Logic Programming Paradigm Research Articles

Declarative probabilistic logic programming in discrete-continuous domains

Over the past three decades, the logic programming paradigm has been successfully expanded to support probabilistic modeling, inference and learning. The resulting paradigm of probabilistic logic programming (PLP) and its programming languages owes much of its success to a declarative semantics, the so-called distribution semantics. However, the distribution semantics is limited to discrete random variables only. While PLP has been extended in various ways for supporting hybrid, that is, mixed discrete and continuous random variables, we are still lacking a declarative semantics for hybrid PLP that not only generalizes the distribution semantics and the modeling language but also the standard inference algorithm that is based on knowledge compilation. We contribute the measure semantics together with the hybrid PLP language DC-ProbLog (where DC stands for distributional clauses) and its inference engine infinitesimal algebraic likelihood weighting (IALW). These have the original distribution semantics, standard PLP languages such as ProbLog, and standard inference engines for PLP based on knowledge compilation as special cases. Thus, we generalize the state of the art of PLP towards hybrid PLP in three different aspects: semantics, language and inference. Furthermore, IALW is the first inference algorithm for hybrid probabilistic programming based on knowledge compilation.

Evaluating Datalog Tools for Meta-reasoning over OWL 2 QL

Abstract Metamodeling is a general approach to expressing knowledge about classes and properties in an ontology. It is a desirable modeling feature in multiple applications that simplifies the extension and reuse of ontologies. Nevertheless, allowing metamodeling without restrictions is problematic for several reasons, mainly due to undecidability issues. Practical languages, therefore, forbid classes to occur as instances of other classes or treat such occurrences as semantically different objects. Specifically, meta-querying in SPARQL under the Direct Semantic Entailment Regime uses the latter approach, thereby effectively not supporting meta-queries. However, several extensions enabling different metamodeling features have been proposed over the last decade. This paper deals with the Metamodeling Semantics (MS) over OWL 2 QL and the Metamodeling Semantic Entailment Regime (MSER), as proposed in Lenzerini et al. (2015, Description Logics) and Lenzerini et al. (2020, Information Systems 88, 101294), Cima et al. (2017, Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics, 1–6). A reduction from OWL 2 QL to Datalog for meta-querying was proposed in Cima et al. (2017, Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics, 1–6). In this paper, we experiment with various logic programming tools that support Datalog querying to determine their suitability as back-ends to MSER query answering. These tools stem from different logic programming paradigms (Prolog, pure Datalog, Answer Set Programming, Hybrid Knowledge Bases). Our work shows that the Datalog approach to MSER querying is practical also for sizeable ontologies with limited resources (time and memory). This paper significantly extends Qureshi and Faber (2021, International Joint Conference on Rules and Reasoning, Springer, 218–233.) by a more detailed experimental analysis and more background.

Mapping natural language procedures descriptions to linear temporal logic templates: an application in the surgical robotic domain

Natural language annotations and manuals can provide useful procedural information and relations for the highly specialized scenario of autonomous robotic task planning. In this paper, we propose and publicly release AUTOMATE, a pipeline for automatic task knowledge extraction from expert-written domain texts. AUTOMATE integrates semantic sentence classification, semantic role labeling, and identification of procedural connectors, in order to extract templates of Linear Temporal Logic (LTL) relations that can be directly implemented in any sufficiently expressive logic programming formalism for autonomous reasoning, assuming some low-level commonsense and domain-independent knowledge is available. This is the first work that bridges natural language descriptions of complex LTL relations and the automation of full robotic tasks. Unlike most recent similar works that assume strict language constraints in substantially simplified domains, we test our pipeline on texts that reflect the expressiveness of natural language used in available textbooks and manuals. In fact, we test AUTOMATE in the surgical robotic scenario, defining realistic language constraints based on a publicly available dataset. In the context of two benchmark training tasks with texts constrained as above, we show that automatically extracted LTL templates, after translation to a suitable logic programming paradigm, achieve comparable planning success in reduced time, with respect to logic programs written by expert programmers.

System Predictor: Grounding Size Estimator for Logic Programs under Answer Set Semantics

AbstractAnswer set programming is a declarative logic programming paradigm geared towards solving difficult combinatorial search problems. While different logic programs can encode the same problem, their performance may vary significantly. It is not always easy to identify which version of the program performs the best. We present the system predictor (and its algorithmic backend) for estimating the grounding size of programs, a metric that can influence a performance of a system processing a program. We evaluate the impact of predictor when used as a guide for rewritings produced by the answer set programming rewriting tools projector and lpopt. The results demonstrate potential to this approach.

