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On the relation among answer set solvers

ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2008年第1-4期53卷 169-204页

作者： Giunchiglia, Enrico Leone, Nicola Maratea, Marco Univ Genoa STAR Lab DIST I-16145 Genoa Italy Univ Calabria Dept Math I-87030 Cosenza Italy

In this paper, we study the relation among answer set programming (ASP) systems from a computational point of view. We consider SMODELS, DLV, and CMODELS ASP systems based on stable model semantics, the first two being native ASP systems and the last being a SAT-based system. We first show that SMODELS, DLV, and CMODELS explore search trees with the same branching nodes (assuming, of course, a same branching heuristic) on the class of tight logic programs. Leveraging on the fact that SAT-based systems rely on the deeply studied Davis-Logemann-Loveland (DLL) algorithm, we derive new complexity results for the ASP procedures. We also show that on nontight programs the SAT-based systems are computationally different from native procedures, and the latter have computational advantages. Moreover, we show that native procedures can guarantee the "correctness" of a reported solution when reaching the leaves of the search trees (i.e., no stability check is needed), while this is not the case for SAT-based procedures on nontight programs. A similar advantage holds for DLV in comparison with SMODELS if the "well-founded" operator is disabled and only Fitting's operator is used for negative inferences. We finally study the "cost" of achieving such advantages and comment on to what extent the results presented extend to other systems.

关键词： answer set solvers answer set programming Search trees

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A Brief Survey on Forgetting from a Knowledge Representation and Reasoning Perspective

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KUNSTLICHE INTELLIGENZ 2019年第1期33卷 9-33页

作者： Eiter, Thomas Kern-Isberner, Gabriele TU Wien Inst Logic & Computat Vienna Austria TU Dortmund Dept Comp Sci Dortmund Germany

Forgetting is an ambivalent concept of (human) intelligence. By definition, it is negatively related to knowledge in that knowledge is lost, be it deliberately or not, and therefore, forgetting has not received as much attention in the field of knowledge representation and reasoning (KRR) as other processes with a more positive orientation, like query answering, inference, or update. However, from a cognitive view, forgetting also has an ordering function in the human mind, suppressing information that is deemed irrelevant and improving cognitive capabilities to focus and deal only with relevant aspects of the problem under consideration. In this regard, forgetting is a crucial part of reasoning. This paper collects and surveys approaches to forgetting in the field of knowledge representation and reasoning, highlighting their roles in diverse tasks of knowledge processing, and elaborating on common techniques. We recall forgetting operations for propositional and predicate logic, as well as for answer set programming (as an important representative of nonmonotonic logics) and modal logics. We discuss forgetting in the context of (ir)relevance and (in)dependence, and explicit the role of forgetting for specific tasks of knowledge representation, showing its positive impact on solving KRR problems.

关键词： Forgetting Knowledge representation and reasoning Logic answer set programming Nonmononotonic reasoning Modal logics Interpolation Relevance Independence

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Digital forensics and investigations meet artificial intelligence

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2019年第1-3期86卷 193-229页

作者： Costantini, Stefania De Gasperis, Giovanni Olivieri, Raffaele Univ Aquila Laquila Italy Dipartimento Ingn & Sci Informaz & Matemat Via Vetoio Snc Loc Coppito I-67100 Laquila Italy

In the frame of Digital Forensic (DF) and Digital Investigations (DI), the Evidence Analysis phase has the aim to provide objective data, and to perform suitable elaboration of these data so as to help in the formation of possible hypotheses, which could later be presented as elements of proof in court. The aim of our research is to explore the applicability of Artificial Intelligence (AI) along with computational logic tools - and in particular the answer set programming (ASP) approach - to the automation of evidence analysis. We will show how significant complex investigations, hardly solvable for human experts, can be expressed as optimization problems belonging in many cases to the P or NP complexity classes. All these problems can be expressed in ASP. As a proof of concept, in this paper we present the formalization of realistic investigative cases via simple ASP programs, and show how such a methodology can lead to the formulation of tangible investigative hypotheses. We also sketch a design for a feasible Decision Support System (DSS) especially meant for investigators, based on artificial intelligence tools.

