In this paper we propose the usage of a framework combining standard deontic logic (SDL) and non-monotoniclogicprogramming -- deontic logic programs (DLP) -- to represent and reason about normative systems.
ISBN:
(纸本)9780981738130
In this paper we propose the usage of a framework combining standard deontic logic (SDL) and non-monotoniclogicprogramming -- deontic logic programs (DLP) -- to represent and reason about normative systems.
In this paper we integrate priorities in sequent-based argumentation. the former is a useful and extensively investigated tool in the context of non-monotonicreasoning, and the latter is a modular and general way of ...
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In this paper we integrate priorities in sequent-based argumentation. the former is a useful and extensively investigated tool in the context of non-monotonicreasoning, and the latter is a modular and general way of handling logical argumentation. their combination offers a platform for representing and reasoning with maximally consistent subsets of prioritized knowledge bases. Moreover, many frameworks of the resulting formalisms satisfy common rationality postulates and other desirable properties, like conflict preservation.
Deontic logicprogramming (DLP) is a framework combining deontic logic and non-monotoniclogicprogramming, and it is useful to represent and reason about normative systems. In this paper we propose an implementation ...
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ISBN:
(纸本)9781450319935
Deontic logicprogramming (DLP) is a framework combining deontic logic and non-monotoniclogicprogramming, and it is useful to represent and reason about normative systems. In this paper we propose an implementation for reasoning in DLP that combines, in a modular way, a reasoner for deontic logic with a reasoner for stable model semantics.
Enabling software systems to adjust their protection in continuously changing environments with imperfect context information is a grand challenging problem. the issue of uncertain reasoning based on imperfect informa...
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Enabling software systems to adjust their protection in continuously changing environments with imperfect context information is a grand challenging problem. the issue of uncertain reasoning based on imperfect information has been overlooked in traditional logicprogramming with classical negation when applied to dynamic systems. this paper sketches a non-monotonic approach based on Answer Set programming to reason with imperfect context data in adaptive security where there is little or no knowledge about certainty of the actions and events.
the proceedings contain 32 papers. the special focus in this conference is on Computational Methods in Systems Biology. the topics include: Reachability Design through Approximate Bayesian Computation;fast Enumeration...
ISBN:
(纸本)9783030313036
the proceedings contain 32 papers. the special focus in this conference is on Computational Methods in Systems Biology. the topics include: Reachability Design through Approximate Bayesian Computation;fast Enumeration of non-isomorphic Chemical Reaction Networks;a Large-Scale Assessment of Exact Model Reduction in the BioModels Repository;computing Difference Abstractions of Metabolic Networks Under Kinetic Constraints;BRE:IN - A Backend for reasoning About Interaction Networks with Temporal logic;the Kappa Simulator Made Interactive;biochemical Reaction Networks with Fuzzy Kinetic Parameters in Snoopy;compartmental Modeling Software: A Fast, Discrete Stochastic Framework for Biochemical and Epidemiological Simulation;spike – Reproducible Simulation Experiments with Configuration File Branching;sequential Reprogramming of Biological Network Fate;KAMIStudio: An Environment for Biocuration of Cellular Signalling Knowledge;A New Version of DAISY to Test Structural Identifiability of Biological Models;semi-quantitative Abstraction and Analysis of Chemical Reaction Networks (Extended Abstract);bayesian Parameter Estimation for Stochastic Reaction Networks from Steady-State Observations;wasserstein Distances for Estimating Parameters in Stochastic Reaction Networks;on Inferring Reactions from Data Time Series by a Statistical Learning Greedy Heuristics;Barbaric Robustness Monitoring Revisited for STL* in Parasim;Symmetry Breaking for GATA-1/PU.1 Model;scalable Control of Asynchronous Boolean Networks;Transcriptional Response of SK-N-AS Cells to Methamidophos (Extended Abstract);control Variates for Stochastic Simulation of Chemical Reaction Networks;separators for Polynomial Dynamic Systems with Linear Complexity;bounding First Passage Times in Chemical Reaction Networks: Poster Abstract;data-Informed Parameter Synthesis for Population Markov Chains;effective Computational Methods for Hybrid Stochastic Gene Networks;designing Distributed Cell Classifier Circuits Using a G
Recently, hardware and software engineers have been showing considerable attention to high-level parallelization and hardware synthesis methodologies. State-of-the-art approaches have benefited from the emergence of m...
