3 edition of Applying knowledge compilation techniques to model-based reasoning found in the catalog.
Applying knowledge compilation techniques to model-based reasoning
by NASA Ames Research Center, Artificial Intelligence Research Branch, National Technical Information Service, distributor in Moffett Field, CA, [Springfield, Va.?
Written in English
|Statement||Richard M. Keller.|
|Series||NASA-TM -- 107876., NASA technical memorandum -- 107876.|
|Contributions||Ames Research Center. Artificial Intelligence Research Branch.|
|The Physical Object|
The more points of data, the more accurate and detailed model-based reasoning can be. This can help modelers avoid potentially costly errors, like failing to anticipate an issue that would have been apparent with more data. As observations come in, they can be added to the body of knowledge, which may result in shifts to the model. Artificial Intelligence: Case-based & Model-based Reasoning 1. Case-based Reasoning (CBR) • Collection of a set of cases – Store a case in the Case Base • CBR is based on human information processing (HIP) model in some problem areas – Thinking about how human processes information – Try to remember previous case/recall similar cases & modify to fit a new situation • .
Case-Based Reasoning as a Technique for Knowledge Management in Business Process Redesign Selma Limam Mansar and Farhi Marir tool for knowledge management in Business Process redesign. Finally, in section 7, conclusions and future than with rule- or model-based approaches. Case-based reasoning is also used extensively in day-to-day common-sense reasoning. The meal planning example above is typical of the reasoning we all situation, the addition of inferred knowledge about the new situation, or a result from applying adaptation strategies in novel ways.
Model-Based Reasoning About Cases Juho IRousu and Robert J. Aarts VTT Biotechnology and Food Research P.O. Box , FIN VT]’, Finland @ Abstract Our bioprocess recipe planner Sophist (Aarts & Rousu, ) uses a qualitative model to represent a large amount of domain knowledge. This knowledge is used to analyse. Created Date: 2/11/ PM.
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Motivation for applying knowledge compilation to Model-Based-- Reasoning As previously mentioned, knowledge compilation techniques are general, and can be applied to any type of software system.
Recently, there has been interest in applying these techniques to systems developed in several AI subareas, including MBR, planning, and problem File Size: KB. Get this from a library. Applying knowledge compilation techniques to model-based reasoning.
[Richard M Keller; Ames Research Center. Artificial Intelligence Research Branch.]. In artificial intelligence, model-based reasoning refers to an inference method used in expert systems based on a model of the physical world.
With this approach, the main focus of application development is developing the model. Then at run time, Applying knowledge compilation techniques to model-based reasoning book "engine" combines this model knowledge with observed data to derive conclusions such as a diagnosis or a prediction.
There are several key ingredients common to the various forms of model-based reasoning considered in this book.
The term ‘model’ comprises both internal and external representations. The models are intended as interpretations of target physical systems, processes, phenomena, or situations and are retrieved or constructed on the basis of potentially satisfying salient constraints of the.
Applying Model-Based Reasoning to the FDIR of the Command and Data Handling Subsystem of the International Space Station. in the spacecraft systems and conventional techniques such as limit. One approach to this reformed issue is to use method-specific compilation of data into knowledge.
We describe an experiment in which a legacy knowledge system called Interactive Kritik is integrated with an ORACLE database.
The experiment indicates the computational feasibility of method-specific data-to-knowledge : J. William Murdock, Ashok K. Goel, Michael J. Donahoo, Shamkant Navathe. The application of knowledge compilation techniques to a specialized class of knowledge-based systems known as model-based reasoning (MBR) systems is discussed.
Model-based Reasoning Recent: business rules Model-based systems: reasoning with understandable model, i.e., they have intuitive semantics – p. 1/ Heuristic rules and their disadvantages Problem solving based on heuristic rules: category knowledge knowledge base problem solving method.
Applying knowledge compilation techniques to model-based reasoning [microform] / Richard M. Keller; Artificial intelligence support for scientific model-building [microform] / Richard M.