关键词： Digital forensics Digital investigation Artificial intelligence answer set programming Automatic investigation Forensic models

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Enhancing DLV instantiator by backjumping techniques

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2007年第2-4期51卷 195-228页

作者： Perri, Simona Scarcello, Francesco Catalano, Gelsomina Leone, Nicola Univ Calabria Dept Math I-87036 Arcavacata Di Rende Italy Univ Calabria DEIS I-87036 Arcavacata Di Rende Italy

Disjunctive logic programming (DLP) is a powerful formalism for knowledge representation and reasoning. The high expressiveness of DLP language, together with the recent availability of some efficient DLP system, has favoured the application of DLP in emerging areas like Knowledge Management and Information Integration. These applications have often to deal with huge input data, and have evidenced the need to improve the efficiency of DLP instantiators. Program instantiation is the first phase of a DLP computation;in this phase, variables are replaced by constants to generate a ground program which is then evaluated by propositional algorithms in the second phase of the computation. The instantiation process may be computationally expensive, and in fact its efficiency has been recognized to be a key issue for solving real-world problems by using disjunctive logic programming. Given a program P, a good instantiation for P is a ground program P' having precisely the same answer sets as P and such that: (1) P' can be computed efficiently from P, and (2) P' does not contain "useless" rules, (P' is as small as possible) and can thus be evaluated efficiently. In this paper, we present a structure-based backjumping algorithm for the instantiation of disjunctive logic programs, that meets the above requirements. In particular, given a rule r to be grounded, our algorithm exploits both the semantical and the structural information about r for computing efficiently the ground instances of r, avoiding the generation of "useless" rules. That is, from each general rule r, we compute only a relevant subset of its ground instances, avoiding the generation of "useless" instances, while fully preserving the semantic of the program. We have implemented this algorithm in DLV-the state-of-the-art implementation of DLP-and we have carried out an experimentation activity on an ample collection of benchmark problems. The experimental results are very positive: the new technique improves s

关键词： artificial intelligence logic programming nonmonotonic reasoning disjunctive logic programming answer set programming deductive databases stable model semantics

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Revising event calculus theories to recover from unexpected observations

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2021年第1-2期89卷 209-236页

作者： Tsampanaki, Nikoleta Patkos, Theodore Flouris, Giorgos Plexousakis, Dimitris FORTH Inst Comp Sci Iraklion Greece

Recent extensions of the Event Calculus resulted in powerful formalisms, able to reason about a multitude of commonsense phenomena in causal domains, involving epistemic notions, functional fluents and probabilistic aspects, among others. Less attention has been paid to the problem of automatically revising (correcting) a Knowledge Base when an observation contradicts inferences made regarding the world state. Despite mature work on the related belief revision field, adapting such results for the case of action theories is non-trivial. This paper describes how to address this problem for deterministic, yet partially observable, domains, by proposing a generic framework in the context of the Event Calculus, along with ASP encodings of the revision algorithm and a web-based tester of the formalism implementation.

关键词： Event calculus Belief revision Action theories answer set programming

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Structural constraints for dynamic operators in abstract argumentation

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ARGUMENT & COMPUTATION 2020年第1-2期11卷 151-190页

作者： Wallner, Johannes P. TU Wien Inst Log & Computat Vienna Austria

Many recent studies of dynamics in formal argumentation within AI focus on the well-known formalism of Dung's argumentation frameworks (AFs). Despite the usefulness of AFs in many areas of argumentation, their abstract notion of arguments creates a barrier for operators that modify a given AF, e.g., in the case that dependencies between arguments have been abstracted away that are important for subsequent modifications. In this paper we aim to support development of dynamic operators on formal models in abstract argumentation by providing constraints imposed on the modification of the structure that can be used to incorporate information that has been abstracted away. Towards a broad reach, we base our results on the general formalism of abstract dialectical frameworks (ADFs) in abstract argumentation. To show applicability, we present two cases studies that adapt an existing extension enforcement operator that modifies AFs: in the first case study, we show how to utilize constraints in order to obtain an enforcement operator on ADFs that is allowed to only add support relations between arguments, and in the second case study we show how an enforcement operator on AFs can be defined that respects dependencies between arguments. We show feasibility of our approach by studying the complexity of the proposed structural constraints and the operators arising from the case studies, and by an experimental evaluation of an answer set programming (ASP) implementation of the enforcement operator based on supports.

关键词： Abstract argumentation dynamics of argumentation argumentation frameworks abstract dialectical frameworks structured argumentation computational complexity constraints answer set programming

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Extended ASP Tableaux and rule redundancy in normal logic programs

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THEORY AND PRACTICE OF LOGIC programming 2008年第5-6期8卷 691-716页

作者： Jarvisalo, Matti Oikarinen, Emilia Aalto Univ Dept Informat & Comp Sci FI-02015 Helsinki Finland

We introduce an extended tableau calculus for answer set programming (ASP). The proof system is based on the ASP tableaux defined in the work by Gebser and Schaub (Tableau calculi for answer set programming. In Proceedings of the 22nd International Conference on Logic programming (ICLP 2006), S. Etalle and M. Truszczynski, Eds. Lecture Notes in Computer Science, vol. 4079. Springer, 11-25) with an added extension rule. We investigate the power of Extended ASP Tableaux both theoretically and empirically. We study the relationship of Extended ASP Tableaux with the Extended Resolution proof system defined by Tseitin for sets of clauses, and separate Extended ASP Tableaux from ASP Tableaux by giving a polynomial-length proof for a family of normal logic programs {Pi(n)} for which ASP Tableaux has exponential-length minimal proofs with respect to n. Additionally, Extended ASP Tableaux imply interesting insight into the effect of program simplification on the lengths of proofs in ASP. Closely related to Extended ASP Tableaux, we empirically investigate the effect of redundant rules on the efficiency of ASP solving.

