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We present a modified ground-and-solve approach based on the clingo Answer Set Programming (ASP) system to perform non-monotonic spatial reasoning tasks, Clingo2DSR. Our system is distinct from previous research integrating ASP with space in that it deals with complex real-world data and numerous time steps. Clingo2DSR is composed of (i) an input language defining spatial entities, functions, and relations; (ii) an external geometry database for performing spatial computations and checking spatial constraints; and (iii) a theory-based solving approach coupling symbolic ASP with external sources for sound and fast model search. We demonstrate our system on three real building models, in the context of architectural design, submitted for analyses and queries where spatial components play an important role. [ABSTRACT FROM AUTHOR]

Automated legal reasoning and its application in smart contracts and automated decisions are increasingly attracting interest. In this context, ethical and legal concerns make it necessary for automated reasoners to justify in human-understandable terms the advice given. Logic Programming, specially Answer Set Programming, has a rich semantics and has been used to very concisely express complex knowledge. However, modelling discretionality to act and other vague concepts such as ambiguity cannot be expressed in top-down execution models based on Prolog, and in bottom-up execution models based on ASP the justifications are incomplete and/or not scalable. We propose to use s(CASP), a top-down execution model for predicate ASP, to model vague concepts following a set of patterns. We have implemented a framework, called s(LAW), to model, reason, and justify the applicable legislation and validate it by translating (and benchmarking) a representative use case, the criteria for the admission of students in the "Comunidad de Madrid". [ABSTRACT FROM AUTHOR]

The Operating Room Scheduling (ORS) problem deals with the optimization of daily operating room surgery schedules. It is a challenging problem subject to many constraints, like to determine the starting time of different surgeries and allocating the required resources, including the availability of beds in different units. In the past years, Answer Set Programming (ASP) has been successfully employed for addressing and solving the ORS problem. Despite its importance, due to the inherent difficulty of retrieving real data, all the analyses on ORS ASP encodings have been performed on synthetic data so far. In this paper, first we present a new, improved ASP encoding for the ORS problem. Then, we deal with the real case of ASL1 Liguria, an Italian health authority operating through three hospitals, and present adaptations of the ASP encodings to deal with the real-world data. Further, we analyse the resulting encodings on hospital scheduling data by ASL1 Liguria. Results on some scenarios show that the ASP solutions produce satisfying schedules also when applied to such challenging, real data. 1 [ABSTRACT FROM AUTHOR]

Weighted knowledge bases for description logics with typicality under a 'concept-wise' multi-preferential semantics provide a logical interpretation of MultiLayer Perceptrons. In this context, Answer Set Programming (ASP) has been shown to be suitable for addressing defeasible reasoning in the finitely many-valued case, providing a |$\varPi ^{p}_{2}$| upper bound on the complexity of the problem, nonetheless leaving unknown the exact complexity and only providing a proof-of-concept implementation. This paper fulfills the lack by providing a |${P^{NP[log]}}$| -completeness result and new ASP encodings that deal with both acyclic and cyclic weighted knowledge bases with large search spaces, as assessed empirically on synthetic test cases. The encodings are used to empower a reasoner for computing solutions and answering queries, possibly interacting with ASP Chef for obtaining an interactive visualization. [ABSTRACT FROM AUTHOR]

Explainable artificial intelligence (XAI) aims at addressing complex problems by coupling solutions with reasons that justify the provided answer. In the context of Answer Set Programming (ASP) the user may be interested in linking the presence or absence of an atom in an answer set to the logic rules involved in the inference of the atom. Such explanations can be given in terms of directed acyclic graphs (DAGs). This article reports on the advancements in the development of the XAI system xASP by revising the main foundational notions and by introducing new ASP encodings to compute minimal assumption sets, explanation sequences, and explanation DAGs. DAGs are shown to the user in an interactive form via the xASP navigator application, also introduced in this work. [ABSTRACT FROM AUTHOR]

We present a novel implementation of Defeasible Deontic Logic as an Answer Set Programming meta-program, and we evaluate the performance of the implementation against a recent set of benchmarks. [ABSTRACT FROM AUTHOR]

