arXiv - CS - Symbolic Computation最新文献

Synthesizing Evolving Symbolic Representations for Autonomous Systems 为自主系统合成不断演化的符号表征

arXiv - CS - Symbolic Computation Pub Date : 2024-09-18 DOI: arxiv-2409.11756

Gabriele Sartor, Angelo Oddi, Riccardo Rasconi, Vieri Giuliano Santucci, Rosa Meo

{"title":"Synthesizing Evolving Symbolic Representations for Autonomous Systems","authors":"Gabriele Sartor, Angelo Oddi, Riccardo Rasconi, Vieri Giuliano Santucci, Rosa Meo","doi":"arxiv-2409.11756","DOIUrl":"https://doi.org/arxiv-2409.11756","url":null,"abstract":"Recently, AI systems have made remarkable progress in various tasks. Deep\u0000Reinforcement Learning(DRL) is an effective tool for agents to learn policies\u0000in low-level state spaces to solve highly complex tasks. Researchers have\u0000introduced Intrinsic Motivation(IM) to the RL mechanism, which simulates the\u0000agent's curiosity, encouraging agents to explore interesting areas of the\u0000environment. This new feature has proved vital in enabling agents to learn\u0000policies without being given specific goals. However, even though DRL\u0000intelligence emerges through a sub-symbolic model, there is still a need for a\u0000sort of abstraction to understand the knowledge collected by the agent. To this\u0000end, the classical planning formalism has been used in recent research to\u0000explicitly represent the knowledge an autonomous agent acquires and effectively\u0000reach extrinsic goals. Despite classical planning usually presents limited\u0000expressive capabilities, PPDDL demonstrated usefulness in reviewing the\u0000knowledge gathered by an autonomous system, making explicit causal\u0000correlations, and can be exploited to find a plan to reach any state the agent\u0000faces during its experience. This work presents a new architecture implementing\u0000an open-ended learning system able to synthesize from scratch its experience\u0000into a PPDDL representation and update it over time. Without a predefined set\u0000of goals and tasks, the system integrates intrinsic motivations to explore the\u0000environment in a self-directed way, exploiting the high-level knowledge\u0000acquired during its experience. The system explores the environment and\u0000iteratively: (a) discover options, (b) explore the environment using options,\u0000(c) abstract the knowledge collected and (d) plan. This paper proposes an\u0000alternative approach to implementing open-ended learning architectures\u0000exploiting low-level and high-level representations to extend its knowledge in\u0000a virtuous loop.","PeriodicalId":501033,"journal":{"name":"arXiv - CS - Symbolic Computation","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253478","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

Introducing Quantification into a Hierarchical Graph Rewriting Language 将量化引入层次图重写语言

arXiv - CS - Symbolic Computation Pub Date : 2024-09-17 DOI: arxiv-2409.11015

Haruto Mishina, Kazunori Ueda

引用次数: 0

Symbolic Regression with a Learned Concept Library 利用学习概念库进行符号回归

arXiv - CS - Symbolic Computation Pub Date : 2024-09-14 DOI: arxiv-2409.09359

Arya Grayeli, Atharva Sehgal, Omar Costilla-Reyes, Miles Cranmer, Swarat Chaudhuri

引用次数: 0

Towards Verified Polynomial Factorisation 实现验证多项式因式分解

arXiv - CS - Symbolic Computation Pub Date : 2024-09-14 DOI: arxiv-2409.09533

James H. Davenport

引用次数: 0

Active Symbolic Discovery of Ordinary Differential Equations via Phase Portrait Sketching 通过相位肖像素描主动符号化发现常微分方程

