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检索条件"任意字段=5th Workshop on Computer Science Logic, CSL 1991"

共 308 条记录，以下是1-10 订阅

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5th International workshop on Dynamic logic - New Trends and Applications, DaLi 2023

5th International Workshop on Dynamic Logic - New Trends and...

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5th International workshop on Dynamic logic - New Trends and Applications, DaLi 2023

ISBN: (纸本)9783031517761

the proceedings contain 8 papers. the special focus in this conference is on Dynamic logic - New Trends and Applications. the topics include: A logical Approach to Doxastic Causal Reasoning;automated Quantum Program Verification in Dynamic Quantum logic;preface;learning by Intervention in Simple Causal Domains;kleene Algebra of Weighted Programs with Domain;logic of the Hide and Seek Game: Characterization, Axiomatization, Decidability.

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Predictive theory of Mind Models Based on Public Announcement logic 1

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5th International workshop on Dynamic logic - New Trends and Applications (DaLi)

作者： Top, Jakob Dirk Jonker, Catholijn Verbrugge, Rineke de Weerd, Harmen Univ Groningen Groningen Netherlands Delft Univ Technol Delft Netherlands Leiden Univ Leiden Netherlands

ISBN: (数字)9783031517778

ISBN: (纸本)9783031517761;9783031517778

Epistemic logic can be used to reason about statements such as 'I know that you know that I know that.'. In this logic, and its extensions, it is commonly assumed that agents can reason about epistemic statements of arbitrary nesting depth. In contrast, empirical findings on theory of Mind, the ability to (recursively) reason about mental states of others, show that human recursive reasoning capability has an upper bound. In the present paper we work towards resolving this disparity by proposing some elements of a logic of bounded theory of Mind, built on Public Announcement logic. Using this logic, and a statistical method called Random-Effects Bayesian Model Selection, we estimate the distribution of theory of Mind levels in the participant population of a previous behavioral experiment. Despite not modeling stochastic behavior, we find that approximately three-quarters of participants' decisions can be described using theory of Mind. In contrast to previous empirical research, our models estimate the majority of participants to be second-order theory of Mind users.

关键词： theory of Mind Public Announcement logic Epistemic logic Behavioral Modeling Random-Effects Bayesian Model Selection Cognitive science

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5th International workshop on Cognition: Interdisciplinary Foundations, Models and Applications, CIFMA 2023 and 11th International workshop on Open Community approaches to Education, Research and Technology, OpenCERT 2023 held in conjunction with 21st International Conference on Software Engineering and Formal Methods, SEFM 2023

5th International Workshop on Cognition: Interdisciplinary F...

引用

5th International workshop on Cognition: Interdisciplinary Foundations, Models and Applications, CIFMA 2023 and 11th International workshop on Open Community approaches to Education, Research and Technology, OpenCERT 2023 held in conjunction with 21st International Conference on Software Engineering and Formal Methods, SEFM 2023

ISBN: (纸本)9783031660207

the proceedings contain 10 papers. the special focus in this conference is on Software Engineering and Formal Methods. the topics include: Private Memory Confers No Advantage;frequentist Probability logic;using Maude to Model Motivation in Human Behaviour;semantic Memory, Mnemonic Effort and Mnemonic Habit;scientific Understanding and the Explanatory Integration in Cognitive sciences;code Biology and Enactivism: Bringing Adaptors to Basic Minds;clinical Cognitive sciences;facets of Openness in a Serious Game: Opening up Format, Content, Software and Hardware.

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the logica System: Elevating SQL Databases to Declarative Data science Engines 5

The Logica System: Elevating SQL Databases to Declarative Da...