Lifting symmetry breaking constraints with inductive logic programming

Efficient omission of symmetric solution candidates is essential for combinatorial problem-solving. Most of the existing approaches are instance-specific and focus on the automatic computation of Symmetry Breaking Constraints (SBCs) for each given problem instance. However, the application of such approaches to large-scale instances or advanced problem encodings might be problematic since the computed SBCs are propositional and, therefore, can neither be meaningfully interpreted nor transferred to other instances. As a result, a time-consuming recomputation of SBCs must be done before every invocation of a solver. To overcome these limitations, we introduce a new model-oriented approach for Answer Set Programming that lifts the SBCs of small problem instances into a set of interpretable first-order constraints using the Inductive Logic Programming paradigm. Experiments demonstrate the ability of our framework to learn general constraints from instance-specific SBCs for a collection of combinatorial problems. The obtained results indicate that our approach significantly outperforms a state-of-the-art instance-specific method as well as the direct application of a solver.

Developing Problem Solving Competency Using Functional Programming Style

This paper is dedicated to the challenges of the education that high school students are facing while developing specific competencies related to the functional programming style (FPS). The presented educational approach consists of two components: first, learning FPS by comparing it with the imperative, procedural, object-oriented and logic programming paradigms and second, using competencies based approach for solving practical problems with functional programming. The paper presents a problem set and the phases of its application in the learning process. The results and the analysis of the approach are presented in two groups of high school students which develop successfully their specific competencies for using FPS for practical problem solving. The presented results show that the students are understanding easier FPS and its differences from their known paradigms (imperative, procedural, object-oriented and logical) by using a problem set with properly prepared practical problems which they can solve in multiple ways which lead them to the FPS solution.

3coSoKu and its declarative modeling

Abstract In this paper, we analyze the physical puzzle IcoSoKu, a game about placing some given triangular tiles on the faces of an icosahedron in order to fill the capacities of its vertices, and we propose its generalization called 3coSoKu, admitting an arbitrary playing field with triangular faces, arbitrary capacities and an arbitrary set of triangular tiles. First, we prove the strong NP-completeness of 3coSoKu, even when the playing field is a convex polyhedron with equilateral triangles as faces. Second, we encode 3coSoKu both in the constraint modeling language MiniZinc and in the logic programming paradigm known as Answer Set Programming and we develop a visual tool for an accessible interface to the solver. Finally, we use our encodings to verify experimentally that every initial state for IcoSoKu admits a solution.

Combining Probabilistic Planning and Logic Programming on Mobile Robots

Key challenges to widespread deployment of mobile robots to interact with humans in real-world domains include the ability to: (a) robustly represent and revise domain knowledge; (b) autonomously adapt sensing and processing to the task at hand; and (c) learn from unreliable high-level human feedback. Partially observable Markov decision processes (POMDPs) have been used to plan sensing and navigation in different application domains. It is however a challenge to include common sense knowledge obtained from sensory or human inputs in POMDPs. In addition, information extracted from sensory and human inputs may have varying levels of relevance to current and future tasks. On the other hand, although a non-monotonic logic programming paradigm such as Answer Set Programming (ASP) is wellsuited for common sense reasoning, it is unable to model the uncertainty in real-world sensing and navigation (Gelfond 2008). This paper presents a hybrid framework that integrates ASP, hierarchical POMDPs (Zhang and Sridharan 2012) and psychophysics principles to address the challenges stated above. Experimental results in simulation and on mobile robots deployed in indoor domains show that the framework results in reliable and efficient operation.

