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ASPeRiX, a first-order forward chaining approach for answer set computing

THEORY AND PRACTICE OF LOGIC programming 2017年第3期17卷 266-310页

作者： Lefevre, Claire Beatrix, Christopher Stephan, Igor Garcia, Laurent Univ Angers LERIA 2 Blvd Lavoisier F-49045 Angers 01 France

The natural way to use answer set programming (ASP) to represent knowledge in Artificial Intelligence or to solve a combinatorial problem is to elaborate a first-order logic program with default negation. In a preliminary step, this program with variables is translated in an equivalent propositional one by a first tool: the grounder. Then, the propositional program is given to a second tool: the solver. This last one computes (if they exist) one or many answer sets (stable models) of the program, each answer set encoding one solution of the initial problem. Until today, almost all ASP systems apply this two steps computation. In this article, the project ASPeRiX is presented as a first-order forward chaining approach for answer set Computing. This project was among the first to introduce an approach of answer set computing that escapes the preliminary phase of rule instantiation by integrating it in the search process. The methodology applies a forward chaining of first-order rules that are grounded on the fly by means of previously produced atoms. Theoretical foundations of the approach are presented, the main algorithms of the ASP solver ASPeRiX are detailed and some experiments and comparisons with existing systems are provided.

关键词： answer set programming solver implementation grounding on the fly first order forward chaining

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plasp 3: Towards Effective ASP Planning

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THEORY AND PRACTICE OF LOGIC programming 2019年第3期19卷 477-504页

作者： Dimopoulos, Yannis Gebser, Martin Luehne, Patrick Romero, Javier Schaub, Torsten Univ Cyprus Nicosia Cyprus Univ Klagenfurt Klagenfurt Austria Graz Univ Technol Graz Austria Univ Potsdam Potsdam Germany INRIA Rennes Rennes France

We describe the new version of the Planning Domain Definition Language (PDDL)-to-answer set programming (ASP) translator plasp. First, it widens the range of accepted PDDL features. Second, it contains novel planning encodings, some inspired by Satisfiability Testing (SAT) planning and others exploiting ASP features such as well-foundedness. All of them are designed for handling multivalued fluents in order to capture both PDDL as well as SAS planning formats. Third, enabled by multishot ASP solving, it offers advanced planning algorithms also borrowed from SAT planning. As a result, plasp provides us with an ASP-based framework for studying a variety of planning techniques in a uniform setting. Finally, we demonstrate in an empirical analysis that these techniques have a significant impact on the performance of ASP planning.

关键词： knowledge representation and nonmonotonic reasoning technical notes and rapid communications answer set programming automated planning action and change

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Optimizing answer set Computation via Heuristic-Based Decomposition

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THEORY AND PRACTICE OF LOGIC programming 2019年第4期19卷 603-628页

作者： Calimeri, Francesco Perri, Simona Zangari, Jessica Univ Calabria Dept Math & Comp Sci Arcavacata Di Rende Italy

answer set programming (ASP) is a purely declarative formalism developed in the field of logic programming and non-monotonic reasoning: computational problems are encoded by logic programs whose answer sets, corresponding to solutions, are computed by an ASP system. Different, semantically equivalent, programs can be defined for the same problem;however, performance of systems evaluating them might significantly vary. We propose an approach for automatically transforming an input logic program into an equivalent one that can be evaluated more efficiently. One can make use of existing tree-decomposition techniques for rewriting selected rules into a set of multiple ones;the idea is to guide and adaptively apply them on the basis of proper new heuristics, to obtain a smart rewriting algorithm to be integrated into an ASP system. The method is rather general: it can be adapted to any system and implement different preference policies. Furthermore, we define a set of new heuristics tailored at optimizing grounding, one of the main phases of the ASP computation;we use them in order to implement the approach into the ASP system DLV, in particular into its grounding subsystem I-DLV, and carry out an extensive experimental activity for assessing the impact of the proposal.