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5th International workshop on the Resurgence of Datalog in Academia and Industry, Datalog-2.0 2024

作者： Skvortsov, Evgeny Xia, Yilin Bowers, Shawn Ludäscher, Bertram Google LLC WA United States University of Illinois Urbana-Champaign School of Information Sciences IL United States Gonzaga University Department of Computer Science SpokaneWA United States

logica (= logic + aggregation) is a freely available, open-source, feature-enhanced version of Datalog that automatically compiles logic rules to a number of popular SQL platforms (DuckDB, SQLite, PostgreSQL, and BigQuery). logica combines beginner-friendly declarative features of Datalog with advanced analytical features needed by data science and ML practitioners when processing real-world data. Since logica is built on top of mature SQL implementations, these features can be executed robustly and scalably. logica allows beginners to seamlessly progress from simple textbook examples to intermediate and advanced use cases. We introduce logica with examples that combine aggregation, recursion, and negation in interesting and powerful ways. Additional advanced examples (maximum flow, matrix inversion, etc.) are demonstrated in an online notebook. logica source programs are compiled into (a) self-contained SQL scripts (for non-recursive and shallow-recursive problems) or (b) Python-driven iterations of SQL queries (when deep recursion is needed). logica’s practical and theoretical expressive power thus extends both SQL and (pure) Datalog. the logica system has been used for data science applications and training in industry, and in graduate-level courses in academia. © 2024 Copyright for this paper by its authors.

关键词： Data science

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On Challenges and Opportunities in the Translation of Deep Neural Networks into Finite Automata 5

On Challenges and Opportunities in the Translation of Deep N...

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5th workshop on Artificial Intelligence and Formal Verification, logic, Automata, and Synthesis, OVERLAY 2023

作者： Sälzer, Marco Alsmann, Eric Lange, Martin Faculty of Electrical Engineering and Computer Science University of Kassel Germany

the certification of safety properties and interpretation of neural networks is a topic of great concern. We recently devised an automata-based approach for addressing these tasks. It translates a neural network with ReLU activations into a finite automaton, capturing the input-output relation induced by the neural network. We report on a proof-of-concept implementation of this translation for a subclass neural networks, so called binarized neural networks, pointing out difficulties and opportunities of the proposed approach. © 2023 CEUR-WS. All rights reserved.

关键词： Finite automata

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Virtual Chip Design for the Digital Circuit Course 5

Virtual Chip Design for the Digital Circuit Course

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5th International workshop on Artificial Intelligence and Education, WAIE 2023

作者： Zou, Deyue Zhao, Yangzhen Qiu, Yang Gao, Xinyi School of Information and Communication Engineering Dalian University of Technology Dalian China School of Electronic Information Engineering Zhuhai College of Science and Technology Zhuhai China

ISBN: (纸本)9798350307061

the design and implementation of digital systems are becoming increasingly complex, and the methods of language description and system design are widely used. the teaching of the undergraduate stage is still based on the basic functions and usage methods of medium-sized logic chips. When students design digital logic circuits, they are often limited by the hardware conditions of fixed chips. the teaching mode with typical chips as clues has been separated from the current technical reality, and there are characteristics of backward technology. this paper proposes a set of virtual chips with high flexibility and strong practicability. It is a computer program with high selectivity based on the hardware implementation of field programmable gate array (FPGA) pocket machine to complete the corresponding digital logic operation function. It is a comprehensive application of hardware language based on the theoretical knowledge of the digital circuit course. the comparative analysis is carried out from two aspects of practical operation and theoretical learning by applying the virtual chip cases to the digital circuit course. the designed virtual chip could effectively improve the students' comprehensive application ability of digital circuit knowledge and the design ability of the actual digital circuit system. It has a specific supporting role in improving the teaching quality of introductory courses of electronic information speciality and cultivating students' practical ability. © 2023 IEEE.

关键词： Digital circuits

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Towards Compliance of Smart Contracts with the European Union Data Act 5

Towards Compliance of Smart Contracts with the European Unio...

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5th workshop on Artificial Intelligence and Formal Verification, logic, Automata, and Synthesis, OVERLAY 2023

作者： Olivieri, Luca Pasetto, Luca Department of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice Venice Italy Department of Computer Science University of Luxembourg Esch-sur-Alzette Luxembourg

the recent proposal of the European Union Data Act adopted by the European Commission has the goal to create fair rules for sharing and using data. An important role in this context is the one played by smart contracts, that are considered as a key technology to enable effective and consensual data sharing. For this purpose, the proposal sets up some requirements for smart contracts, specifically in the context of interoperability with applications that process data. there is a need for scientific work that explains which steps to take so that smart contracts comply with the proposal. this paper aims to begin addressing this gap by considering each one of the requirements and by providing a series of techniques from different areas of computer science to comply with them. © 2023 CEUR-WS. All rights reserved.