Automated activity and progress analysis based on non-monotonic reasoning of construction operations

PurposeReal-time location sensing (RTLS) systems offer a significant potential to advance the management of construction processes by potentially providing real-time access to the locations of workers and equipment. Many location-sensing technologies tend to perform poorly for indoor work environments and generate large data sets that are somewhat difficult to process in a meaningful way. Unfortunately, little is still known regarding the practical benefits of converting raw worker tracking data into meaningful information about construction project progress, effectively impeding widespread adoption in construction.Design/methodology/approachThe presented framework is designed to automate as many steps as possible, aiming to avoid manual procedures that significantly increase the time between progress estimation updates. The authors apply simple location tracking sensor data that does not require personal handling, to ensure continuous data acquisition. They use a generic and non-site-specific knowledge base (KB) created through domain expert interviews. The sensor data and KB are analyzed in an abductive reasoning framework implemented in Answer Set Programming (extended to support spatial and temporal reasoning), a logic programming paradigm developed within the artificial intelligence domain.FindingsThis work demonstrates how abductive reasoning can be applied to automatically generate rich and qualitative information about activities that have been carried out on a construction site. These activities are subsequently used for reasoning about the progress of the construction project. Our framework delivers an upper bound on project progress (“optimistic estimates”) within a practical amount of time, in the order of seconds. The target user group is construction management by providing project planning decision support.Research limitations/implicationsThe KB developed for this early-stage research does not encapsulate an exhaustive body of domain expert knowledge. Instead, it consists of excerpts of activities in the analyzed construction site. The KB is developed to be non-site-specific, but it is not validated as the performed experiments were carried out on one single construction site.Practical implicationsThe presented work enables automated processing of simple location tracking sensor data, which provides construction management with detailed insight into construction site progress without performing labor-intensive procedures common nowadays.Originality/valueWhile automated progress estimation and activity recognition in construction have been studied for some time, the authors approach it differently. Instead of expensive equipment, manually acquired, information-rich sensor data, the authors apply simple data, domain knowledge and a logical reasoning system for which the results are promising.

A General Framework for Stable Roommates Problems using Answer Set Programming

AbstractThe Stable Roommates problem (SR) is characterized by the preferences of agents over other agents as roommates: each agent ranks all others in strict order of preference. A solution to SR is then a partition of the agents into pairs so that each pair shares a room, and there is no pair of agents that would block this matching (i.e., who prefers the other to their roommate in the matching). There are interesting variations of SR that are motivated by applications (e.g., the preference lists may be incomplete (SRI) and involve ties (SRTI)), and that try to find a more fair solution (e.g., Egalitarian SR). Unlike the Stable Marriage problem, every SR instance is not guaranteed to have a solution. For that reason, there are also variations of SR that try to find a good-enough solution (e.g., Almost SR). Most of these variations are NP-hard. We introduce a formal framework, called SRTI-ASP, utilizing the logic programming paradigm Answer Set Programming, that is provable and general enough to solve many of such variations of SR. Our empirical analysis shows that SRTI-ASP is also promising for applications.

Using automated reasoning to explore the metabolism of unconventional organisms: a first step to explore host-microbial interactions.

Systems modelled in the context of molecular and cellular biology are difficult to represent with a single calibrated numerical model. Flux optimisation hypotheses have shown tremendous promise to accurately predict bacterial metabolism but they require a precise understanding of metabolic reactions occurring in the considered species. Unfortunately, this information may not be available for more complex organisms or non-cultured microorganisms such as those evidenced in microbiomes with metagenomic techniques. In both cases, flux optimisation techniques may not be applicable to elucidate systems functioning. In this context, we describe how automatic reasoning allows relevant features of an unconventional biological system to be identified despite a lack of data. A particular focus is put on the use of Answer Set Programming, a logic programming paradigm with combinatorial optimisation functionalities. We describe its usage to over-approximate metabolic responses of biological systems and solve gap-filling problems. In this review, we compare steady-states and Boolean abstractions of metabolic models and illustrate their complementarity via applications to the metabolic analysis of macro-algae. Ongoing applications of this formalism explore the emerging field of systems ecology, notably elucidating interactions between a consortium of microbes and a host organism. As the first step in this field, we will illustrate how the reduction in microbiotas according to expected metabolic phenotypes can be addressed with gap-filling problems.

Beyond NP: Quantifying over Answer Sets

AbstractAnswer Set Programming (ASP) is a logic programming paradigm featuring a purely declarative language with comparatively high modeling capabilities. Indeed, ASP can model problems in NP in a compact and elegant way. However, modeling problems beyond NP with ASP is known to be complicated, on the one hand, and limited to problems in $\[\Sigma _2^P\]$ on the other. Inspired by the way Quantified Boolean Formulas extend SAT formulas to model problems beyond NP, we propose an extension of ASP that introduces quantifiers over stable models of programs. We name the new language ASP with Quantifiers (ASP(Q)). In the paper we identify computational properties of ASP(Q); we highlight its modeling capabilities by reporting natural encodings of several complex problems with applications in artificial intelligence and number theory; and we compare ASP(Q) with related languages. Arguably, ASP(Q) allows one to model problems in the Polynomial Hierarchy in a direct way, providing an elegant expansion of ASP beyond the class NP.

