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Strong Equivalence of logic Programs with Ordered Disjunction: A logical Perspective

theory and practice of logic programming 2022年第5期22卷 708-722页

作者： Charalambidis, Angelos Nomikos, Christos Rondogiannis, Panos Natl & Kapodistrian Univ Athens Dept Informat & Telecommun Athens Greece Univ Ioannina Dept Comp Sci & Engn Ioannina Greece

logic Programs with Ordered Disjunction (LPODs) extend classical logic programs with the capability of expressing preferential disjunctions in the heads of program rules. The initial semantics of LPODs, although simple and quite intuitive, is not purely model-theoretic. As a result, certain properties of programs appear non-trivial to formalize in purely logical terms. For example, the current characterization of strong equivalence for LPODs, does not coincide with logical equivalence in some specific logic. This comes in sharp contrast with the well-known characterization of strong equivalence for classical logic programs, which coincides with logical equivalence in the logic of here-and-there. In this paper we obtain a purely logical characterization of strong equivalence for LPODs as logical equivalence in a four-valued logic. Moreover, we provide a new proof of the coNP-completeness of strong equivalence for LPODs, which has an interest in its own right since it relies on the special structure of such programs. Our results are based on the recent logical semantics of LPODs, a fact which we believe indicates that this new semantics may prove to be a useful tool in the further study of LPODs.

关键词： ordered disjunction strong equivalence logic of here-and-there answer sets

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A Neuro-Symbolic ASP Pipeline for Visual Question Answering

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theory and practice of logic programming 2022年第5期22卷 739-754页

作者： Eiter, Thomas Higuera, Nelson Oetsch, Johannes Pritz, Michael Vienna Univ Technol TU Wien Inst Log & Computat Vienna Austria

We present a neuro-symbolic visual question answering (VQA) pipeline for CLEVR, which is a well-known dataset that consists of pictures showing scenes with objects and questions related to them. Our pipeline covers (i) training neural networks for object classification and bounding-box prediction of the CLEVR scenes, (ii) statistical analysis on the distribution of prediction values of the neural networks to determine a threshold for high-confidence predictions, and (iii) a translation of CLEVR questions and network predictions that pass confidence thresholds into logic programmes so that we can compute the answers using an answer-set programming solver. By exploiting choice rules, we consider deterministic and non-deterministic scene encodings. Our experiments show that the non-deterministic scene encoding achieves good results even if the neural networks are trained rather poorly in comparison with the deterministic approach. This is important for building robust VQA systems if network predictions are less-than perfect. Furthermore, we show that restricting non-determinism to reasonable choices allows for more efficient implementations in comparison with related neuro-symbolic approaches without losing much accuracy.

关键词： answer-set programming visual question answering neuro-symbolic computation

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Aggregate Semantics for Propositional Answer Set Programs

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theory and practice of logic programming 2023年第1期23卷 157-194页

作者： Alviano, Mario Faber, Wolfgang Gebser, Martin Univ Calabria Arcavacata Di Rende Italy Univ Klagenfurt Klagenfurt Austria Graz Univ Technol Graz Austria

Answer set programming (ASP) emerged in the late 1990s as a paradigm for knowledge representation and reasoning. The attractiveness of ASP builds on an expressive high-level modeling language along with the availability of powerful off-the-shelf solving systems. While the utility of incorporating aggregate expressions in the modeling language has been realized almost simultaneously with the inception of the first ASP solving systems, a general semantics of aggregates and its efficient implementation have been long-standing challenges. Aggregates have been proposed and widely used in database systems, and also in the deductive database language Datalog, which is one of the main precursors of ASP. The use of aggregates was, however, still restricted in Datalog (by either disallowing recursion or only allowing monotone aggregates), while several ways to integrate unrestricted aggregates evolved in the context of ASP. In this survey, we pick up at this point of development by presenting and comparing the main aggregate semantics that have been proposed for propositional ASP programs. We highlight crucial properties such as computational complexity and expressive power, and outline the capabilities and limitations of different approaches by illustrative examples.

