Collaboration of Knowledge Bases via Knowledge Based Coordination

CKBS ’90 ◽  
1991 ◽  
pp. 113-129 ◽  
Author(s):  
Donald D. Steiner ◽  
Dirk E. Mahling ◽  
Hans Haugeneder
Keyword(s):  
Knowledge Bases ◽  
Knowledge Based

Cyberphysical Design Automation Framework for Knowledge-based Engineering

2013 ◽  
Vol 1 (1) ◽  
pp. 158-178
Author(s):  
Urcun John Tanik
Keyword(s):  
System Design ◽  
Design Process ◽  
Design Automation ◽  
Design Theory ◽  
Knowledge Bases ◽  
Knowledge Based ◽  
Knowledge Based Engineering ◽  
Cyberphysical System ◽  
Automation Framework

Cyberphysical system design automation utilizing knowledge based engineering techniques with globally networked knowledge bases can tremendously improve the design process for emerging systems. Our goal is to develop a comprehensive architectural framework to improve the design process for cyberphysical systems (CPS) and implement a case study with Axiomatic Design Solutions Inc. to develop next generation toolsets utilizing knowledge-based engineering (KBE) systems adapted to multiple domains in the field of CPS design automation. The Cyberphysical System Design Automation Framework (CPSDAF) will be based on advances in CPS design theory based on current research and knowledge collected from global sources automatically via Semantic Web Services. A case study utilizing STEM students is discussed.

scholarly journals Supporting Creative Mechanical Design

1994 ◽  
Author(s):  
Kun Sun ◽  
Boi Faltings
Keyword(s):  
Mechanical Design ◽  
Knowledge Bases ◽  
Creative Design ◽  
Knowledge Based Systems ◽  
Cad Systems ◽  
Knowledge Based ◽  
Logical Language ◽  
Promising Application ◽  
Qualitative Physics ◽  
And Function

Abstract Knowledge-based CAD systems limit designers’ creativity by constraining them to work with the prototypes provided by the systems’ knowledge bases. We investigate knowledge-based CAD systems capable of supporting creative designs in the example domain of elementary mechanisms. We present a technique based on qualitative explanations which allows a designer to extend the knowledge base by demonstrating a structure which implements a function in a creative way. Structure is defined as the geometry of the parts, and function using a general logical language based on qualitative physics. We argue that the technique can accommodate any creative design in the example domain, and we demonstrate the technique using an example of a creative design. The use of qualitative physics as a tool for extensible knowledge-based systems points out a new and promising application area for qualitative physics.

Legal Knowledge Systems

2011 ◽  
pp. 1161-1166 ◽  
Author(s):  
T. F. Gordon
Keyword(s):  
Life Cycle ◽  
Knowledge Bases ◽  
Knowledge Systems ◽  
Information And Communications Technology ◽  
Legal Knowledge ◽  
Levels Of Abstraction ◽  
Research Issues ◽  
Implementation Phase ◽  
Knowledge Based ◽  
Representation Techniques

There are many conceptions of e-governance (Malkia, Anttiroiko, & Savolainen, 2004; Reinermann & Lucke, 2002). Our view is that e-governance is about the use of information and communications technology to improve the quality and efficiency of all phases of the life cycle of legislation. In this conception, computer models of legislation play a central role. We use the term “model” in a broad way, to cover every kind of data model of legislation or metadata about legislation, at various levels of abstraction or detail, including full text, hypertext, diagrams and other visualization methods, and legal knowledge-bases using Artificial Intelligence knowledge representation techniques. The appropriate kind of model depends on the particular task to be supported. In this article, the focus will be on the use of legal knowledge systems (LKS) to support the implementation phase of the life cycle of legislation. Legal Knowledge Systems are also known as legal knowledge-based systems (LKBS). LKS can greatly improve the correctness, consistency, transparency and, last but not least, the efficiency of the administration of complex legislation. The rest of this article is organized as follows. The next section explains the relevance of legal knowledge systems for governance. This is followed by a section motivating the use of LKS to support tasks in the implementation phase of the life cycle of legislation and providing a brief introduction to LKS technology. Next, various application scenarios for implementing public policy and legislation using LKS are discussed. Although research on technology for legal knowledge systems continues, it is a mature technology with many impressive applications in regular use by public administration. The article concludes by reiterating its main points and identifying open research issues.