Introducing Computer Science to High School Students Through Logic Programming

AbstractThis paper investigates how high school students in an introductory computer science (CS) course approach computing in the logic programming (LP) paradigm. This qualitative study shows how novice students operate within the LP paradigm while engaging in foundational computing concepts and skills: students are engaged in a cyclical process of abstraction, reasoning, and creating representations of their ideas in code while also being informed by the (procedural) requirements and the revision/debugging process. As these computing concepts and skills are also expected in traditional approaches to introductory K-12 CS courses, this paper asserts that LP is a viable paradigm choice for high school novices.

OnlineSPARC: A Programming Environment for Answer Set Programming

AbstractRecent progress in logic programming (e.g. the development of the answer set programming (ASP) paradigm) has made it possible to teach it to general undergraduate and even middle/high school students. Given the limited exposure of these students to computer science, the complexity of downloading, installing, and using tools for writing logic programs could be a major barrier for logic programming to reach a much wider audience. We developed onlineSPARC, an online ASP environment with a self-contained file system and a simple interface. It allows users to type/edit logic programs and perform several tasks over programs, including asking a query to a program, getting the answer sets of a program, and producing a drawing/animation based on the answer sets of a program.

Reasoning about UML/OCL class diagrams using constraint logic programming and formula

Model Driven Engineering promotes the use of models as the main artifacts in software and system development. Verification and validation of models are key activities to ensure the quality of the system under development. This paper presents a framework to reason about the satisfiability of class models described using the Unified Modeling Language (UML). The proposed framework allows us to identify possible design flaws as early as possible in the software development cycle. More specifically, we focus on UML Class Diagrams annotated with Object Constraint Language (OCL) invariants, which are considered to be the main artifacts in Object-Oriented analysis and design for representing the static structure of a system. We use the Constraint Logic programming (CLP) paradigm to reason about UML Class Diagrams modeling foundations. In particular, we use Formula as a model-finding and design space exploration tool. We also present an experimental Eclipse plug-in, which implements our UML model to Formula translation proposal following a Model Driven Architecture (MDA) approach. The proposed framework can be used to reason, validate, and verify UML Class Diagram software designs by checking correctness properties and generating model instances using the model exploration tool Formula.

РАЗРАБОТКА СРЕДСТВ АГЕНТНОГО ЛОГИЧЕСКОГО ПРОГРАММИРОВАНИЯ ДЛЯ МНОГОКАНАЛЬНОГО ИНТЕЛЛЕКТУАЛЬНОГО ВИДЕОНАБЛЮДЕНИЯ

Experimental means developed in the Actor Prolog parallel object-oriented logic language for implementation of heterogeneous multichannel intelligent visual surveillance systems are considered. These means are examined by the instance of a logic program for permanent monitoring of people's body parts temperature in the area of visual surveillance. The logic program implements a fusion of heterogeneous data acquired by two devices: (1) 3D coordinates of human body are measured using a time-of-flight (ToF) camera; (2) 3D coordinates of human body skeleton are computed on the base of 3D coordinates of the body; (3) a thermal video is acquired using a thermal imaging camera. Special built-in classes are developed in the Actor Prolog language for the acquisition and analysis of 3D and 2D video data: the Kinect and KinectBuffer classes implement interaction between the logic program and the ToF camera of the Kinect-2 device; the BufferedScene class implements storing and transferring 3D images; the Canvas3D class implements 3D graphics based on the Java3D open source library. In the considered example, the thermal video is projected to the 3D surface of the human body; then the temperature of the human body is projected to the vertices and edges of the skeleton. A special logical agent (i.e., the logic program written in Actor Prolog) implements these operations in real-time and transfers the data to another logical agent. The latter agent implements a time average of the temperature of the human skeletons and displays colored 3D images of the skeletons; the average temperature of the vertices and edges of the skeletons is depicted by colors. A method of remote predicate calls is used for the interaction of the logical agents; this method was developed and implemented in Actor Prolog to support the agent logic programming paradigm. The logic programming means under consideration are developed for the purpose of the implementation of logical analysis of video scene semantics in the intelligent visual surveillance systems. © 2018 Radioelektronika, Nanosistemy, Informacionnye Tehnologii. All rights reserved.