arXiv - CS - Symbolic Computation Pub Date : 2024-09-02 DOI: arxiv-2409.01416

Nan Jiang, Md Nasim, Yexiang Xue

{"title":"Active Symbolic Discovery of Ordinary Differential Equations via Phase Portrait Sketching","authors":"Nan Jiang, Md Nasim, Yexiang Xue","doi":"arxiv-2409.01416","DOIUrl":"https://doi.org/arxiv-2409.01416","url":null,"abstract":"Discovering Ordinary Differential Equations (ODEs) from trajectory data is a\u0000crucial task in AI-driven scientific discovery. Recent methods for symbolic\u0000discovery of ODEs primarily rely on fixed training datasets collected a-priori,\u0000often leading to suboptimal performance, as observed in our experiments in\u0000Figure 1. Inspired by active learning, we explore methods for querying\u0000informative trajectory data to evaluate predicted ODEs, where data are obtained\u0000by the specified initial conditions of the trajectory. Chaos theory indicates\u0000that small changes in the initial conditions of a dynamical system can result\u0000in vastly different trajectories, necessitating the maintenance of a large set\u0000of initial conditions of the trajectory. To address this challenge, we\u0000introduce Active Symbolic Discovery of Ordinary Differential Equations via\u0000Phase Portrait Sketching (APPS). Instead of directly selecting individual\u0000initial conditions, APPS first identifies an informative region and samples a\u0000batch of initial conditions within that region. Compared to traditional active\u0000learning methods, APPS eliminates the need for maintaining a large amount of\u0000data. Extensive experiments demonstrate that APPS consistently discovers more\u0000accurate ODE expressions than baseline methods using passively collected\u0000datasets.","PeriodicalId":501033,"journal":{"name":"arXiv - CS - Symbolic Computation","volume":"96 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211406","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

Boolean Matrix Logic Programming 布尔矩阵逻辑编程

arXiv - CS - Symbolic Computation Pub Date : 2024-08-19 DOI: arxiv-2408.10369

Lun Ai, Stephen H. Muggleton

引用次数: 0

Cognitive LLMs: Towards Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-making 认知 LLMs：为制造决策整合认知架构和大型语言模型

arXiv - CS - Symbolic Computation Pub Date : 2024-08-17 DOI: arxiv-2408.09176

Siyu Wu, Alessandro Oltramari, Jonathan Francis, C. Lee Giles, Frank E. Ritter

{"title":"Cognitive LLMs: Towards Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-making","authors":"Siyu Wu, Alessandro Oltramari, Jonathan Francis, C. Lee Giles, Frank E. Ritter","doi":"arxiv-2408.09176","DOIUrl":"https://doi.org/arxiv-2408.09176","url":null,"abstract":"Resolving the dichotomy between the human-like yet constrained reasoning\u0000processes of Cognitive Architectures and the broad but often noisy inference\u0000behavior of Large Language Models (LLMs) remains a challenging but exciting\u0000pursuit, for enabling reliable machine reasoning capabilities in production\u0000systems. Because Cognitive Architectures are famously developed for the purpose\u0000of modeling the internal mechanisms of human cognitive decision-making at a\u0000computational level, new investigations consider the goal of informing LLMs\u0000with the knowledge necessary for replicating such processes, e.g., guided\u0000perception, memory, goal-setting, and action. Previous approaches that use LLMs\u0000for grounded decision-making struggle with complex reasoning tasks that require\u0000slower, deliberate cognition over fast and intuitive inference -- reporting\u0000issues related to the lack of sufficient grounding, as in hallucination. To\u0000resolve these challenges, we introduce LLM-ACTR, a novel neuro-symbolic\u0000architecture that provides human-aligned and versatile decision-making by\u0000integrating the ACT-R Cognitive Architecture with LLMs. Our framework extracts\u0000and embeds knowledge of ACT-R's internal decision-making process as latent\u0000neural representations, injects this information into trainable LLM adapter\u0000layers, and fine-tunes the LLMs for downstream prediction. Our experiments on\u0000novel Design for Manufacturing tasks show both improved task performance as\u0000well as improved grounded decision-making capability of our approach, compared\u0000to LLM-only baselines that leverage chain-of-thought reasoning strategies.","PeriodicalId":501033,"journal":{"name":"arXiv - CS - Symbolic Computation","volume":"322 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227915","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

