes
consisting of hundreds of operations, which can take several months from lot
release to completion. The high-tech machines used in these processes are
diverse, operate on individual wafers, lots, or batches in multiple stages, and
necessitate product-specific setups and specialized maintenance procedures.
This situation is different from traditional job-shop scheduling scenarios,
which have less complex production processes and machines, and mainly focus on
solving highly combinatorial but abstract scheduling problems. In this work, we
address the scheduling of realistic semiconductor manufacturing processes by
modeling their specific requirements using hybrid Answer Set Programming with
difference logic, incorporating flexible machine processing, setup, batching
and maintenance operations. Unlike existing methods that schedule semiconductor
manufacturing processes locally with greedy heuristics or by independently
optimizing specific machine group allocations, we examine the potentials of
large-scale scheduling subject to multiple optimization objectives.Comment: 17 pages, 1 figure, 4 listings, 1 table; a short version of this
paper is presented at the 18th European Conference on Logics in Artificial
Intelligence (JELIA 2023
Proteus: A Hierarchical Portfolio of Solvers and Transformations
In recent years, portfolio approaches to solving SAT problems and CSPs have
become increasingly common. There are also a number of different encodings for
representing CSPs as SAT instances. In this paper, we leverage advances in both
SAT and CSP solving to present a novel hierarchical portfolio-based approach to
CSP solving, which we call Proteus, that does not rely purely on CSP solvers.
Instead, it may decide that it is best to encode a CSP problem instance into
SAT, selecting an appropriate encoding and a corresponding SAT solver. Our
experimental evaluation used an instance of Proteus that involved four CSP
solvers, three SAT encodings, and six SAT solvers, evaluated on the most
challenging problem instances from the CSP solver competitions, involving
global and intensional constraints. We show that significant performance
improvements can be achieved by Proteus obtained by exploiting alternative
view-points and solvers for combinatorial problem-solving.Comment: 11th International Conference on Integration of AI and OR Techniques
in Constraint Programming for Combinatorial Optimization Problems. The final
publication is available at link.springer.co
Revisiting the Training of Logic Models of Protein Signaling Networks with a Formal Approach based on Answer Set Programming
A fundamental question in systems biology is the construction and training to
data of mathematical models. Logic formalisms have become very popular to model
signaling networks because their simplicity allows us to model large systems
encompassing hundreds of proteins. An approach to train (Boolean) logic models
to high-throughput phospho-proteomics data was recently introduced and solved
using optimization heuristics based on stochastic methods. Here we demonstrate
how this problem can be solved using Answer Set Programming (ASP), a
declarative problem solving paradigm, in which a problem is encoded as a
logical program such that its answer sets represent solutions to the problem.
ASP has significant improvements over heuristic methods in terms of efficiency
and scalability, it guarantees global optimality of solutions as well as
provides a complete set of solutions. We illustrate the application of ASP with
in silico cases based on realistic networks and data
Evaluation Techniques and Systems for Answer Set Programming: a Survey
Answer set programming (ASP) is a prominent knowledge representation and reasoning paradigm that found both industrial and scientific applications. The success of ASP is due to the combination of two factors: a rich modeling language and the availability of efficient ASP implementations. In this paper we trace the history of ASP systems, describing the key evaluation techniques and their implementation in actual tools
One More Decidable Class of Finitely Ground Programs
Abstract. When a logic program is processed by an answer set solver, the first task is to generate its instantiation. In a recent paper, Calimeri et el. made the idea of efficient instantiation precise for the case of disjunctive programs with function symbols, and introduced the class of âfinitely ground â programs that can be efficiently instantiated. Since that class is undecidable, it is important to find its large decidable subsets. In this paper, we introduce such a subsetâthe class of argument-restricted programs. It includes, in particular, all finite domain programs, Ï-restricted programs, and λ-restricted programs.
Flexible graph matching and graph edit distance using answer set programming
The graph isomorphism, subgraph isomorphism, and graph edit distance problems
are combinatorial problems with many applications. Heuristic exact and
approximate algorithms for each of these problems have been developed for
different kinds of graphs: directed, undirected, labeled, etc. However,
additional work is often needed to adapt such algorithms to different classes
of graphs, for example to accommodate both labels and property annotations on
nodes and edges. In this paper, we propose an approach based on answer set
programming. We show how each of these problems can be defined for a general
class of property graphs with directed edges, and labels and key-value
properties annotating both nodes and edges. We evaluate this approach on a
variety of synthetic and realistic graphs, demonstrating that it is feasible as
a rapid prototyping approach.Comment: To appear, PADL 202
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