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knowledge representation and reasoning in the context of automated production planning

JOURNAL OF INTELLIGENT MANUFACTURING 2025年 1-20页

作者： Sahota, Harkiran Klodowski, Adam Daimler Buses Neu Ulm Germany LUT Univ Lappeenranta Finland

In the manufacturing industry the step of generating the assembly plans for products is crucial but very time and labour intensive as most of the work has to be done manually. Especially for products with high customisability this means, a new plan is required for every custom order. To reduce the complexity, a new hybrid approach for the generation of these assembly plans is introduced, based on knowledge representation and reasoning. The focus of the approach is to be deployable in real-world production instead of only focusing on research environments. Therefore, a knowledge base is generated, specifying the preconditions and details about different operations linked to geometrical features. To preserve these valuable information Answer Set Programming (ASP) is used to define rules. By this, the generic information of when to apply which operation can be separated from the product-specific information of how a product looks, and therefore, be stored centrally and reused for any product or even different applications without any changes. Performing a geometric analysis on the product model allows extracting important geometrical features, that can then be used to derive the necessary information from the corresponding rules. In all of this, the human intervention is crucial to cover even complex assemblies and to strengthen the acceptance of the method. To showcase the functionality of this approach, a simple reproducible example is given on the assembly of the IKEA Hyllis shelf as well as the PUBLIC Bikes Sprout bike.

关键词： Automated assembly planning Rule based system knowledge representation and reasoning Answer set programming

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knowledge representation and reasoning - A History of DARPA Leadership

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AI MAGAZINE 2020年第2期41卷 9-21页

作者： Fikes, Richard Garvey, Tom Stanford Univ Comp Sci Stanford CA 94305 USA SRI Int AI Ctr Anaheim CA USA

A fundamental goal of artificial intelligence research and development is the creation of machines that demonstrate what humans consider to be intelligent behavior. Effective knowledge representation and reasoning methods are a foundational requirement for intelligent machines. The development of these methods remains a rich and active area of artificial intelligence research in which advances have been motivated by many factors, including interest in new challenge problems, interest in more complex domains, shortcomings of current methods, improved computational support, increases in requirements to interact effectively with humans, and ongoing funding from the Defense Advanced Research Projects Agency and other agencies. This article highlights several decades of advances in knowledge representation and reasoning methods, paying particular attention to research on planning and on the impact of the Defense Advanced Research Projects Agency's support.

关键词： Planning Artificial intelligence DARPA knowledge knowledge representation and reasoning Reason

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Q-Rung Orthopair Fuzzy Petri Nets for knowledge representation and reasoning

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IEEE ACCESS 2023年 11卷 93560-93573页

作者： Bai, Kaiyuan Jia, Dan Meng, Weiye He, Xingmin China Telecom Res Inst AI Res Ctr Beijing 102209 Peoples R China

This paper investigates a novel fuzzy Petri nets (FPNs) method based on q-rung orthopair fuzzy sets (q-ROFSs) to provide an efficient solution to uncertain knowledge representation and reasoning. It not only improves FPN's flexibility in knowledge parameter representation and reasoning algorithms but also addresses the challenges that most FPNs cannot implement backward reasoning, which is a common reasoning task to infer condition statuses according to consequences reversely. Specifically, we first propose the q-rung orthopair FPNs (q-ROFPNs) by integrating q-ROFSs with FPNs. It achieves an intuitive evaluation of hesitancy information and a flexible adjustment of the knowledge representation ranges. And a reasoning algorithm based on the ordered weighted averaging-weighted average (OWAWA) operator is developed to accomplish the forward reasoning driven by q-ROFPNs, which can balance the proposition weights and its position weights flexibly. Building upon q-ROFPNs, we further propose the q-rung orthopair fuzzy reversed Petri nets (q-ROFRPNs) for backward reasoning task, where a decomposition algorithm for q-ROFRPNs is designed for reducing the inference complexity, and an ordered weighted backward reasoning (OWBR) algorithm is provided to backward reasoning suitable for different fuzzy environments. In addition, to ensure the accuracy and rationality of reasoning results, we propose a knowledge acquisition method by power average (PA) operator to eliminate the negative impact of outliers on knowledge parameter assessments. A simulation experiment on the fault diagnosis of the air conditioning system demonstrates that the proposed method can achieve a more flexible and reliable knowledge representation and reasoning than the state-of-the-art FPNs methods.