关键词： answer set programming aggregate expressions semantics complexity and expressiveness

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Effective Theories in programming practice

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2023年

作者： Jayadev Misra

ISBN: (纸本)9781450399739

Set theory, logic, discrete mathematics, and fundamental algorithms (along with their correctness and complexity analysis) will always remain useful for computing professionals and need to be understood by students who want to succeed. This textbook explains a number of those fundamental algorithms to programming students in a concise, yet precise, manner. The book includes the background material needed to understand the explanations and to develop such explanations for other algorithms. The author demonstrates that clarity and simplicity are achieved not by avoiding formalism, but by using it properly. The book is self-contained, assuming only a background in high school mathematics and elementary program writing skills. It does not assume familiarity with any specific programming language. Starting with basic concepts of sets, functions, relations, logic, and proof techniques including induction, the necessary mathematical framework for reasoning about the correctness, termination and efficiency of programs is introduced with examples at each stage. The book contains the systematic development, from appropriate theories, of a variety of fundamental algorithms related to search, sorting, matching, graph-related problems, recursive programming methodology and dynamic programming techniques, culminating in parallel recursive structures.

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Probabilistic QoS-aware Placement of VNF Chains at the Edge

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theory and practice of logic programming 2022年第1期22卷 1-36页

作者： Forti, Stefano Paganelli, Federica Brogi, Antonio Univ Pisa Dept Comp Sci Pisa Italy

Deploying Internet of Things (IoT)-enabled virtual network function (VNF) chains to Cloud-Edge infrastructures requires determining a placement for each VNF that satisfies all set deployment requirements as well as a software-defined routing of traffic flows between consecutive functions that meets all set communication requirements. In this article, we present a declarative solution, EdgeUsher, to the problem of how to best place VNF chains to Cloud-Edge infrastructures. EdgeUsher can determine all eligible placements for a set of VNF chains to a Cloud-Edge infrastructure so to satisfy all of their hardware, IoT, security, bandwidth, and latency requirements. It exploits probability distributions to model the dynamic variations in the available Cloud-Edge infrastructure and to assess output eligible placements against those variations.

关键词： Edge computing IoT VNF chain placement NFV SDN probabilistic logic programming

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An ASP Approach for Reasoning on Neural Networks under a Finitely Many-Valued Semantics for Weighted Conditional Knowledge Bases

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theory and practice of logic programming 2022年第4期22卷 589-605页

作者： GIORDANO, L. A. U. R. A. THESEIDER DUPRE, D. A. N. I. E. L. E. Univ Piemonte Orientale DISIT Vercelli Italy

Weighted knowledge bases for description logics with typicality have been recently considered under a "concept-wise" multipreference semantics (in both the two-valued and fuzzy case), as the basis of a logical semantics of multilayer perceptrons (MLPs). In this paper we consider weighted conditional ALC knowledge bases with typicality in the finitely many-valued case, through three different semantic constructions. For the boolean fragment LC of ALC we exploit answer set programming and asprin for reasoning with the concept-wise multipreference entailment under a phi-coherent semantics, suitable to characterize the stationary states of MLPs. As a proof of concept, we experiment the proposed approach for checking properties of trained MLPs.

关键词： description logics neural networks multi-valued logics answer set programming

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Locksynth: Deriving Synchronization Code for Concurrent Data Structures with ASP

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theory and practice of logic programming 2023年第4期23卷 812-831页

作者： Varanasi, Sarat Chandra Mittal, Neeraj Gupta, Gopal Gen Elect Res Schenectady NY 12308 USA Univ Texas Dallas Richardson TX 75080 USA

We present Locksynth, a tool that automatically derives synchronization needed for destructive updates to concurrent data structures that involve a constant number of shared heap memory write operations. Locksynth serves as the implementation of our prior work on deriving abstract synchronization code. Designing concurrent data structures involves inferring correct synchronization code starting with a prior understanding of the sequential data structure's operations. Further, an understanding of shared memory model and the synchronization primitives is also required. The reasoning involved transforming a sequential data structure into its concurrent version can be performed using Answer Set programming, and we mechanized our approach in previous work. The reasoning involves deduction and abduction that can be succinctly modeled in ASP. We assume that the abstract sequential code of the data structure's operations is provided, alongside axioms that describe concurrent behavior. This information is used to automatically derive concurrent code for that data structure, such as dictionary operations for linked lists and binary search trees that involve a constant number of destructive update operations. We also are able to infer the correct set of locks (but not code synthesis) for external height-balanced binary search trees that involve left/right tree rotations. Locksynth performs the analyses required to infer correct sets of locks and as a final step, also derives the C++ synchronization code for the synthesized data structures. We also provide a performance analysis of the C++ code synthesized by Locksynth with the hand-crafted versions available from the Synchrobench microbenchmark suite. To the best of our knowledge, our tool is the first to employ ASP as a backend reasoner to perform concurrent data structure synthesis.