ONSEP: A Novel Online Neural-Symbolic Framework for Event Prediction Based on Large Language Model ONSEP：基于大型语言模型的事件预测在线神经符号框架

arXiv - CS - Symbolic Computation Pub Date : 2024-08-14 DOI: arxiv-2408.07840

Xuanqing Yu, Wangtao Sun, Jingwei Li, Kang Liu, Chengbao Liu, Jie Tan

{"title":"ONSEP: A Novel Online Neural-Symbolic Framework for Event Prediction Based on Large Language Model","authors":"Xuanqing Yu, Wangtao Sun, Jingwei Li, Kang Liu, Chengbao Liu, Jie Tan","doi":"arxiv-2408.07840","DOIUrl":"https://doi.org/arxiv-2408.07840","url":null,"abstract":"In the realm of event prediction, temporal knowledge graph forecasting (TKGF)\u0000stands as a pivotal technique. Previous approaches face the challenges of not\u0000utilizing experience during testing and relying on a single short-term history,\u0000which limits adaptation to evolving data. In this paper, we introduce the\u0000Online Neural-Symbolic Event Prediction (ONSEP) framework, which innovates by\u0000integrating dynamic causal rule mining (DCRM) and dual history augmented\u0000generation (DHAG). DCRM dynamically constructs causal rules from real-time\u0000data, allowing for swift adaptation to new causal relationships. In parallel,\u0000DHAG merges short-term and long-term historical contexts, leveraging a\u0000bi-branch approach to enrich event prediction. Our framework demonstrates\u0000notable performance enhancements across diverse datasets, with significant\u0000Hit@k (k=1,3,10) improvements, showcasing its ability to augment large language\u0000models (LLMs) for event prediction without necessitating extensive retraining.\u0000The ONSEP framework not only advances the field of TKGF but also underscores\u0000the potential of neural-symbolic approaches in adapting to dynamic data\u0000environments.","PeriodicalId":501033,"journal":{"name":"arXiv - CS - Symbolic Computation","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211410","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

Algebraic Representations for Faster Predictions in Convolutional Neural Networks 用代数表示法加快卷积神经网络的预测速度

arXiv - CS - Symbolic Computation Pub Date : 2024-08-14 DOI: arxiv-2408.07815

Johnny Joyce, Jan Verschelde

引用次数: 0

MathPartner: An Artificial Intelligence Cloud Service MathPartner：人工智能云服务

arXiv - CS - Symbolic Computation Pub Date : 2024-08-09 DOI: arxiv-2408.04999

Gennadi Malaschonok, Alexandr Seliverstov

{"title":"MathPartner: An Artificial Intelligence Cloud Service","authors":"Gennadi Malaschonok, Alexandr Seliverstov","doi":"arxiv-2408.04999","DOIUrl":"https://doi.org/arxiv-2408.04999","url":null,"abstract":"In a broad sense, artificial intelligence is a service to find a solution to\u0000complex intellectual problems. In this sense, the MathPartner service provides\u0000artificial intelligence that allows us to formulate questions and receive\u0000answers to questions formulated in a mathematical language. For mathematicians\u0000and physicists today, such a language is LaTeX. The MathPartner service uses a\u0000dialect of LaTeX, which is called Mathpar. The service is a cloud-based\u0000computer algebra system and provides users with the opportunity to solve many\u0000mathematical problems. In this publication, we focus only on a small class of\u0000extremum problems, which are widely applied in economics, management,\u0000logistics, and in many engineering fields. In particular, we consider the\u0000shortest path problem and discuss an algorithm that is based on the tropical\u0000mathematics. The ability to work with many types of classical and tropical\u0000algebras, which are freely available to users, is an important distinguishing\u0000feature of this intelligent tool for symbolic-numerical calculations. We also\u0000consider the use of the simplex algorithm for solving optimization problems.","PeriodicalId":501033,"journal":{"name":"arXiv - CS - Symbolic Computation","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141938851","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