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ASP-Based Encoding Model of Architecture Synthesis for Smart Cameras in Distributed Networks

ACM TRANSACTIONS ON DESIGN AUTOMATION OF ELECTRONIC SYSTEMS 2015年第2期20卷 27-27页

作者： Yonga, Franck Mefenza, Michael Bobda, Christophe Univ Arkansas Dept Comp Sci & Comp Engn Fayetteville AR 72701 USA

A synthesis approach based on answer set programming (ASP) for heterogeneous system-on-chips to be used in distributed camera networks is presented. In such networks, the tight resource limitations represent a major challenge for application development. Starting with a high-level description of applications, the physical constraints of the target devices, and the specification of network configuration, our goal is to produce optimal computing infrastructures made of a combination of hardware and software components for each node of the network. Optimization aims at maximizing speed while minimizing chip area and power consumption. Additionally, by performing the architecture synthesis simultaneously for all cameras in the network, we are able to minimize the overall utilization of communication resources and consequently reduce power consumption. Because of its reconfiguration capabilities, a Field Programmable Gate Array (FPGA) has been chosen as the target device, which enhances the exploration of several design alternatives. We present several realistic network scenarios to evaluate and validate the proposed synthesis approach.

关键词： Design Measurement Performance Theory System design architecture synthesis answer set programming multiobjective optimization technology mapping power minimization FPGA reconfigurable systems distributed smart cameras

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Expressiveness of Logic Programs under the General Stable Model Semantics

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ACM TRANSACTIONS ON COMPUTATIONAL LOGIC 2017年第2期18卷 9-9页

作者： Zhang, Heng Zhang, Yan Tianjin Univ Sch Comp Software Tianjin 300072 Peoples R China Western Sydney Univ Sch Comp Engn & Math Locked Bag 1797 Penrith NSW 2751 Australia Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing 100190 Peoples R China

Stable model semantics had been recently generalized to non-Herbrand structures by several works, which provides a unified framework and solid logical foundations for answer set programming. This article focuses on the expressiveness of normal and disjunctive logic programs under general stable model semantics. A translation from disjunctive logic programs to normal logic programs is proposed for infinite structures. Over finite structures, some disjunctive logic programs are proved to be intranslatable to normal logic programs if the arities of auxiliary predicates and functions are bounded in a certain way. The equivalence of the expressiveness of normal logic programs and disjunctive logic programs over arbitrary structures is also shown to coincide with that over finite structures and coincide with whether the complexity class NP is closed under complement. Moreover, to obtain a more explicit picture of the expressiveness, some intertranslatability results between logic program classes, and fragments of second-order logic are established.

关键词： Languages Theory answer set programming expressiveness complexity nonmonotonic reasoning second-order logic

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Combining neural and symbolic approaches to solve the Picasso problem: A first step

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DISPLAYS 2022年 74卷

作者： Gliozzi, Valentina Pozzato, Gian Luca Valese, Alberto Univ Torino Dipartimento Informat Cso Svizzera 185 I-10149 Turin Italy

In this work we create a bridge between Convolutional Neural Networks and answer set programming in order to tackle the known Picasso Problem in the automated detection of images. The basic idea is to first exploit the main features of the neural network approach for image recognition, and then to address the problem of identifying well-formed (not meshed up) images by means of explicit knowledge expressed by logical rules. Preliminary experiments suggest that the proposed approach is promising and can be considered as a first step in the direction of solving the Picasso Problem, as well as a witness of the benefits that can be obtained by the combination of a neural approach with a pure symbolic one.

关键词： Neural networks answer set programming Automated reasoning

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Commonsense visual sensemaking for autonomous driving-On generalised neurosymbolic online abduction integrating vision and semantics

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ARTIFICIAL INTELLIGENCE 2021年 299卷 103522-103522页

作者： Suchan, Jakob Bhatt, Mehul Varadarajan, Srikrishna Univ Bremen Bremen Germany Orebro Univ Orebro Sweden Cognit Vis CoDesign Lab EU Stockholm Sweden

We demonstrate the need and potential of systematically integrated vision and semantics solutions for visual sensemaking in the backdrop of autonomous driving. A general neurosymbolic method for online visual sensemaking using answer set programming (ASP) is systematically formalised and fully implemented. The method integrates state of the art in visual computing, and is developed as a modular framework that is generally usable within hybrid architectures for realtime perception and control. We evaluate and demonstrate with community established benchmarks KITTIMOD, MOT2017, and MOT-2020. As use-case, we focus on the significance of human-centred visual sensemaking -e.g., involving semantic representation and explainability, questionanswering, commonsense interpolation- in safety-critical autonomous driving situations. The developed neurosymbolic framework is domain-independent, with the case of autonomous driving designed to serve as an exemplar for online visual sensemaking in diverse cognitive interaction settings in the backdrop of select human-centred AI technology design considerations. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Cognitive vision Deep semantics Declarative spatial reasoning Knowledge representation and reasoning Commonsense reasoning Visual abduction answer set programming Autonomous driving Human-centred computing and design Standardisation in driving technology Spatial cognition and AI