关键词： Cognition knowledge representation Fuzzy sets Petri nets Task analysis Linguistics Uncertainty Expert systems Expert system fuzzy petri net knowledge representation and reasoning q-rung orthopair fuzzy sets

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PrimeNet: A Framework for Commonsense knowledge representation and reasoning Based on Conceptual Primitives

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COGNITIVE COMPUTATION 2024年第6期16卷 3429-3456页

作者： Liu, Qian Han, Sooji Cambria, Erik Li, Yang Kwok, Kenneth Univ Auckland Sch Comp Sci Auckland New Zealand Intapp Berlin Germany Nanyang Technol Univ Coll Comp & Data Sci Singapore Singapore Northwestern Polytech Univ Sch Automat Xian Peoples R China ASTAR Inst High Performance Comp Singapore Singapore

Commonsense knowledge acquisition and representation is a core topic in artificial intelligence (AI), which is crucial for building more sophisticated and human-like AI systems. However, existing commonsense knowledge bases organize facts in an isolated manner like bag of facts, lacking the cognitive-level connections that humans commonly possess. People have the ability to efficiently organize vast amounts of knowledge by linking or generalizing concepts using a limited set of conceptual primitives that serve as the fundamental building blocks of reasoning. These conceptual primitives are basic, foundational elements of thought that humans use to make sense of the world. By combining and recombining these primitives, people can construct complex ideas, solve problems, and understand new concepts. To emulate this cognitive mechanism, we design a new commonsense knowledge base, termed PrimeNet, organized in a three-layer structure: a small core of conceptual primitives (e.g., FOOD), a bigger set of concepts that connect to such primitives (e.g., fruit), and an even larger layer of entities connecting to the concepts (e.g., banana). First, we collect commonsense knowledge and employ a gradual expansion strategy for knowledge integration. After refinement, PrimeNet contains 6 million edges between 2 million nodes, with 34 different types of relations. Then, we design a new conceptualization method by leveraging a probabilistic taxonomy, to build the concept layer of PrimeNet. Finally, we conduct primitive detection to build the primitive layer, where a lexical substitution task is used to identify related concepts, and large language models are employed to generate a rational primitive to label each concept cluster as well as verify the primitive detection process.

关键词： Commonsense acquisition knowledge representation and reasoning Conceptual primitives

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Experiential knowledge representation and reasoning based on linguistic Petri nets with application to aluminum electrolysis cell condition identification

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INFORMATION SCIENCES 2020年 529卷 141-165页

作者： Yue, Weichao Gui, Weihua Xie, Yongfang Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou 450002 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China

Fuzzy Petri nets (FPNs) play an important role in knowledge representation and reasoning (KRR), and they have been widely used in many fields. Linguistic terms are usually used to express the experiential knowledge of decision-makers in the above fields. However, cognitive nonconformity, fuzziness and uncertainty of experiential knowledge are widespread in industrial production processes, making it difficult for current FPNs to precisely model the experience or cognition of experts. In an effort to overcome the shortcomings of FPNs, linguistic Petri nets (LPNs) are proposed based on interval type-2 fuzzy sets theory and FPNs in this paper. The extended TOPSIS (ETOPSIS) is proposed to fuse together the cognition of multiple decision-makers. An interval type-2 fuzzy ordered weighted averaging operator is proposed to improve the knowledge reasoning capability of LPNs. Two comparisons are presented to demonstrate the validity of the proposed ETOPSIS and LPNs. In addition, the KRR model for aluminum electrolysis cell condition identification (AECCI) is proposed and AECCI results show the proposed methods are efficient to embrace cognitive nonconformity and manage fuzziness and uncertainty of experiential knowledge. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Interval type-2 fuzzy sets Cognitive inconsistency knowledge representation and reasoning Linguistic Petri nets Extended TOPSIS Aluminum electrolysis cell condition identification

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Dynamic uncertain causality graph based on cloud model theory for knowledge representation and reasoning

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2020年第8期11卷 1781-1799页

作者： Li, Li Xie, Yongfang Chen, Xiaofang Yue, Weichao Zeng, Zhaohui Cent South Univ Sch Automat Changsha 410083 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou 450002 Peoples R China

The dynamic uncertain causality graph (DUCG), which has been widely applied in many fields, is an important modelling technique for knowledge representation and reasoning. However, the extant DUCG models have been criticized because they cannot precisely represent experts' knowledge owing to the ignorance of the fuzziness and randomness of uncertain knowledge. In response, we propose a new type of DUCG model called the cloud reasoning dynamic uncertain causality graph (CDUCG). The CDUCG model, which is based on cloud model theory, can handle with the fuzziness and randomness of uncertain information simultaneously. Moreover, an inference algorithm based on the combination of CDUCG and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is proposed to implement fuzzy knowledge inference effectively and thus make the expert systems more dependable and intelligent. Finally, illustrative examples and an industrial application concerning root cause analysis of aluminum electrolysis are provided to demonstrate the proposed CDUCG model. And experimental results show that the new CDUCG model is flexible and reliable for knowledge representation and reasoning.