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Recently, hardware and software engineers have been showing considerable attention to high-level parallelization and hardware synthesis methodologies. State-of-the-art approaches have benefited from the emergence of modern high-density Field Programmable Gate Arrays. In this paper, we explore the effectiveness of a formal methodology in the design of pipelined versions of a matrix multiplication algorithm. the suggested methodology adopts a functional programming notation for specifying algorithms and for reasoning about them. the parallel behavior of the specification is then derived and mapped onto hardware. Several pipelined implementations are developed with different performance characteristics. the refined designs are tested under Agility's RC-1000 reconfigurable computer with its 2 million gates Virtex-E FPGA. Performance analysis and evaluation of the proposed implementations are presented in comparison with an Intel Core 2 DUO processor.
作者:
N. LeoneP. RulloISI
CNR-c/o DEIS-UNICAL Rende Italy DIMET
Universita di Reggio Calabria Reggio Calabria Italy
the BQM system extends deductive database technology with knowledge structuring capabilities to provide an advanced environment for the development of data and knowledge-based applications. the system relies on a know...
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the BQM system extends deductive database technology with knowledge structuring capabilities to provide an advanced environment for the development of data and knowledge-based applications. the system relies on a knowledge representation language that combines the declarativeness of logicprogramming withthe notions of object, inheritance with exceptions and message passing. Exceptions are supported by allowing rules with negated heads. the use of exceptions inside the inheritance mechanism makes the language inherently nonmonotonic. the paper describes BQM focusing on boththe language and the implementation techniques. An informal overview of the language is first given. then, a number of techniques for efficient query evaluation are presented. these techniques significantly extend "traditional" deductive database query evaluation strategies to deal withnonmonotonicreasoning. A description of the architecture of the current prototype of the BQM system is also given.
In this paper, we present a new approach for lifted MAP inference in Markov logic Networks (MLNs). Our approach is based on the following key result that we prove in the paper: if an MLN has no shared terms then MAP i...
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In this paper, we present a new approach for lifted MAP inference in Markov logic Networks (MLNs). Our approach is based on the following key result that we prove in the paper: if an MLN has no shared terms then MAP inference over it can be reduced to MAP inference over a Markov network having the following properties: (i) the number of random variables in the Markov network is equal to the number of first-order atoms in the MLN;and (ii) the domain size of each variable in the Markov network is equal to the number of groundings of the corresponding first-order atom. We show that inference over this Markov network is exponentially more efficient than ground inference, namely inference over the Markov network obtained by grounding all first-order atoms in the MLN. We improve this result further by showing that if non-shared MLNs contain no self joins, namely every atom appears at most once in each of its formulas, then all variables in the corresponding Markov network need only be bi-valued. Our approach is quite general and can be easily applied to an arbitrary MLN by simply grounding all of its shared terms. the key feature of our approach is that because we reduce lifted inference to propositional inference, we can use any propositional MAP inference algorithm for performing lifted MAP inference. Within our approach, we experimented with two propositional MAP inference algorithms: Gurobi and MaxWalkSAT. Our experiments on several benchmark MLNs clearly demonstrate our approach is superior to ground MAP inference in terms of scalability and solution quality.
Probabilistic model checking is a well-established method for the automated quantitative system analysis. It has been used in various application areas such as coordination algorithms for distributed systems, communic...
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ISBN:
(纸本)9783642548048;9783642548031
Probabilistic model checking is a well-established method for the automated quantitative system analysis. It has been used in various application areas such as coordination algorithms for distributed systems, communication and multimedia protocols, biological systems, resilient systems or security. In this paper, we report on the experiences we made in inter-disciplinary research projects where we contribute with formal methods for the analysis of hardware and software systems. Many performance measures that have been identified as highly relevant by the respective domain experts refer to multiple objectives and require a good balance between two or more cost or reward functions, such as energy and utility. the formalization of these performance measures requires several concepts like quantiles, conditional probabilities and expectations and ratios of cost or reward functions that are not supported by state-of-the-art probabilistic model checkers. We report on our current work in this direction, including applications in the field of software product line verification.
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