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ASP and subset minimality: Enumeration, cautious reasoning and MUSes

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ARTIFICIAL INTELLIGENCE 2023年第1期320卷

作者： Alviano, Mario Dodaro, Carmine Fiorentino, Salvatore Previti, Alessandro Ricca, Francesco Univ Calabria Dept Math & Comp Sci Via P BucciCubo 30B I-87036 Arcavacata Di Rende CS Italy Ericsson Res Torshamnsgatan 23 S-16440 Stockholm Sweden

answer set programming (ASP) is a well-known logic-based formalism that has been used to model and solve a variety of AI problems. For several years, ASP implementations primarily focused on the main computational task: the computation of one answer set of a (logic) program. Nonetheless, several AI problems, that can be conveniently modelled in ASP, require to enumerate solutions characterized by an optimality property that can be expressed in terms of subset-minimality with respect to some objective atoms. In this context, solutions are often either (i) answer sets that are subset-minimal w.r.t. the objective atoms or (ii) atoms that are contained in all subset-minimal answer sets, or (iii) sets of atoms that enforce the absence of answer sets on the ASP program at hand - such sets are referred to as minimal unsatisfiable subsets (MUSes). In all the above-mentioned cases, the corresponding computational task is currently not supported by plain state-of-the-art ASP solvers. In this paper, we study formally these tasks and fill the gap in current implementations by proposing several algorithms to enumerate MUSes and subset-minimal answer sets, as well as perform cautious reasoning on subset-minimal answer sets. We implement our algorithms on top of wASp and perform an experimental analysis on several hard benchmarks showing the good performance of our implementation. (c) 2023 Elsevier B.V. All rights reserved.

关键词： answer set programming Enumeration Minimal models Cautious reasoning MUS

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Tableau Calculi for Logic Programs under answer set Semantics

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ACM TRANSACTIONS ON COMPUTATIONAL LOGIC 2013年第2期14卷 15-15页

作者： Gebser, Martin Schaub, Torsten Univ Potsdam Inst Informat D-14482 Potsdam Germany

We introduce formal proof systems based on tableau methods for analyzing computations in answer set programming (ASP). Our approach furnishes fine-grained instruments for characterizing operations as well as strategies of ASP solvers. The granularity is detailed enough to capture a variety of propagation and choice methods of algorithms used for ASP solving, also incorporating SAT-based and conflict-driven learning approaches to some extent. This provides us with a uniform setting for identifying and comparing fundamental properties of ASP solving approaches. In particular, we investigate their proof complexities and show that the run-times of best-case computations can vary exponentially between different existing ASP solvers. Apart from providing a framework for comparing ASP solving approaches, our characterizations also contribute to their understanding by pinning down the constitutive atomic operations. Furthermore, our framework is flexible enough to integrate new inference patterns, and so to study their relation to existing ones. To this end, we generalize our approach and provide an extensible basis aiming at a modular incorporation of additional language constructs. This is exemplified by augmenting our basic tableau methods with cardinality constraints and disjunctions.

关键词： Theory answer set programming tableau calculi proof complexity

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Extended Hapicare: A telecare system with probabilistic diagnosis and self-adaptive treatment

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 186卷 115749-115749页

作者： Kordestani, Hossain Mojarad, Roghayeh Chibani, Abdelghani Barkaoui, Kamel Amirat, Yacine Zahran, Wagdy Maidis SAS Dept Res & Innovat Chatou France Univ Paris Est Paris Creteil Lab Image Signal & Intelligent Syst LISSI Vitry Sur Seine France Conservatoire Natl Arts & Metiers Ctr Etud & Rech Informat & Commun Paris France

The massive growth of the population with chronic diseases calls for a telecare system to enhance their quality of life and reduce their treatment costs. Most of the current solutions depend on reliable data, deterministic rules, or the similarity of patients, while studies have shown otherwise. To this end, in this paper, we have extended our previous work on the Hapicare framework to integrate probabilistic diagnosis and self-adaptive treatment. Our new framework enables sensors' datastream analysis and online decision-making. Its ontology-based reasoning uses Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT) ontology to add contextual information to the collected data. Moreover, probabilistic reasoning is applied for diagnosis and screening to manage the uncertainty and unreliability of data as well as the indeterministic medical rules. The treatment system is designed to be modifiable by the experts and automatically adaptable to patients' needs. The probabilistic diagnosis performance has been evaluated based on two public datasets regarding symptoms and risk factors of two chronic diseases: chronic kidney disease and dermatologic disease. The results show that our solution outperforms a classical classifier specifically when more than 40% of the data are missing. The proposed framework is also validated using four scenarios. The evaluation results demonstrate the ability of the proposed framework to help patients and doctors diagnose and treat medical conditions and episodes.