Marketplaces bring together products from multiple providers and automatically manage orders that involve several suppliers. We document the use of Answer Set Programming to automatically choose products from various warehouses within a marketplace network to fulfill a specified order. The proposed solution seamlessly adapts to various objective functions utilized at different stages of order management, leading to cost savings for customers and simplifying logistics for both the marketplace and its suppliers. [ABSTRACT FROM AUTHOR]

Answer Set Programming (ASP) is a declarative language oriented towards solving complex combinatorial problems. In fact, ASP has been successfully used to address problems in various academic and industrial domains. The success of ASP can be attributed to its concise syntax, intuitive semantics, and the availability of several efficient solvers based on the Conflict-Driven Clause Learning (CDCL) algorithm. This paper details the design and implementation of contemporary CDCL solvers, emphasizing both algorithmic descriptions and their effective and efficientimplementation. [ABSTRACT FROM AUTHOR]

Assurance cases offer a structured way to present arguments and evidence for certification of systems where safety and security are critical. However, creating and evaluating these assurance cases can be complex and challenging, even for systems of moderate complexity. Therefore, there is a growing need to develop new automation methods for these tasks. While most existing assurance case tools focus on automating structural aspects, they lack the ability to fully assess the semantic coherence and correctness of the assurance arguments. In prior work, we introduced the Assurance 2.0 framework that prioritizes the reasoning process, evidence utilization, and explicit delineation of counter-claims (defeaters) and counter-evidence. In this paper, we present our approach to enhancing Assurance 2.0 with semantic rule-based analysis capabilities using common-sense reasoning and answer set programming solvers, specifically s(CASP). By employing these analysis techniques, we examine the unique semantic aspects of assurance cases, such as logical consistency, adequacy, indefeasibility, etc. The application of these analyses provides both system developers and evaluators with increased confidence about the assurance case. [ABSTRACT FROM AUTHOR]

We are interested in automating reasoning with and about study regulations, catering to various stakeholders, ranging from administrators, over faculty, to students at different stages. Our work builds on an extensive analysis of various study programs at the University of Potsdam. The conceptualization of the underlying principles provides us with a formal account of study regulations. In particular, the formalization reveals the properties of admissible study plans. With these at end, we propose an encoding of study regulations in Answer Set Programming that produces corresponding study plans. Finally, we show how this approach can be extended to a generic user interface for exploring study plans. [ABSTRACT FROM AUTHOR]

Answer set programming (ASP) has demonstrated its potential as an effective tool for concisely representing and reasoning about real-world problems. In this paper, we present an application in which ASP has been successfully used in the context of dynamic traffic distribution for urban networks, within a more general framework devised for solving such a real-world problem. In particular, ASP has been employed for the computation of the "optimal" routes for all the vehicles in the network. We also provide an empirical analysis of the performance of the whole framework, and of its part in which ASP is employed, on two European urban areas, which shows the viability of the framework and the contribution ASP can give. [ABSTRACT FROM AUTHOR]

The dominating set reconfiguration problem is defined as determining, for a given dominating set problem and two among its feasible solutions, whether one is reachable from the other via a sequence of feasible solutions subject to a certain adjacency relation. This problem is PSPACE-complete in general. The concept of the dominating set is known to be quite useful for analyzing wireless networks, social networks, and sensor networks. We develop an approach to solve the dominating set reconfiguration problem based on answer set programming (ASP). Our declarative approach relies on a high-level ASP encoding, and both the grounding and solving tasks are delegated to an ASP-based combinatorial reconfiguration solver. To evaluate the effectiveness of our approach, we conduct experiments on a newly created benchmark set. [ABSTRACT FROM AUTHOR]