关键词： knowledge representation and nonmonotonic reasoning logic programming methodology and applications databases and semantic web reasoning answer set programming

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Using linear constraints for logic program termination analysis

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THEORY AND PRACTICE OF LOGIC programming 2016年第3期16卷 353-377页

作者： Calautti, Marco Greco, Sergio Molinaro, Cristian Trubitsyna, Irina Univ Calabria DIMES I-87036 Arcavacata Di Rende CS Italy

It is widely acknowledged that function symbols are an important feature in answer set programming, as they make modelling easier, increase the expressive power, and allow us to deal with infinite domains. The main issue with their introduction is that the evaluation of a program might not terminate and checking whether it terminates or not is undecidable. To cope with this problem, several classes of logic programs have been proposed where the use of function symbols is restricted but the program evaluation termination is guaranteed. Despite the significant body of work in this area, current approaches do not include many simple practical programs whose evaluation terminates. In this paper, we present the novel classes of rule-bounded and cycle-bounded programs, which overcome different limitations of current approaches by performing a more global analysis of how terms are propagated from the body to the head of rules. Results on the correctness, the complexity, and the expressivity of the proposed approach are provided.

关键词： answer set programming Function symbols Bottom-up evaluation Program evaluation termination Stable models

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Reasoning with infinite stable models

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ARTIFICIAL INTELLIGENCE 2004年第1期156卷 75-111页

作者： Bonatti, PA Univ Naples Federico II Dipartimento Sci Fisiche I-80126 Naples Italy

This paper illustrates extensively the theoretical properties, the implementation issues, and the programming style underlying finitary programs. They are a class of normal logic programs whose consequences under the stable model semantics can be effectively computed, despite the fact that finitary programs admit function symbols (hence infinite domains) and recursion. From a theoretical point of view, finitary programs are interesting because they enjoy properties that are extremely unusual for a nonmonotonic formalism, such as compactness. From the application point of view, the theory of finitary programs shows how the existing technology for answer set programming can be extended from problem solving below the second level of the polynomial hierarchy to all semidecidable problems. Moreover, finitary programs allow a more natural encoding of recursive data structures and may increase the performance of credulous reasoners. (C) 2004 Elsevier B.V. All rights reserved.

关键词： finitary programs answer set programming first-order nonmonotonic reasoning infinite domains logic programming stable model semantics

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Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework

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MACHINE LEARNING 2022年第4期111卷 1523-1549页

作者： Mitchener, Ludovico Tuckey, David Crosby, Matthew Russo, Alessandra Imperial Coll London Exhibit Rd London SW7 2BX England

In this paper we introduce Detect, Understand, Act (DUA), a neuro-symbolic reinforcement learning framework. The Detect component is composed of a traditional computer vision object detector and tracker. The Act component houses a set of options, high-level actions enacted by pre-trained deep reinforcement learning (DRL) policies. The Understand component provides a novel answer set programming (ASP) paradigm for symbolically implementing a meta-policy over options and effectively learning it using inductive logic programming (ILP). We evaluate our framework on the Animal-AI (AAI) competition testbed, a set of physical cognitive reasoning problems. Given a set of pre-trained DRL policies, DUA requires only a few examples to learn a meta-policy that allows it to improve the state-of-the-art on multiple of the most challenging categories from the testbed. DUA constitutes the first holistic hybrid integration of computer vision, ILP and DRL applied to an AAI-like environment and sets the foundations for further use of ILP in complex DRL challenges.

关键词： Neuro-symbolic Hierarchical reinforcement learning Deep reinforcement learning Inductive logic programming answer set programming

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Inductive learning of answer set programs for autonomous surgical task planning Application to a training task for surgeons

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MACHINE LEARNING 2021年第7期110卷 1739-1763页

作者： Meli, Daniele Sridharan, Mohan Fiorini, Paolo Univ Verona Dept Comp Sci Str Grazie 15 I-37135 Verona Italy Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