Keller; Man-made minds: the promise of artificial intelligence / M. Mitchell Waldrop. Model Based Reasoning - Lecture Outline • Basics of the task • Different amounts and types of knowledge can be brought to bear at each phase Model Based Troubleshooting Constraint Suspension 3 5 5 5 20 3 Produces Output 35 Times Times Times Plus Plus 15 15 25 40 40 35 40 Consistent Diagnosis: Broken takes inputs 25 and 15 File Size: KB.
Model-based reasoning is "the ability for students to construct scientific models in order to explain observed phenomena". Many educators see the word "model" and think of some type of physical model (for example a model of a cell), however, this is a common misconception.
Education: Model Based Reasoning techniques are used for realising tutoring and training functions, and subject matter construction. Other Areas: Model Based Systems have also been seen in other application area, including, medicine, industrial processes/plants, automotive, aeronautics, aerospace, physics, telecommunications Size: 90KB.
In addition, modern knowledge systems typically characterize the kinds of knowledge needed by specific problem-solving methods quite precisely. This leads us to hypothesize method-specific data-to-knowledge compilation as a potential mechanism for integrating heterogeneous knowledge systems and legacy databases for : J.
William Murdock, Ashok K. Goel, Michael J. Donahoo, Shamkant B. Navathe. It will describe the fundamental techniques of CBR and contrast its approach to that of model-based reasoning systems. A critical review of currently available CBR software tools is followed by descriptions of CBR applications both from academic research and, in more detail, three CBR systems that are presently being used commercially.
: Model-Based Reasoning in Science and Technology: Logical, Epistemological, and Cognitive Issues (Studies in Applied Philosophy, Epistemology and Rational Ethics) (): Lorenzo Magnani, Claudia Casadio: Books.
Applying knowledge compilation techniques to model-based reasoning [microform] / Richard M. Keller Artificial intelligence support for scientific model-building [microform] / Richard M. Keller Localization of Humans in Images Using Convolutional Networks / Tompson, Jonathan James Richard. We have designed this website to serve three functions.
First, it is a repository of the MBER-Bio curriculum that our team and other members have designed as well as a number of other resources to support you in teaching (see MBER Essentials).Second, it is a community space meant to enable interaction on forums among teachers using MBER-Bio.
And finally, it is a personal design space. Characterization of model-based reasoning strategies for use in IVHM paper summarizes the characterization of the modeling process for each of the techniques. Keywords: Model-based reasoning, diagnostic reasoning, fault of the most difficult and time-consuming aspects of putting together an IVHM system is knowledge acquisition.
Introduction. Since the advent of the mental model theory in the s, reasoning with model-based mental representations has been an intuitive paradigm that has been shown to be theoretically sound and to possess some computational advantages over reasoning with logical formula-based by: Suggested Citation: "9 Teaching Science as Practice." National Research Council.
Taking Science to School: Learning and Teaching Science in Grades K Washington, DC: The National Academies Press. doi: / that can support K-8 students in learning science across the strands and point out how this diverges from current practice.
When models of the observed system are used as a basis for fault detection and diagnosis, this is often referred to as "model based reasoning". Defining the models then becomes a significant part of the application development effort. An “engine” combines the model knowledge with observed data to derive conclusions at run time.Principles of Knowledge Representation and Reasoning contains the proceedings of the Fourth International Conference on Principles of Knowledge Representation and Reasoning (KR '94) held in Bonn, Germany, on MayThe conference provided a forum for reviewing the theory and principles underlying knowledge representation and reasoning.
The first section, Models and Methods, covers fundamental aspects of creating and applying knowledge first, methodologies are compared by Daniela Lucas da Silva, Renato Rocha Souza, and Maurício Barcellos Almeida.
The authors conduct an analytical study compiled by the analysis of literature about methodologies for building ontologies and controlled vocabularies as well .