关键词： answer set programming concurrent data structure synthesis

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Alternating Fixpoint Operator for Hybrid MKNF Knowledge Bases as an Approximator of AFT

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theory and practice of logic programming 2022年第2期22卷 305-334页

作者： Liu, Fangfang You, Jia-Huai Shanghai Univ Sch Comp Engn & Sci Shanghai Peoples R China Univ Alberta Dept Comp Sci Edmonton AB Canada

Approximation fixpoint theory (AFT) provides an algebraic framework for the study of fixpoints of operators on bilattices and has found its applications in characterizing semantics for various classes of logic programs and nonmonotonic languages. In this paper, we show one more application of this kind: the alternating fixpoint operator by Knorr et al. for the study of the well-founded semantics for hybrid minimal knowledge and negation as failure (MKNF) knowledge bases is in fact an approximator of AFT in disguise, which, thanks to the abstraction power of AFT, characterizes not only the well-founded semantics but also two-valued as well as three-valued semantics for hybrid MKNF knowledge bases. Furthermore, we show an improved approximator for these knowledge bases, of which the least stable fixpoint is information richer than the one formulated from Knorr et al.'s construction. This leads to an improved computation for the well-founded semantics. This work is built on an extension of AFT that supports consistent as well as inconsistent pairs in the induced product bilattice, to deal with inconsistencies that arise in the context of hybrid MKNF knowledge bases. This part of the work can be considered generalizing the original AFT from symmetric approximators to arbitrary approximators.

关键词： approximation fixpoint theory hybrid MKNF knowledge bases logic programs answer set semantics description logics inconsistencies

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A Reliable Common-Sense Reasoning Socialbot Built Using LLMs and Goal-Directed ASP

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theory and practice of logic programming 2024年第4期24卷 606-627页

作者： Zeng, Yankai Rajasekharan, Abhiramon Basu, Kinjal Wang, Huaduo Arias, Joaquin Gupta, Gopal Univ Texas Dallas Richardson TX 75080 USA IBM Res Yorktown Hts NY USA Univ Ray Juan Carlos Madrid Spain

The development of large language models (LLMs), such as GPT, has enabled the construction of several socialbots, like ChatGPT, that are receiving a lot of attention for their ability to simulate a human conversation. However, the conversation is not guided by a goal and is hard to control. In addition, because LLMs rely more on pattern recognition than deductive reasoning, they can give confusing answers and have difficulty integrating multiple topics into a cohesive response. These limitations often lead the LLM to deviate from the main topic to keep the conversation interesting. We propose AutoCompanion, a socialbot that uses an LLM model to translate natural language into predicates (and vice versa) and employs commonsense reasoning based on answer set programming (ASP) to hold a social conversation with a human. In particular, we rely on s(CASP), a goal-directed implementation of ASP as the backend. This paper presents the framework design and how an LLM is used to parse user messages and generate a response from the s(CASP) engine output. To validate our proposal, we describe (real) conversations in which the chatbot's goal is to keep the user entertained by talking about movies and books, and s(CASP) ensures (i) correctness of answers, (ii) coherence (and precision) during the conversation-which it dynamically regulates to achieve its specific purpose-and (iii) no deviation from the main topic.

关键词： Answer Set programming (ASP) large language model socialbot

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The effect of different usage of the educational programming language in programming education on the programming anxiety and achievement

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EDUCATION AND INFORMATION TECHNOLOGIES 2022年第3期27卷 4171-4194页

作者： Demir, Faruk Anadolu Logist Co Balikesir Turkey

The abstract structure, logic, negative perceptions, and anxiety of programming are seen as obstacles to novice programmers. The importance of educational programming languages is increasing day by day in overcoming these obstacles. In this study, it was aimed to investigate the effect of educational programming language integration on academic achievement and programming anxiety level. The pretest-posttest test design without control group, which is one of the experimental methods, was used in the study, which was carried out on three groups consisting of the theory, practice and integration of the course into both theory and practice part. The groups determined by random sampling method consist of 87 people, 61 boys and 26 girls. Pretest-posttest method was used to determine academic success. During the application process, five performance tests were used to determined the change in success. The scale developed by Cheung (1990) in determining computer programming anxiety was adapted to Turkish by the validity and reliability study by the researcher and was used as pre-test and post-test. Variance and covariance analyzes were used to determine anxiety about academic success and programming, and the results of Kruskal-Wallis test analyzes were used for analysis of performance tests. It is concluded that educational programming languages can be used by integrating both the theory and practice of the course in order to increase academic success and in-class performance and reduce anxiety about computer programming.

关键词： programming education programming language programming anxiety programming success Scratch

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