This paper proposes a new methodology for early validation of high-level requirements on cyber-physical systems with the aim of improving their quality and, thus, lowering chances of specification errors propagating into later stages of development where it is much more expensive to fix them. The paper presents a transformation of a real-world requirements specification of a medical device—the Patient-Controlled Analgesia (PCA) Pump—into an Event Calculus model that is then evaluated using Answer Set Programming and the s(CASP) system. The evaluation under s(CASP) allowed deductive as well as abductive reasoning about the specified functionality of the PCA pump on the conceptual level with minimal implementation or design dependent influences and led to fully automatically detected nuanced violations of critical safety properties. Further, the paper discusses scalability and non-termination challenges that had to be faced in the evaluation and techniques proposed to (partially) solve them. Finally, ideas for improving s(CASP) to overcome its evaluation limitations that still persist as well as to increase its expressiveness are presented. [ABSTRACT FROM AUTHOR]

Answer set programming is a well-understood and established problem-solving and knowledge representation paradigm. It has become more prominent amongst a wider audience due to its multiple applications in science and industry. The constant development of advanced programming and modeling techniques extends the toolset for developers and users regularly. This paper compiles and demonstrates different techniques to reuse logic program parts (multi-shot) by solving the arcade game snake. This game is particularly interesting because a victory can be assured by solving the NP-hard problem of Hamiltonian Cycles. We will demonstrate five hands-on implementations in clingo and compare their performance in an empirical evaluation. In addition, our implementation utilizes clingraph to generate a simple yet informative image representation of the game's progress. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive survey of the progress made in the field, spanning from its foundational concepts to its application in challenging planning domains. The survey delves into the strengths and weaknesses of answer set planning, exploring its typical applications and outlining key challenges for future research. One notable contribution of this paper is the integration of answer set programming with mixed integer programming, a widely applied paradigm. Theoretical results establish translations from non-disjunctive logic programs to linear constraints in mixed integer programming, aligning solutions to these constraints with the answer sets of the original programs. This integration forms the basis for an extended answer set programming language that incorporates linear constraints as a primitive, facilitating more concise problem encodings. To validate the theoretical findings, a prototype system has been implemented, utilizing a state-of-the-art mixed integer programming solver to compute answer sets. Experimental results showcase the effectiveness of this approach in addressing optimization problems and those with variables spanning large domains. This research not only contributes to the theoretical foundations of answer set planning but also introduces a practical and efficient approach for solving complex problems in various domains. [ABSTRACT FROM AUTHOR]

Single-cell transcriptomic studies of differentiating systems allow meaningful understanding, especially in human embryonic development and cell fate determination. We present an innovative method aimed at modeling these intricate processes by leveraging scRNAseq data from various human developmental stages. Our implemented method identifies pseudo-perturbations, since actual perturbations are unavailable due to ethical and technical constraints. By integrating these pseudo-perturbations with prior knowledge of gene interactions, our framework generates stage-specific Boolean networks (BNs). We apply our method to medium and late trophectoderm developmental stages and identify 20 pseudo-perturbations required to infer BNs. The resulting BN families delineate distinct regulatory mechanisms, enabling the differentiation between these developmental stages. We show that our program outperforms existing pseudo-perturbation identification tool. Our framework contributes to comprehending human developmental processes and holds potential applicability to diverse developmental stages and other research scenarios. [ABSTRACT FROM AUTHOR]

The SOSAc-Reasoner is a commonsense reasoning engine, implemented using Answer Set Programming. It is designed to automatically generate IoT context knowledge, representing the capabilities of system devices, from a simple smart scenario description. The inference engine is fed with knowledge about device types and generates knowledge according to two ontologies derived from the SOSA (Sensor, Observation, Sample, and Actuator) ontology. The SOSAc-Reasoner comprises two ASP rule modules: the basic and advanced inference modules, which perform reasoning with different objectives. Implemented with Potassco, the SOSAc-Reasoner effectively generates context knowledge within a reasonable timeframe. This significantly facilitates the task of modeling a highly valuable type of knowledge in intelligent environments, a task that traditionally involves manual efforts, is prone to errors, and consumes a significant amount of time.