关键词： DUCG knowledge representation and reasoning Cloud model Uncertain information TOPSIS

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A Brief Survey on Forgetting from a knowledge representation and reasoning Perspective

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KUNSTLICHE INTELLIGENZ 2019年第1期33卷 9-33页

作者： Eiter, Thomas Kern-Isberner, Gabriele TU Wien Inst Logic & Computat Vienna Austria TU Dortmund Dept Comp Sci Dortmund Germany

Forgetting is an ambivalent concept of (human) intelligence. By definition, it is negatively related to knowledge in that knowledge is lost, be it deliberately or not, and therefore, forgetting has not received as much attention in the field of knowledge representation and reasoning (KRR) as other processes with a more positive orientation, like query answering, inference, or update. However, from a cognitive view, forgetting also has an ordering function in the human mind, suppressing information that is deemed irrelevant and improving cognitive capabilities to focus and deal only with relevant aspects of the problem under consideration. In this regard, forgetting is a crucial part of reasoning. This paper collects and surveys approaches to forgetting in the field of knowledge representation and reasoning, highlighting their roles in diverse tasks of knowledge processing, and elaborating on common techniques. We recall forgetting operations for propositional and predicate logic, as well as for answer set programming (as an important representative of nonmonotonic logics) and modal logics. We discuss forgetting in the context of (ir)relevance and (in)dependence, and explicit the role of forgetting for specific tasks of knowledge representation, showing its positive impact on solving KRR problems.

关键词： Forgetting knowledge representation and reasoning Logic Answer set programming Nonmononotonic reasoning Modal logics Interpolation Relevance Independence

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Can Current Methods in knowledge representation and reasoning Make Robots Culturally Robust? 4

Can Current Methods in Knowledge Representation and Reasonin...

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4th Biennial Robophilosophy Conference - Culturally Sustainable Social Robotics

作者： Mansouri, Masoumeh Univ Birmingham Sch Comp Sci Birmingham England

ISBN: (纸本)9781643681559;9781643681542

In the attempt to make robots culturally diverse, social robotics research is overwhelmed by cultural stereotypes. Many researchers introduce concepts such as Culturally Robust Robots to account for the dynamic and flexible nature of culture. These concepts are grounded on an implicit assumption: that current AI methods are epistemologically adequate to represent and reason about "culture". This paper questions that assumption by looking at two knowledge representation and reasoning (KR&R) methods used in intelligent robotics;argue for the inadequacy of current methods;and call for a critical revision of the use of KR&R in social robotics.

关键词： Culturally-aware robotics knowledge representation and reasoning social robotics

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representation and reasoning of context-dependant knowledge in distributed fuzzy ontologies

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第8期37卷 6052-6060页

作者： Jiang, Yuncheng Tang, Yong Wang, Ju Tang, Suqin S China Normal Univ Sch Comp Sci Guangzhou 510631 Guangdong Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing 100190 Peoples R China Guangxi Normal Univ Sch Comp Sci & Informat Technol Guilin 541004 Peoples R China

Most of current knowledge based systems manage impressive amounts of information (especially distributed fuzzy information). In addition to widely pointed-out integration and maintenance difficulties, other common problem is overwhelming of users with much more information than the strictly necessary for fulfilling a task. This issue has been pointed out with the name of "information overload". Use of context knowledge has been envisioned as an appropriate solution to deal with this information overload matter. In this paper, we present a distributed fuzzy context-domain relevance (DFCDR) model for representation in fuzzy ontologies relevance relations between fuzzy context ontology and distributed fuzzy domain ontologies. In fact, the DFCDR model is a distributed fuzzy extension of the context-domain relevance (CDR) model. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： knowledge representation and reasoning Context-aware systems Fuzzy ontologies Distributed ontologies Description logics

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Geometry fusion representation for knowledge graph completion using multi-view information bottleneck

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2025年第3期28卷 1-25页

作者： Chen, Kai Yu, Han Song, Xin Wang, Ye Shan, Yongxue Li, Aiping Huang, Yinxuan Liu, Ziniu Natl Univ Def Technol 109 Deya Rd Changsha 410003 Hunan Peoples R China

The inherent incompleteness of knowledge Graphs (KGs) has spurred significant research efforts in the domain of knowledge graph completion (KGC), which is grounded in the premise of leveraging existing knowledge to infer the unknown and fill in the gaps. A multitude of studies have focused on representation learning across a spectrum of geometric spaces, namely Euclidean, hyperbolic, and spherical. Each of these spaces excels in modeling distinct structural and characteristic elements within KGs, thereby enhancing the ability to uncover and reason about missing knowledge. Recognizing the distinct modeling advantages of each space, there is a growing effort to integrate these disparate geometries. Despite these efforts, current fusion methods, which rely on simplistic weighted summation, fail to adequately focus on the objectives of reasoning and tend to retain a significant amount of redundant information. To address this, we propose the Multi-View Information Bottleneck based Geometry Fusion (MVIBGF) method for KGC. We utilize a multi-view learning approach, aligning each view with a unique geometric space, and apply the Information Bottleneck principle to enhance mutual information with the inference goal while minimizing it within individual views. This process ensures that only the most salient and reasoning-relevant information is retained, thus enhancing the precision and efficiency of the knowledge integration. Experiments show that MVIBGF outperforms existing KGC methods, proving its robustness and effectiveness in KG modeling.

关键词： knowledge graph completion knowledge representation and reasoning Geometry fusion Multi-view information bottleneck

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