关键词： Interoperability

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Differentiable logics for Neural Network Training and Verification 5th

Differentiable Logics for Neural Network Training and Verifi...

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5th International workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS)

作者： Slusarz, Natalia Komendantskaya, Ekaterina Daggitt, Matthew L. Stewart, Robert Heriot Watt Univ Edinburgh Midlothian Scotland

ISBN: (纸本)9783031212215;9783031212222

Neural network (NN) verification is a problem that has drawn attention of many researchers. the specific nature of neural networks does away with the conventional assumption that a static program is given for verification as in the case of NNs multiple models can be used if one fails a new one can be trained leading to an approach called continuous verification, referring to the loop between training and verification. One tactic for improving the network's performance is through "constraint-based loss functions" - a method of using differentiable logic (DL) to translate logical constraints into loss functions which can then be used to train the network specifically to satisfy said constraint. In this paper we present a uniform way of defining a translation from logic syntax to a differentiable loss function then examine and compare the existing DLs. We explore mathematical properties desired in such translations and discuss the design space identifying possible directions of future work.

关键词： computer circuits

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5th International workshop on Software Verification and Formal Methods for ML-Enables Autonomous Systems, FoMLAS 2022 and 15th International workshop on Numerical Software Verification, NSV 2022

5th International Workshop on Software Verification and Form...

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5th International workshop on Software Verification and Formal Methods for ML-Enables Autonomous Systems, FoMLAS 2022 and 15th International workshop on Numerical Software Verification, NSV 2022

ISBN: (纸本)9783031212215

the proceedings contain 11 papers. the special focus in this conference is on Software Verification and Formal Methods for ML-Enables Autonomous Systems. the topics include: Neural Network Precision Tuning Using Stochastic Arithmetic;MLTL Multi-type (MLTLM): A logic for Reasoning About Signals of Different Types;a Cascade of Checkers for Run-time Certification of Local Robustness;CEG4N: Counter-Example Guided Neural Network Quantization Refinement;minimal Multi-Layer Modifications of Deep Neural Networks;differentiable logics for Neural Network Training and Verification;neural Networks in Imandra: Matrix Representation as a Verification Choice;self-correcting Neural Networks for Safe Classification;formal Specification for Learning-Enabled Autonomous Systems;verified Numerical Methods for Ordinary Differential Equations.

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Decomposing Natural logic Inferences for Neural NLI 5

Decomposing Natural Logic Inferences for Neural NLI

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5th workshop on Analyzing and Interpreting Neural Networks for NLP, BlackboxNLP 2022 hosted by the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Rozanova, Julia Ferreira, Deborah thayaparan, Mokanarangan Valentino, Marco Freitas, André Department of Computer Science University of Manchester United Kingdom Idiap Research Institute Switzerland

ISBN: (纸本)9781959429050

In the interest of interpreting neural NLI models and their reasoning strategies, we carry out a systematic probing study which investigates whether these models capture the crucial semantic features central to natural logic: monotonicity and concept inclusion. Correctly identifying valid inferences in downward-monotone contexts is a known stumbling block for NLI performance, subsuming linguistic phenomena such as negation scope and generalized quantifiers. To understand this difficulty, we emphasize monotonicity as a property of a context and examine the extent to which models capture relevant monotonicity information in the vector representations which are intermediate to their decision making process. Drawing on the recent advancement of the probing paradigm, we compare the presence of monotonicity features across various models. We find that monotonicity information is notably weak in the representations of popular NLI models which achieve high scores on benchmarks, and observe that previous improvements to these models based on fine-tuning strategies have introduced stronger monotonicity features together with their improved performance on challenge sets. © 2022 Association for Computational Linguistics.

关键词： Benchmarking

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