Framework For Answer Set Programming Research Articles

From quantitative SBML models to Boolean networks

Modelling complex biological systems is necessary for their study and understanding. Biomodels is a repository of peer-reviewed models represented in the Systems Biology Markup Language (SBML). Most of these models are quantitative, but in some cases, qualitative models—such as Boolean networks (BNs)—are better suited. This paper focuses on the automatic transformation of quantitative SBML models to Boolean networks. We propose SBML2BN, a pipeline dedicated to this task. Our approach takes advantage of several SBML elements (reactions, rules, events) as well as a numerical simulation of the concentration of the species over time to constrain both the structure and the dynamics of the Boolean networks to synthesise. Finding all the BNs complying with the given structure and dynamics was formalised as an optimisation problem solved in the answer-set programming framework. We run SBML2BN on more than 200 quantitative SBML models, and we provide evidence that one can automatically construct Boolean networks which are compatible with the structure and the dynamics of an SBML model. In case the SBML model includes rules or events, we also show how the evaluation criteria are impacted when taking these elements into account.

An ASP-based solver for parametrized-difference revision

Abstract In the present article, a solver for the well-behaved concrete revision operators, named parametrized-difference (PD) revision operators, is described. The solver is developed by means of the powerful framework of answer set programming, which constitutes a contemporary modelling tool, oriented towards difficult search problems. Several useful functionalities are supported by the system, namely, dynamic PD revision, query-answering capabilities, integrity-constraints handling and revision under the closed-world assumption. The solver exhibits high performance in a plethora of revision instances, including hard ones of the SATLIB library; the fact that PD revision respects an intuitive relevance-sensitive principle, identified herein, contributes to this high performance. A graphical user interface provides easy interaction with the implemented system, making it a high-end standalone revision-tool for potential artificial intelligence applications.

A Unifying Approach for Nonmonotonic S4F, (Reflexive) Autoepistemic Logic, and Answer Set Programming

This paper presents a general strategy, bringing together some major types of nonmonotonic reasoning under a monotonic bimodal setting. Such formalisms are also of interest to the fields of knowledge representation and declarative programming. We exemplify the methodology, capturing minimal model reasoning that underlies nonmonotonicity over S4F first, but then we also show how to apply the technique to other nonmonotonic logics respectively based on the modal logics KD45 and SW5. We naturally succeed it, by modifying only the axioms of the underlying modal logic and show that it successfully works. The last two formalisms are also known as autoepistemic logic (AEL) and its reflexive extension (RAEL) in the given order: AEL is an important form of nonmonotonic reasoning, introduced by Robert C. Moore in order to allow an agent to reason about his own knowledge. Equilibrium logic (EL) is a general-purpose nonmonotonic reasoning formalism, proposed more recently by David Pearce as a semantical framework for answer set programming (ASP). The latter is an efficient declarative problem solving approach with lots of applications to science and technology. Fariñas et al. have embedded EL (and so ASP) into a monotonic bimodal logic. We take this work as an initiative and successfully apply a similar methodology to closely aligned nonmonotonic modal logics. We finally discuss the potential capability to subsume the epistemic extensions of ASP within our unified paradigm.

Dealing with Biology Systems in the Framework of Answer Set Programming

Abstract Reasoning about gene networks is essential from various perspectives, such as predicting side effects of drugs or explaining unusual cellular behavior. Because of the massive size of these gene networks, a biologist can only work on a small part of the network. Thus, there is an essential requirement for logical representations and automated reasoning on such networks to help biologists to understand genetic interactions. However, the knowledge about gene networks is always incomplete and sometimes not accurate. Hence, knowledge has to be continuously revised and extended. In this work, we propose an approach based on non-monotonic logic programming, and the framework of Answer Set Programming(ASP), to represent and handle gene networks. We show how to model reasoning, predict events, and explain observations in gene networks. Finally, we show how our approach is applied to represent and resolve the DNA double-strand breaks, which is one of the most severe genomic lesions.

Industrial Applications of Answer Set Programming

Automated problem solving in combination with declarative specifications of search-problems have shown to substantially improve the implementation and maintenance costs as well as the man-machine interaction of deployed industrial applications. The knowledge representation and reasoning (KRR) framework of answer set programming (ASP) offers a rich representation language and high performance solvers. Therefore, ASP has become very attractive for the representation and solving of search-problems both for academia and industry. This article focuses on the latest industrial applications of ASP. We do not only present successful applications of ASP but also describe the development process and the design of ASP programs in an industrial context. Finally, we discuss current approaches to tackle the most significant application challenges such as grounding and runtime improvements by heuristics.

A model building framework for answer set programming with external computations

AbstractAs software systems are getting increasingly connected, there is a need for equipping nonmonotonic logic programs with access to external sources that are possibly remote and may contain information in heterogeneous formats. To cater for this need,hexprograms were designed as a generalization of answer set programs with an API style interface that allows to access arbitrary external sources, providing great flexibility. Efficient evaluation of such programs however is challenging, and it requires to interleave external computation and model building; to decide when to switch between these tasks is difficult, and existing approaches have limited scalability in many real-world application scenarios. We present a new approach for the evaluation of logic programs with external source access, which is based on a configurable framework for dividing the non-ground program into possibly overlapping smaller parts called evaluation units. The latter will be processed by interleaving external evaluation and model building using an evaluation graph and a model graph, respectively, and by combining intermediate results. Experiments with our prototype implementation show a significant improvement compared to previous approaches. While designed forhex-programs, the new evaluation approach may be deployed to related rule-based formalisms as well.

Modelling gene and protein regulatory networks with Answer Set Programming

Recently, many approaches to model regulatory networks have been proposed in the systems biology domain. However, the task is far from being solved. In this paper, we propose an Answer Set Programming (ASP)-based approach to model interaction networks. We build a general ASP framework that describes the network semantics and allows modelling specific networks with little effort. ASP provides a rich and flexible toolbox that allows expanding the framework with desired features. In this paper, we tune our framework to mimic Boolean network behaviour and apply it to model the Budding Yeast and Fission Yeast cell cycle networks. The obtained steady states of these networks correspond to those of the Boolean networks.

Comparative uncertainty: theory and automation

In recent decades, qualitative approaches to probabilistic uncertainty have received more and more attention. We propose a characterisation of partial preference orders through a uniform axiomatic treatment of a variety of qualitative uncertainty notions. To this end, we prove a representation result that connects qualitative notions of partial uncertainty to their numerical counterparts. We describe an executable specification, in the declarative framework of Answer Set Programming, that constitutes the core engine for qualitative management of uncertainty. Some basic reasoning tasks are also identified.

Qualitative Uncertainty Orderings Revised

In recent decades, qualitative approaches to probabilistic uncertainty have been receiving wider and wider attention. We propose a new characterization of some of the most adopted partial preference orders by providing an uniform axiomatic treatment of a variety of qualitative uncertainty notions. We prove a representation result that connects qualitative notions of partial uncertainty to their numerical counterparts. We also describe an executable specification, in the declarative framework of Answer Set Programming, that constitutes the core engine for the qualitative management of uncertainty. Some basic reasoning tasks are also identified.