关键词： answer set programming tableau method extension rule proof complexity problem structure

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A Syntactic Operator for Forgetting that Satisfies Strong Persistence

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THEORY AND PRACTICE OF LOGIC programming 2019年第5-6期19卷 1038-1055页

作者： Berthold, Matti Goncalves, Ricardo Knorr, Matthias Leite, Joao Tech Univ Dresden Dresden Germany Univ Leipzig Leipzig Germany Univ Nova Lisboa Dept Informat NOVA LINCS Lisbon Portugal

Whereas the operation of forgetting has recently seen a considerable amount of attention in the context of answer set programming (ASP), most of it has focused on theoretical aspects, leaving the practical issues largely untouched. Recent studies include results about what sets of properties operators should satisfy, as well as the abstract characterization of several operators and their theoretical limits. However, no concrete operators have been investigated. In this paper, we address this issue by presenting the first concrete operator that satisfies strong persistence - a property that seems to best capture the essence of forgetting in the context of ASP - whenever this is possible, and many other important properties. The operator is syntactic, limiting the computation of the forgetting result to manipulating the rules in which the atoms to be forgotten occur, naturally yielding a forgetting result that is close to the original program.

关键词： answer set programming Forgetting Strong Persistence Syntactic Operator

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A hybrid inductive learning-based and deductive reasoning-based 3-D path planning method in complex environments

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AUTONOMOUS ROBOTS 2022年第5期46卷 645-666页

作者： Segato, Alice Calimeri, Francesco Testa, Irene Corbetta, Valentina Riva, Marco De Momi, Elena Politecn Milan Dept Elect Informat & Bioengn Dept DEIB NearLab I-20133 Milan Italy Univ Calabria Dept Math & Comp Sci I-87036 Arcavacata Di Rende Italy Univ Milan Dept Med Biotechnol & Translat Med Milan Italy IRCCS Humanitas Res Hosp Milan Italy

Traditional path planning methods, such as sampling-based and iterative approaches, allow for optimal path's computation in complex environments. Nonetheless, environment exploration is subject to rules which can be obtained by domain experts and could be used for improving the search. The present work aims at integrating inductive techniques that generate path candidates with deductive techniques that choose the preferred ones. In particular, an inductive learning model is trained with expert demonstrations and with rules translated into a reward function, while logic programming is used to choose the starting point according to some domain expert's suggestions. We discuss, as use case, 3-D path planning for neurosurgical steerable needles. Results show that the proposed method computes optimal paths in terms of obstacle clearance and kinematic constraints compliance, and is able to outperform state-of-the-art approaches in terms of safety distance-from-obstacles respect, smoothness, and computational time.

关键词： Path planning Artificial intelligence Machine learning Surgical robotics Steerable needles answer set programming

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Ontology-based hybrid commonsense reasoning framework for handling context abnormalities in uncertain and partially observable environments

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INFORMATION SCIENCES 2023年 631卷 468-486页

作者： Moulouel, Koussaila Chibani, Abdelghani Amirat, Yacine Univ Paris Est Creteil LISSI F-94400 Vitry Sur Seine France

Ambient intelligence (AmI) systems aim to provide users with context-aware assistance services intended to improve the quality of their lives in terms of autonomy, safety, and well-being. Taking the uncertainty and partial observability of these environments into account is of major importance for context recognition and, more specifically, to detect and solve context abnormalities such as those related to the user's behavior or those related to context attribute prediction. In this paper, an ontology-based framework integrating machine learning and probabilistic planning within commonsense reasoning is proposed to recognize the user's context and abnormalities associated with it. The reasoning is performed using event calculus in answer set programming (ECASP);ECASP allows for abductive and temporal reasoning, which results in an eXplainable AI (XAI) approach. A context ontology is proposed to axiomatize the reasoning and introduce the notion of probabilistic fluents into the EC formalism in order to perform probabilistic reasoning. The reasoning incorporates probabilistic planning based on a partially observable Markov decision process (POMDP) to solve knowledge incompleteness. To evaluate the proposed framework, real-life scenarios, based on the Orange4Home and SIMADL public datasets are implemented and discussed.

关键词： Deep learning CNN-LSTM model Transformer model Probabilistic commonsense reasoning Probabilistic planning Ontology Event calculus answer set programming POMDP

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