Chapter 5. Answering Natural-Language Questions with Neuro-Symbolic Knowledge Bases

2021 ◽  
Author(s):  
Haitian Sun ◽  
Pat Verga ◽  
William W. Cohen
Keyword(s):  
Neural Networks ◽  
Query Language ◽  
Feedforward Neural Networks ◽  
Knowledge Bases ◽  
Language Models ◽  
Computationally Efficient ◽  
Knowledge Based ◽  
Reasoning System ◽  
Symbolic Reasoning ◽  
Computational Intractability

Symbolic reasoning systems based on first-order logics are computationally powerful, and feedforward neural networks are computationally efficient, so unless P=NP, neural networks cannot, in general, emulate symbolic logics. Hence bridging the gap between neural and symbolic methods requires achieving a delicate balance: one needs to incorporate just enough of symbolic reasoning to be useful for a task, but not so much as to cause computational intractability. In this chapter we first present results that make this claim precise, and then use these formal results to inform the choice of a neuro-symbolic knowledge-based reasoning system, based on a set-based dataflow query language. We then present experimental results with a number of variants of this neuro-symbolic reasoner, and also show that this neuro-symbolic reasoner can be closely integrated into modern neural language models.

A Knowledge-Based Approach to Modelling of Robotic Assembly Cells

Robotica ◽  
1991 ◽  
Vol 9 (1) ◽  
pp. 31-42 ◽  
Author(s):  
Dae-Won Kim ◽  
Bum-Hee Lee ◽  
Myoung-Sam Ko
Keyword(s):  
Knowledge Base ◽  
State Transition ◽  
Knowledge Bases ◽  
The State ◽  
System Structure ◽  
Robotic Assembly ◽  
Knowledge Based ◽  
Job Information ◽  
Assembly Cell ◽  
Transition Map

SUMMARYIn this paper, an approach to modelling of a robotic assembly cell is proposed and a method for managing the cell operation is described using a knowledge base. Since the modelling structure is based on the concept of the state variable, the relationships between states are described by the state transition map (STM). The knowledge-bases for state transition and assembly job information are obtained from the STM and the assembly job tree (AJT), respectively. Using the knowledge-base, the System structure is discussed in relation to both managing the cell operation and evaluating the performances. Finally, a simulation algorithm is presented with the simulation results to show the significance of the proposed modelling approach.

Altering the SightPlan knowledge-based systems

1992 ◽  
Vol 6 (1) ◽  
pp. 19-37 ◽  
Author(s):  
I. D. Tommelein ◽  
B. Hayes-Roth ◽  
R. E. Levitt
Keyword(s):  
Problem Solving ◽  
Knowledge Bases ◽  
Knowledge Based Systems ◽  
Knowledge Domain ◽  
Task Knowledge ◽  
Knowledge Based ◽  
Site Layout ◽  
Different Types ◽  
Control Knowledge ◽  
Construction Site Layout

SightPlan refers to several knowledge-based systems that address construction site layout. Five different versions were implemented and their components of expertise are described here. These systems are alterations of one another, differing either in the problems they solve, the problem-solving methods they apply, or the tasks they address. Because they share either control knowledge, domain concepts, or heuristics, and such knowledge is implemented in well-defined modular knowledge bases, these systems could easily re-use parts of one another. Experiments like those presented here may clarify the role played by different types of knowledge during problem solving, enabling researchers to gain a broader understanding of the generality of the domain and task knowledge that is embedded in KBSs and of the power of their systems.

The Cost of Knowledge Recovery: A Challenge for the Application of Concept Mapping

2007 ◽  
Vol 51 (4) ◽  
pp. 328-331
Author(s):  
Robert R. Hoffman ◽  
Paul J. Feltovich ◽  
David W. Eccles
Keyword(s):  
Concept Maps ◽  
Expert Knowledge ◽  
Knowledge Bases ◽  
Knowledge Elicitation ◽  
Terrain Analysis ◽  
Aerial Photos ◽  
Knowledge Based ◽  
Knowledge Models ◽  
Rock Types ◽  
The Cost

Whereas knowledge management relies on processes of knowledge elicitation, there is also a process in which knowledge is “recovered,” typically from archived documents. We conducted a knowledge recovery (KR) effort, going from documents to a structured set of propositions concerning expert knowledge about terrain analysis, discussing landforms, soils, rock types, etc. Assertions and feature associations were recast as over 3,000 propositions. When contrasted with results from previous evaluations of methods of knowledge elicitation, KR was costly in terms of time and effort, suggesting that knowledge-based organizations should make knowledge capture an on-going aspect of work, rather than finding themselves in the “catch-up mode” to recover lost expertise. For both knowledge elicitation and recovery, the knowledge has to be represented in a form that is usable and useful (e.g., instantiation in knowledge bases). We created from the propositions a navigable knowledge model based on over 150 Concept Maps, which were hyperlinked together and to dozens of resources (aerial photos, maps, diagrams, etc.). Such knowledge models are intended to make the “expertise of the past” more useful and usable in training and in performance support.

scholarly journals A Knowledge-Based Method for Innovative Design for Additive Manufacturing Supported by Modular Ontologies