In temporal extensions of answer set programming (ASP) based on linear time, the behavior of dynamic systems is captured by sequences of states. While this representation reflects their relative order, it abstracts away the specific times associated with each state. However, timing constraints are important in many applications like, for instance, when planning and scheduling go hand in hand. We address this by developing a metric extension of linear-time temporal equilibrium logic, in which temporal operators are constrained by intervals over natural numbers. The resulting Metric Equilibrium Logic (MEL) provides the foundation of an ASP-based approach for specifying qualitative and quantitative dynamic constraints. To this end, we define a translation of metric formulas into monadic first-order formulas and give a correspondence between their models in MEL and Monadic Quantified Equilibrium Logic, respectively. Interestingly, our translation provides a blue print for implementation in terms of ASP modulo difference constraints. [ABSTRACT FROM AUTHOR]

Payroll management is a critical business task that is subject to a large number of rules, which vary widely between companies, sectors, and countries. Moreover, the rules are often complex and change regularly. Therefore, payroll management systems must be flexible in design. In this paper, we suggest an approach based on a flexible answer set programming (ASP) model and an easy-to-read tabular representation based on the decision model and notation standard. It allows HR consultants to represent complex rules without the need for a software engineer and to ultimately design payroll systems for a variety of different scenarios. We show how the multi-shot solving capabilities of the clingo ASP system can be used to reach the performance that is necessary to handle real-world instances. [ABSTRACT FROM AUTHOR]

Epistemic logic programs (ELPs), extend answer set programming (ASP) with epistemic operators. The semantics of such programs is provided in terms of world views , which are sets of belief sets, that is, syntactically, sets of sets of atoms. Different semantic approaches propose different characterizations of world views. Recent work has introduced semantic properties that should be met by any semantics for ELPs, like the Epistemic Splitting Property , that, if satisfied, allows to modularly compute world views in a bottom-up fashion, analogously to "traditional" ASP. We analyze the possibility of changing the perspective, shifting from a bottom-up to a top-down approach to splitting. We propose a basic top-down approach, which we prove to be equivalent to the bottom-up one. We then propose an extended approach, where our new definition: (i) is provably applicable to many of the existing semantics; (ii) operates similarly to "traditional" ASP; (iii) provably coincides under any semantics with the bottom-up notion of splitting at least on the class of Epistemically Stratified Programs (which are, intuitively, those where the use of epistemic operators is stratified); (iv) better adheres to common ASP programming methodology. [ABSTRACT FROM AUTHOR]

Search/optimization problems are plentiful in scientific and engineering domains. Artificial intelligence has long contributed to the development of search algorithms and declarative programming languages geared toward solving and modeling search/optimization problems. Automated reasoning and knowledge representation are the subfields of AI that are particularly vested in these developments. Many popular automated reasoning paradigms provide users with languages supporting optimization statements: answer set programming or MaxSAT or min-one, to name a few. These paradigms vary significantly in their languages and in the ways they express quality conditions on computed solutions. Here we propose a unifying framework of so-called weight systems that eliminates syntactic distinctions between paradigms and allows us to see essential similarities and differences between optimization statements provided by paradigms. This unifying outlook has significant simplifying and explanatory potential in the studies of optimization and modularity in automated reasoning and knowledge representation. It also supplies researchers with a convenient tool for proving the formal properties of distinct frameworks; bridging these frameworks; and facilitating the development of translational solvers. [ABSTRACT FROM AUTHOR]

The problem of scheduling pre-operative assessment clinic (PAC) consists of assigning patients to a day for the exams needed before a surgical procedure, taking into account patients with different priority levels, due dates and operators availability. Realizing a satisfying schedule is of upmost importance for a hospital, since delay in PAC can cause delay in the subsequent phases, thus lowering patients' satisfaction. In this paper, we propose a two-phase solution to the PAC problem: in the first phase, patients are assigned to a day taking into account a default list of exams; then, in the second phase, having the actual list of exams needed by each patient, we use the results of the first phase to assign a starting time to each exam. We first present a mathematical formulation for both problems. Further, we present a solution where modeling and solving are done via answer set programming. We then introduce a rescheduling solution that may come into play when the scheduling solution cannot be applied fully. Experiments employing synthetic benchmarks on both scheduling and rescheduling show that both solutions provide satisfying results in short time. We finally show the implementation and usage of a web application that allows to run our scheduling solution and analyze the results graphically in a transparent way. [ABSTRACT FROM AUTHOR]

