arXiv - CS - Symbolic Computation最新文献_第2页

First steps towards Computational Polynomials in Lean 精益计算多项式的第一步

arXiv - CS - Symbolic Computation Pub Date : 2024-08-08 DOI: arxiv-2408.04564

James Harold Davenport

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An Abstraction-Preserving Block Matrix Implementation in Maple 在马普尔中实现保留抽象的块矩阵

arXiv - CS - Symbolic Computation Pub Date : 2024-08-04 DOI: arxiv-2408.02112

David J. Jeffrey, Stephen M. Watt

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Recent Developments in Real Quantifier Elimination and Cylindrical Algebraic Decomposition 实量子消除和圆柱代数分解的最新进展

arXiv - CS - Symbolic Computation Pub Date : 2024-07-29 DOI: arxiv-2407.19781

Matthew England

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Equality of morphic sequences 形态序列的平等

arXiv - CS - Symbolic Computation Pub Date : 2024-07-22 DOI: arxiv-2407.15721

Hans Zantema

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Algebraic anti-unification 代数反统一

arXiv - CS - Symbolic Computation Pub Date : 2024-07-22 DOI: arxiv-2407.15510

Christian Antić

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Error Detection and Constraint Recovery in Hierarchical Multi-Label Classification without Prior Knowledge 无先验知识分层多标签分类中的错误检测和约束恢复

arXiv - CS - Symbolic Computation Pub Date : 2024-07-21 DOI: arxiv-2407.15192

Joshua Shay Kricheli, Khoa Vo, Aniruddha Datta, Spencer Ozgur, Paulo Shakarian

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From Words to Worlds: Compositionality for Cognitive Architectures 从文字到世界：认知架构的组合性

arXiv - CS - Symbolic Computation Pub Date : 2024-07-18 DOI: arxiv-2407.13419

Ruchira Dhar, Anders Søgaard

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Evaluating Task-Oriented Dialogue Consistency through Constraint Satisfaction 通过约束满足评估面向任务的对话一致性

arXiv - CS - Symbolic Computation Pub Date : 2024-07-16 DOI: arxiv-2407.11857

Tiziano Labruna, Bernardo Magnini

{"title":"Evaluating Task-Oriented Dialogue Consistency through Constraint Satisfaction","authors":"Tiziano Labruna, Bernardo Magnini","doi":"arxiv-2407.11857","DOIUrl":"https://doi.org/arxiv-2407.11857","url":null,"abstract":"Task-oriented dialogues must maintain consistency both within the dialogue\u0000itself, ensuring logical coherence across turns, and with the conversational\u0000domain, accurately reflecting external knowledge. We propose to conceptualize\u0000dialogue consistency as a Constraint Satisfaction Problem (CSP), wherein\u0000variables represent segments of the dialogue referencing the conversational\u0000domain, and constraints among variables reflect dialogue properties, including\u0000linguistic, conversational, and domain-based aspects. To demonstrate the\u0000feasibility of the approach, we utilize a CSP solver to detect inconsistencies\u0000in dialogues re-lexicalized by an LLM. Our findings indicate that: (i) CSP is\u0000effective to detect dialogue inconsistencies; and (ii) consistent dialogue\u0000re-lexicalization is challenging for state-of-the-art LLMs, achieving only a\u00000.15 accuracy rate when compared to a CSP solver. Furthermore, through an\u0000ablation study, we reveal that constraints derived from domain knowledge pose\u0000the greatest difficulty in being respected. We argue that CSP captures core\u0000properties of dialogue consistency that have been poorly considered by\u0000approaches based on component pipelines.","PeriodicalId":501033,"journal":{"name":"arXiv - CS - Symbolic Computation","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141721172","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

Hyperion - A fast, versatile symbolic Gaussian Belief Propagation framework for Continuous-Time SLAM Hyperion - 用于连续时间 SLAM 的快速、多用途符号高斯信念传播框架

arXiv - CS - Symbolic Computation Pub Date : 2024-07-09 DOI: arxiv-2407.07074

David Hug, Ignacio Alzugaray, Margarita Chli

引用次数: 0

A Neurosymbolic Approach to Adaptive Feature Extraction in SLAM SLAM 中自适应特征提取的神经符号方法

arXiv - CS - Symbolic Computation Pub Date : 2024-07-09 DOI: arxiv-2407.06889

Yasra Chandio, Momin A. Khan, Khotso Selialia, Luis Garcia, Joseph DeGol, Fatima M. Anwar

{"title":"A Neurosymbolic Approach to Adaptive Feature Extraction in SLAM","authors":"Yasra Chandio, Momin A. Khan, Khotso Selialia, Luis Garcia, Joseph DeGol, Fatima M. Anwar","doi":"arxiv-2407.06889","DOIUrl":"https://doi.org/arxiv-2407.06889","url":null,"abstract":"Autonomous robots, autonomous vehicles, and humans wearing mixed-reality\u0000headsets require accurate and reliable tracking services for safety-critical\u0000applications in dynamically changing real-world environments. However, the\u0000existing tracking approaches, such as Simultaneous Localization and Mapping\u0000(SLAM), do not adapt well to environmental changes and boundary conditions\u0000despite extensive manual tuning. On the other hand, while deep learning-based\u0000approaches can better adapt to environmental changes, they typically demand\u0000substantial data for training and often lack flexibility in adapting to new\u0000domains. To solve this problem, we propose leveraging the neurosymbolic program\u0000synthesis approach to construct adaptable SLAM pipelines that integrate the\u0000domain knowledge from traditional SLAM approaches while leveraging data to\u0000learn complex relationships. While the approach can synthesize end-to-end SLAM\u0000pipelines, we focus on synthesizing the feature extraction module. We first\u0000devise a domain-specific language (DSL) that can encapsulate domain knowledge\u0000on the important attributes for feature extraction and the real-world\u0000performance of various feature extractors. Our neurosymbolic architecture then\u0000undertakes adaptive feature extraction, optimizing parameters via learning\u0000while employing symbolic reasoning to select the most suitable feature\u0000extractor. Our evaluations demonstrate that our approach, neurosymbolic Feature\u0000EXtraction (nFEX), yields higher-quality features. It also reduces the pose\u0000error observed for the state-of-the-art baseline feature extractors ORB and\u0000SIFT by up to 90% and up to 66%, respectively, thereby enhancing the system's\u0000efficiency and adaptability to novel environments.","PeriodicalId":501033,"journal":{"name":"arXiv - CS - Symbolic Computation","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141576696","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