The quality of robot-assisted surgery can be improved and the use of hospital resources can be optimized by enhancing autonomy and reliability in the robot's operation. Logic programming is a good choice for task planning in robot-assisted surgery because it supports reliable reasoning with domain knowledge and increases transparency in the decision making. However, prior knowledge of the task and the domain is typically incomplete, and it often needs to be refined from executions of the surgical task(s) under consideration to avoid sub-optimal performance. In this paper, we investigate the applicability of inductive logic programming for learning previously unknown axioms governing domain dynamics. We do so under answer set semantics for a benchmark surgical training task, the ring transfer. We extend our previous work on learning the immediate preconditions of actions and constraints, to also learn axioms encoding arbitrary temporal delays between atoms that are effects of actions under the event calculus formalism. We propose a systematic approach for learning the specifications of a generic robotic task under the answer set semantics, allowing easy knowledge refinement with iterative learning. In the context of 1000 simulated scenarios, we demonstrate the significant improvement in performance obtained with the learned axioms compared with the hand-written ones;specifically, the learned axioms address some critical issues related to the plan computation time, which is promising for reliable real-time performance during surgery.

关键词： Inductive logic programming Surgical robotics answer set programming

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Dual-normal logic programs - the forgotten class

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THEORY AND PRACTICE OF LOGIC programming 2015年第4-5期15卷 495-510页

作者： Fichte, Johannes K. Truszczynski, Miroslaw Woltran, Stefan TU Wien Vienna Austria Univ Potsdam Potsdam Germany Univ Kentucky Lexington KY USA

Disjunctive answer set programming is a powerful declarative programming paradigm with complexity beyond NP. Identifying classes of programs for which the consistency problem is in NP is of interest from the theoretical standpoint and can potentially lead to improvements in the design of answer set programming solvers. One of such classes consists of dual-normal programs, where the number of positive body atoms in proper rules is at most one. Unlike other classes of programs, dual-normal programs have received little attention so far. In this paper we study this class. We relate dual-normal programs to propositional theories and to normal programs by presenting several inter-translations. With the translation from dual-normal to normal programs at hand, we introduce the novel class of body-cycle free programs, which are in many respects dual to head-cycle free programs. We establish the expressive power of dual-normal programs in terms of SE- and UE-models, and compare them to normal programs. We also discuss the complexity of deciding whether dual-normal programs are strongly and uniformly equivalent.

关键词： answer set programming classes of logic programs strong and uniform equivalence propositional satisfiability

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Query answering in resource-based answer set semantics

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THEORY AND PRACTICE OF LOGIC programming 2016年第5-6期16卷 619-635页

作者： Costantini, Stefania Formisano, Andrea Univ Aquila DISIM I-67100 Laquila Italy Univ Perugia DMI GNCS INdAM I-06100 Perugia Italy

In recent work we defined resource-based answer set semantics, which is an extension to answer set semantics stemming from the study of its relationship with linear logic. In fact, the name of the new semantics comes from the fact that in the linear-logic formulation every literal (including negative ones) were considered as a resource. In this paper, we propose a query-answering procedure reminiscent of Prolog for answer set programs under this extended semantics as an extension of XSB-resolution for logic programs with negation.(1) We prove formal properties of the proposed procedure. Under consideration for acceptance in TPLP.

关键词： answer set programming Procedural Semantics Top-down Query-answering

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Thirty years of Epistemic Specifications

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THEORY AND PRACTICE OF LOGIC programming 2022年第6期22卷 1043-1083页

作者： Fandinno, Jorge Faber, Wolfgang Gelfond, Michael Univ Nebraska Omaha Omaha NE 68182 USA Univ Potsdam Potsdam Germany Alpen Adria Univ Klagenfurt Klagenfurt Austria Texas Tech Univ Lubbock TX 79409 USA

The language of epistemic specifications and epistemic logic programs extends disjunctive logic programs under the stable model semantics with modal constructs called subjective literals. Using subjective literals, it is possible to check whether a regular literal is true in every or some stable models of the program, those models, in this context also called belief sets, being collected in a set called world view. This allows for representing, within the language, whether some proposition should be understood accordingly to the open or the closed world assumption. Several attempts for capturing the intuitions underlying the language by means of a formal semantics were given, resulting in a multitude of proposals that makes it difficult to understand the current state of the art. In this article, we provide an overview of the inception of the field and the knowledge representation and reasoning tasks it is suitable for. We also provide a detailed analysis of properties of proposed semantics, and an outlook of challenges to be tackled by future research in the area.

关键词： answer set programming epistemic logic programs epistemic specifications epistemic reasoning autopistemic logic

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