关键词： Medical telemonitoring Bayesian reasoning answer set programming Self-adaptive systems

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Preferred First-Order answer set Programs

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ACM TRANSACTIONS ON COMPUTATIONAL LOGIC 2014年第2期15卷 11-11页

作者： Asuncion, Vernon Zhang, Yan Zhou, Yi Univ Western Sydney Sch Comp Engn & Math Penrith NSW 2751 Australia

In this article, we consider the issue of how first-order answer set programs can be extended for handling preference reasoning. To this end, we propose a progression-based preference semantics for first-order answer set programs while explicit preference relations are presented. We study essential properties of the proposed preferred answer set semantics. To understand the expressiveness of preferred first-order answer set programming, we further specify a second-order logic representation which precisely characterizes the progression-based preference semantics.

关键词： Languages Theory answer set programming first-order nonmonotonic reasoning preference second-order logic

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Fixed point semantics for stream reasoning

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ARTIFICIAL INTELLIGENCE 2020年 288卷 103370-103370页

作者： Antic, Christian Vienna Univ Technol Inst Discrete Math & Geometry Wiedner Hauptstr 8-10 A-1040 Vienna Austria

Reasoning over streams of input data is an essential part of human intelligence. During the last decade stream reasoning has emerged as a research area within the AI-community with many potential applications. In fact, the increased availability of streaming data via services like Google and Facebook has raised the need for reasoning engines coping with data that changes at high rate. Recently, the rule-based formalism LARS for non-monotonic stream reasoning under the answer set semantics has been introduced. Syntactically, LARS programs are logic programs with negation incorporating operators for temporal reasoning, most notably window operators for selecting relevant time points. Unfortunately, by preselecting fixed intervals for the semantic evaluation of programs, the rigid semantics of LARS programs is not flexible enough to constructively cope with rapidly changing data dependencies. Moreover, we show that defining the answer set semantics of LARS in terms of FLP reducts leads to undesirable circular justifications similar to other ASP extensions. This paper fixes all of the aforementioned shortcomings of LARS. More precisely, we contribute to the foundations of stream reasoning by providing an operational fixed point semantics for a fully flexible variant of LARS and we show that our semantics is sound and constructive in the sense that answer sets are derivable bottom-up and free of circular justifications. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Dynamic data answer set programming Stream reasoning

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Designing experiments to discriminate families of logic models

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2015年第0期3卷 131-131页

作者： Videla, Santiago Konokotina, Irina Alexopoulos, Leonidas G. Saez-Rodriguez, Julio Schaub, Torsten Siegel, Anne Guziolowski, Carito CNRS UMR 6074 IRISA Rennes France INRIA Dyliss Project Rennes France Univ Potsdam Inst Informat Potsdam Germany Consejo Nacl Invest Cient & Tecn Fdn Inst Leloir LBSI Buenos Aires DF Argentina Ecole Cent Nantes IRCCyN UMR CNRS 6597 1 Rue Noe F-44321 Nantes France Natl Tech Univ Athens Dept Mech Engn Athens Greece European Bioinformat Inst European Mol Biol Lab Hinxton England

Logic models of signaling pathways are a promising way of building effective in silico functional models of a cell, in particular of signaling pathways. The automated learning of Boolean logic models describing signaling pathways can be achieved by training to phosphoproteomics data, which is particularly useful if it is measured upon different combinations of perturbations in a high-throughput fashion. However, in practice, the number and type of allowed perturbations are not exhaustive. Moreover, experimental data are unavoidably subjected to noise. As a result, the learning process results in a family of feasible logical networks rather than in a single model. This family is composed of logic models implementing different internal wirings for the system and therefore the predictions of experiments from this family may present a significant level of variability, and hence uncertainty. In this paper, we introduce a method based on answer set programming to propose an optimal experimental design that aims to narrow down the variability (in terms of input-output behaviors) within families of logical models learned from experimental data. We study how the fitness with respect to the data can be improved after an optimal selection of signaling perturbations and how we learn optimal logic models with minimal number of experiments. The methods are applied on signaling pathways in human liver cells and phosphoproteomics experimental data. Using 25% of the experiments, we obtained logical models with fitness scores (mean square error) 15% close to the ones obtained using all experiments, illustrating the impact that our approach can have on the design of experiments for efficient model calibration.

关键词： experimental design Boolean logic models phosphoproteomic answer set programming signaling networks

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