Extending Coinductive Logic Programming with Co-Facts

We introduce a generalized logic programming paradigm where programs, consisting of facts and rules with the usual syntax, can be enriched by co-facts, which syntactically resemble facts but have a special meaning. As in coinductive logic programming, interpretations are subsets of the complete Herbrand basis, including infinite terms. However, the intended meaning (declarative semantics) of a program is a fixed point which is not necessarily the least, nor the greatest one, but is determined by co-facts. In this way, it is possible to express predicates on non well-founded structures, such as infinite lists and graphs, for which the coinductive interpretation would be not precise enough. Moreover, this paradigm nicely subsumes standard (inductive) and coinductive logic programming, since both can be expressed by a particular choice of co-facts, hence inductive and coinductive predicates can coexist in the same program. We illustrate the paradigm by examples, and provide declarative and operational semantics, proving the correctness of the latter. Finally, we describe a prototype meta-interpreter.

Logic/Constraint Programming and Concurrency: The hard-won lessons of the Fifth Generation Computer project

The technical goal of the Fifth Generation Computer Systems (FGCS) project (1982–1993) was to develop Parallel Inference technologies, namely systematized technologies for realizing knowledge information processing on top of parallel computer architecture. The Logic Programming paradigm was adopted as the central working hypothesis of the project. At the same time, building a large-scale Parallel Inference Machine (PIM) meant to develop a novel form of general-purpose computing technologies that are powerful enough to express various parallel algorithms and to describe a full operating system of PIM. Accordingly, the research goal of the Kernel Language was set to designing a concurrent and parallel programming language under the working hypothesis of Logic Programming. The aim of this article is to describe the design process of the Kernel Language (KL1) in the context of related programming models in the 1980s, the essence of Concurrent Logic Programming and Constraint-Based Concurrency, and how the technologies we developed in those days evolved after their conception. • Design process of the Kernel Language (KL1). • Essence of Concurrent Logic Programming and Constraint-Based Concurrency. • How the technologies evolved after their conception.

Combining Answer Set Programming and domain heuristics for solving hard industrial problems (Application Paper)

AbstractAnswer Set Programming (ASP) is a popular logic programming paradigm that has been applied for solving a variety of complex problems. Among the most challenging real-world applications of ASP are two industrial problems defined by Siemens: the Partner Units Problem (PUP) and the Combined Configuration Problem (CCP). The hardest instances of PUP and CCP are out of reach for state-of-the-art ASP solvers. Experiments show that the performance of ASP solvers could be significantly improved by embedding domain-specific heuristics, but a proper effective integration of such criteria in off-the-shelf ASP implementations is not obvious. In this paper the combination of ASP and domain-specific heuristics is studied with the goal of effectively solving real-world problem instances of PUP and CCP. As a byproduct of this activity, the ASP solverwaspwas extended with an interface that eases embedding new external heuristics in the solver. The evaluation shows that our domain-heuristic-driven ASP solver finds solutions for all the real-world instances of PUP and CCP ever provided by Siemens.

Predicting Opponent's Production in Real-Time Strategy Games With Answer Set Programming

The adversarial character of real-time strategy (RTS) games is one of the main sources of uncertainty within this domain. Since players lack exact knowledge about their opponent's actions, they need a reasonable representation of alternative possibilities and their likelihood. In this article we propose a method of predicting the most probable combination of units produced by the opponent during a certain time period. We employ a logic programming paradigm called Answer Set Programming, since its semantics is well suited for reasoning with uncertainty and incomplete knowledge. In contrast with typical, purely probabilistic approaches, the presented method takes into account the background knowledge about the game and only considers the combinations that are consistent with the game mechanics and with the player's partial observations. Experiments, conducted during different phases of StarCraft: Brood War and Warcraft III: The Frozen Throne games, show that the prediction accuracy for time intervals of 1–3 min seems to be surprisingly high, making the method useful in practice. Root-mean-square error grows only slowly with increasing prediction intervals—almost in a linear fashion.