arXiv - CS - Symbolic Computation最新文献_第7页

Effective alpha theory certification using interval arithmetic: alpha theory over regions 使用区间算术的有效阿尔法理论认证：区域阿尔法理论

arXiv - CS - Symbolic Computation Pub Date : 2024-05-08 DOI: arxiv-2405.04842

Kisun Lee

引用次数: 0

Certifying Phase Abstraction 认证阶段抽象

arXiv - CS - Symbolic Computation Pub Date : 2024-05-07 DOI: arxiv-2405.04297

Nils Froleyks, Emily Yu, Armin Biere, Keijo Heljanko

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How to generate all possible rational Wilf-Zeilberger forms? 如何生成所有可能的有理 Wilf-Zeilberger 形式？

arXiv - CS - Symbolic Computation Pub Date : 2024-05-03 DOI: arxiv-2405.02430

Shaoshi Chen, Christoph Koutschan, Yisen Wang

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Rings with common division, common meadows and their conditional equational theories 具有共同分割、共同草地的环及其条件等式理论

arXiv - CS - Symbolic Computation Pub Date : 2024-05-02 DOI: arxiv-2405.01733

Jan A Bergstra, John V Tucker

{"title":"Rings with common division, common meadows and their conditional equational theories","authors":"Jan A Bergstra, John V Tucker","doi":"arxiv-2405.01733","DOIUrl":"https://doi.org/arxiv-2405.01733","url":null,"abstract":"We examine the consequences of having a total division operation\u0000$frac{x}{y}$ on commutative rings. We consider two forms of binary division,\u0000one derived from a unary inverse, the other defined directly as a general\u0000operation; each are made total by setting $1/0$ equal to an error value $bot$,\u0000which is added to the ring. Such totalised divisions we call common divisions.\u0000In a field the two forms are equivalent and we have a finite equational\u0000axiomatisation $E$ that is complete for the equational theory of fields\u0000equipped with common division, called common meadows. These equational axioms\u0000$E$ turn out to be true of commutative rings with common division but only when\u0000defined via inverses. We explore these axioms $E$ and their role in seeking a\u0000completeness theorem for the conditional equational theory of common meadows.\u0000We prove they are complete for the conditional equational theory of commutative\u0000rings with inverse based common division. By adding a new proof rule, we can\u0000prove a completeness theorem for the conditional equational theory of common\u0000meadows. Although, the equational axioms $E$ fail with common division defined\u0000directly, we observe that the direct division does satisfies the equations in\u0000$E$ under a new congruence for partial terms called eager equality.","PeriodicalId":501033,"journal":{"name":"arXiv - CS - Symbolic Computation","volume":"284 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140887953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unification in the description logic $mathcal{FL}_bot$ 描述逻辑中的统一 $mathcal{FL}_bot$

arXiv - CS - Symbolic Computation Pub Date : 2024-05-01 DOI: arxiv-2405.00912

Barbara Morawska

引用次数: 0

Constrained Neural Networks for Interpretable Heuristic Creation to Optimise Computer Algebra Systems 用于优化计算机代数系统的可解释启发式创建的约束神经网络

arXiv - CS - Symbolic Computation Pub Date : 2024-04-26 DOI: arxiv-2404.17508

Dorian Florescu, Matthew England

引用次数: 0

Evolutionary Causal Discovery with Relative Impact Stratification for Interpretable Data Analysis 利用相对影响分层发现进化因果，实现可解释的数据分析

arXiv - CS - Symbolic Computation Pub Date : 2024-04-25 DOI: arxiv-2404.16361

Ou Deng, Shoji Nishimura, Atsushi Ogihara, Qun Jin

{"title":"Evolutionary Causal Discovery with Relative Impact Stratification for Interpretable Data Analysis","authors":"Ou Deng, Shoji Nishimura, Atsushi Ogihara, Qun Jin","doi":"arxiv-2404.16361","DOIUrl":"https://doi.org/arxiv-2404.16361","url":null,"abstract":"This study proposes Evolutionary Causal Discovery (ECD) for causal discovery\u0000that tailors response variables, predictor variables, and corresponding\u0000operators to research datasets. Utilizing genetic programming for variable\u0000relationship parsing, the method proceeds with the Relative Impact\u0000Stratification (RIS) algorithm to assess the relative impact of predictor\u0000variables on the response variable, facilitating expression simplification and\u0000enhancing the interpretability of variable relationships. ECD proposes an\u0000expression tree to visualize the RIS results, offering a differentiated\u0000depiction of unknown causal relationships compared to conventional causal\u0000discovery. The ECD method represents an evolution and augmentation of existing\u0000causal discovery methods, providing an interpretable approach for analyzing\u0000variable relationships in complex systems, particularly in healthcare settings\u0000with Electronic Health Record (EHR) data. Experiments on both synthetic and\u0000real-world EHR datasets demonstrate the efficacy of ECD in uncovering patterns\u0000and mechanisms among variables, maintaining high accuracy and stability across\u0000different noise levels. On the real-world EHR dataset, ECD reveals the\u0000intricate relationships between the response variable and other predictive\u0000variables, aligning with the results of structural equation modeling and\u0000shapley additive explanations analyses.","PeriodicalId":501033,"journal":{"name":"arXiv - CS - Symbolic Computation","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140806141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Symbolic Integration Algorithm Selection with Machine Learning: LSTMs vs Tree LSTMs 利用机器学习选择符号集成算法：LSTM 与树状 LSTM