When designing a system at the Electronic System Level (ESL), designers are confronted with a very large number of design decisions, each affecting the characteristics of the resulting system. Simultaneously, the demands for the system's performance, reliability, and energy consumption have increased drastically. Design Space Exploration (DSE) aims to facilitate this complex task by automating the system synthesis and traversing the design space autonomously. Previous studies on DSE have mainly considered fixed architectures with a fixed set of hardware components only. In the paper at hand, we overcome this limitation to allow for a higher degree of freedom in the design of a multiprocessor system. Instead of a fixed architecture as input, we are using a resource library containing resource types whose instances can then be arbitrarily placed and connected. More specifically, we enable the exploration of the types, the number, and the positions of required processing-type instances in a grid-based topology template in addition to deciding on the remaining system synthesis tasks, namely, resource allocation, task binding, routing, and scheduling. We provide an extensible framework, based on Answer Set Programming (ASP) modulo Theories (ASPmT), for generating system architectures fulfilling predefined constraints. Our studies show that this higher degree of freedom, originating from fewer restrictions regarding the architecture, leads to an increased complexity of the problem. In extensive experiments, we show scalability trends for a set of parameters, demonstrating the capabilities and limits of our approach. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Answer set programming (ASP) is a popular declarative programming language for solving hard combinatorial problems. Although ASP has gained widespread acceptance in academic and industrial contexts, there are certain user groups who may find it more advantageous to employ a higher-level language that closely resembles natural language when specifying ASP programs. In this paper, we propose a novel tool, called CNL2ASP, for translating English sentences expressed in a controlled natural language (CNL) form into ASP. In particular, we first provide a definition of the type of sentences allowed by our CNL and their translation as ASP rules and then exemplify the usage of the CNL for the specification of both synthetic and real-world combinatorial problems. Finally, we report the results of an experimental analysis conducted on the real-world problems to compare the performance of automatically generated encodings with the ones written by ASP practitioners, showing that our tool can obtain satisfactory performance on these benchmarks. [ABSTRACT FROM AUTHOR]

Detecting sets of relevant patterns from a given dataset is an important challenge in data mining. The relevance of a pattern, also called utility in the literature, is a subjective measure and can be actually assessed from very different points of view. Rule-based languages like Answer Set Programming (ASP) seem well suited for specifying user-provided criteria to assess pattern utility in a form of constraints; moreover, declarativity of ASP allows for a very easy switch between several criteria in order to analyze the dataset from different points of view. In this paper, we make steps toward extending the notion of High-Utility Pattern Mining; in particular, we introduce a new framework that allows for new classes of utility criteria not considered in the previous literature. We also show how recent extensions of ASP with external functions can support a fast and effective encoding and testing of the new framework. To demonstrate the potential of the proposed framework, we exploit it as a building block for the definition of an innovative method for predicting ICU admission for COVID-19 patients. Finally, an extensive experimental activity demonstrates both from a quantitative and a qualitative point of view the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

A core part of the rehabilitation scheduling process consists of planning rehabilitation physiotherapy sessions for patients, by assigning proper operators to them in a certain time slot of a given day, taking into account several legal, medical, and ethical requirements and optimizations, for example, patient's preferences and operator's work balancing. Being able to efficiently solve such problem is of upmost importance, in particular after the COVID-19 pandemic that significantly increased rehabilitation's needs. In this paper, we present a two-phase solution to rehabilitation scheduling based on Answer Set Programming, which proved to be an effective tool for solving practical scheduling problems. We first present a general encoding and then add domain-specific optimizations. Results of experiments performed on both synthetic and real benchmarks, the latter provided by ICS Maugeri, show the effectiveness of our solution as well as the impact of our domain-specific optimizations. [ABSTRACT FROM AUTHOR]