2018 ◽  
Vol 18 (2) ◽  
Author(s):  
Thomas J. Hagedorn ◽  
Sundar Krishnamurty ◽  
Ian R. Grosse
Keyword(s):  
Additive Manufacturing ◽  
Business Case ◽  
Knowledge Bases ◽  
Product Development Process ◽  
Design Teams ◽  
Design Problems ◽  
Innovative Design ◽  
Knowledge Based ◽  
Formal Ontologies ◽  
Innovative Capabilities

Additive manufacturing (AM) offers significant opportunities for product innovation in many fields provided that designers are able to recognize the potential values of AM in a given product development process. However, this may be challenging for design teams without substantial experience with the technology. Design inspiration based on past successful applications of AM may facilitate application of AM even in relatively inexperienced teams. While designs for additive manufacturing (DFAM) methods have experimented with reuse of past knowledge, they may not be sufficient to fully realize AM's innovative potential. In many instances, relevant knowledge may be hard to find, lack context, or simply unavailable. This design information is also typically divorced from the underlying logic of a products' business case. In this paper, we present a knowledge based method for AM design ideation as well as the development of a suite of modular, highly formal ontologies to capture information about innovative uses of AM. This underlying information model, the innovative capabilities of additive manufacturing (ICAM) ontology, aims to facilitate innovative use of AM by connecting a repository of a business and technical knowledge relating to past AM products with a collection of knowledge bases detailing the capabilities of various AM processes and machines. Two case studies are used to explore how this linked knowledge can be queried in the context of a new design problem to identify highly relevant examples of existing products that leveraged AM capabilities to solve similar design problems.

AN ACTIVE DATABASE APPROACH TO CONSISTENCY MANAGEMENT IN DATA- AND KNOWLEDGE-BASED SYSTEMS

1993 ◽  
Vol 02 (02) ◽  
pp. 187-200 ◽  
Author(s):  
LEONARD J. SELIGMAN ◽  
LARRY KERSCHBERG
Keyword(s):  
Knowledge Base ◽  
Knowledge Bases ◽  
Simple Extension ◽  
Active Databases ◽  
New Approach ◽  
Consistency Management ◽  
Knowledge Based ◽  
Consistency Constraints ◽  
Consistency Problem ◽  
Representation Language

Many AI and other applications populate their knowledge-bases with information retrieved from large, shared databases. This paper describes a new approach to maintaining consistency between objects in dynamic, shared databases and copies of those objects which are cached in an application knowledge-base. The approach relies on an intelligent interface to active databases that we call a Mediator for Approximate Consistency (MAC). The MAC has several unique features: (1) it permits applications to specify their consistency requirements declaratively, using a simple extension of a frame-based representation language, (2) it automatically generates the interfaces and database objects necessary to enforce those consistency requirements, shielding the knowledge-base developer from the implementation details of consistency maintenance, and (3) it provides an explicit representation of consistency constraints in the database, which allows them to be queried and reasoned about. The paper describes the knowledge-base/database consistency problem and previous approaches to dealing with it. It then describes our architecture for maintaining approximate knowledge-base/database consistency, including techniques for specifying, representing, and enforcing consistency constraints.

Integration of knowledge-based and generative systems for building characterization and prediction

2010 ◽  
Vol 24 (1) ◽  
pp. 3-16 ◽  
Author(s):  
Ajla Aksamija ◽  
Kui Yue ◽  
Hyunjoo Kim ◽  
Francois Grobler ◽  
Ramesh Krishnamurti
Keyword(s):  
Architectural Design ◽  
Contextual Information ◽  
Three Dimensional ◽  
Knowledge Bases ◽  
Shape Grammar ◽  
Interactive System ◽  
Existing Buildings ◽  
Knowledge Based ◽  
Building Models ◽  
Three Dimensional Models

AbstractThis paper discusses the integration of knowledge bases and shape grammars for the generation of building models, covering interaction, system, and implementation. Knowledge-based and generative systems are combined to construct a method for characterizing existing buildings, in particular, their interior layouts based on exterior features and certain other parameters such as location and real dimensions. The knowledge-based model contains information about spatial use, organization, elements, and contextual information, with the shape grammar principally containing style rules. Buildings are analyzed and layouts are generated through communication and interaction between these two systems. The benefit of using an interactive system is that the complementary properties of the two schemes are employed to strengthen the overall process. Ontologies capture knowledge relating to architectural design principles, building anatomy, structure, and systems. Shape grammar rules embody change through geometric manipulation and transformation. Existing buildings are analyzed using this approach, and three-dimensional models are automatically generated. Two particular building types, the vernacular rowhouse and high-rise apartment building, both from Baltimore, Maryland, are presented to illustrate the process and for comparing the utilized methodologies.

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