arXiv - CS - Symbolic Computation Pub Date : 2024-04-23 DOI: arxiv-2404.14973

Rashid Barket, Matthew England, Jürgen Gerhard

引用次数: 0

Proceedings 18th International Workshop on Logical and Semantic Frameworks, with Applications and 10th Workshop on Horn Clauses for Verification and Synthesis 第 18 届逻辑和语义框架及应用国际研讨会暨第 10 届用于验证和合成的角分句研讨会论文集

arXiv - CS - Symbolic Computation Pub Date : 2024-04-21 DOI: arxiv-2404.13672

Temur KutsiaRISC, Johannes Kepler University Linz, Daniel VenturaINF, Universidade Federal de Goiás, David MonniauxCNRS - Verimag, José F. MoralesIMDEA

{"title":"Proceedings 18th International Workshop on Logical and Semantic Frameworks, with Applications and 10th Workshop on Horn Clauses for Verification and Synthesis","authors":"Temur KutsiaRISC, Johannes Kepler University Linz, Daniel VenturaINF, Universidade Federal de Goiás, David MonniauxCNRS - Verimag, José F. MoralesIMDEA","doi":"arxiv-2404.13672","DOIUrl":"https://doi.org/arxiv-2404.13672","url":null,"abstract":"This volume contains * The post-proceedings of the Eighteenth Logical and Semantic Frameworks with\u0000Applications (LSFA 2023). The meeting was held on July 1-2, 2023, organised by\u0000the Sapienza Universit`a di Roma, Italy. LSFA aims to bring researchers and\u0000students interested in theoretical and practical aspects of logical and\u0000semantic frameworks and their applications. The covered topics include proof\u0000theory, type theory and rewriting theory, specification and deduction\u0000languages, and formal semantics of languages and systems. * The post-proceedings of the Tenth Workshop on Horn clauses for Verification\u0000and Synthesis (HCVS 2023). The meeting was held on April 23, 2023 at the\u0000Institut Henri Poincar'e in Paris. HCVS aims to bring together researchers\u0000working in the two communities of constraint/ logic programming (e.g., ICLP and\u0000CP), program verification (e.g., CAV, TACAS, and VMCAI), and automated\u0000deduction (e.g., CADE, IJCAR), on the topics of Horn clause based analysis,\u0000verification, and synthesis.","PeriodicalId":501033,"journal":{"name":"arXiv - CS - Symbolic Computation","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140797740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On Modeling Multi-Criteria Decision Making with Uncertain Information using Probabilistic Rules 论利用概率规则建立具有不确定信息的多标准决策模型

arXiv - CS - Symbolic Computation Pub Date : 2024-04-20 DOI: arxiv-2404.13419

Shengxin Hong, Xiuyi Fan

{"title":"On Modeling Multi-Criteria Decision Making with Uncertain Information using Probabilistic Rules","authors":"Shengxin Hong, Xiuyi Fan","doi":"arxiv-2404.13419","DOIUrl":"https://doi.org/arxiv-2404.13419","url":null,"abstract":"Decision-making processes often involve dealing with uncertainty, which is\u0000traditionally addressed through probabilistic models. However, in practical\u0000scenarios, assessing probabilities reliably can be challenging, compounded by\u0000diverse perceptions of probabilistic information among decision makers. To\u0000address this variability and accommodate diverse preferences regarding\u0000uncertainty, we introduce the Probabilistic Abstract Decision Framework (PADF).\u0000PADF offers a structured approach for reasoning across different decision\u0000criteria, encompassing the optimistic, pessimistic, and Laplace perspectives,\u0000each tailored to distinct perceptions of uncertainty. We illustrate how PADF\u0000facilitates the computation of optimal decisions aligned with these criteria by\u0000leveraging probabilistic rules. Furthermore, we present strategies for\u0000optimizing the computational efficiency of these rules, leveraging appropriate\u0000independence assumptions to navigate the extensive search space inherent in\u0000PADF. Through these contributions, our framework provides a robust and\u0000adaptable tool for effectively navigating the complexities of decision-making\u0000under uncertainty.","PeriodicalId":501033,"journal":{"name":"arXiv - CS - Symbolic Computation","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140797817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0