Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. Genre/Form: Ressources InternetActes de congrès Additional Physical Format: Version imprimée : Material Type: Document, Internet resource Document Type: Internet Resource, Computer File All Authors / Contributors: David Riaño; Annette Teije; Silvia Miksch Find more information about: David Riaño Annette Teije Silvia Miksch ISBN: 9783642276972 3642276970 OCLC Number: 807331853 Notes: Titre de l'écran-titre (visionné le 7 mars 2012). Description: 1 online resource Contents: The Human Cli-Knowme Project: Building a Universal, Formal, Procedural and Declarative Clinical Knowledge Base, for the Automation of Therapy and Research -- A Systematic Analysis of Medical Decisions: How to Store Knowledge and Experience in Decision Tables -- Task Network Based Modeling, Dynamic Generation and Adaptive Execution of Patient-Tailored Treatment Plans Based on Smart Process Management Technologies -- Towards the Automated Calculation of Clinical Quality Indicators -- Reasoning with Effects of Clinical Guideline Actions Using OWL: AL Amyloidosis as a Case Study -- Careflow Personalization Services: Concepts and Tool for the Evaluation of Computer-Interpretable Guidelines -- Diaflux: A Graphical Language for Computer-Interpretable Guidelines -- Analysis of Treatment Compliance of Patients with Diabetes -- Computing Problem Oriented Medical Records -- Detecting Dominant Alternative Interventions to Reduce Treatment Costs -- Patterns of Clinical Trial Eligibility Criteria -- Mammographic Knowledge Representation in Description Logic. Series Title: Lecture Notes in Computer Science, 6924. Responsibility: edited by David Riaño, Annette Teije, Silvia Miksch.
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There are various methods for knowledge representation; however, they do not meet the requirements of an ips in stem education.
A semantic network is a graphical knowledge representation technique. This knowledge representation system is primarily on network structure. The semantic networks were basically developed to model human memory. The arcs are defined in a variety of ways, depending upon the kind of knowledge.
Although knowledge representation is one of the central and in some ways most familiar concepts in ai, the most fundamental question about it---what is it?---has rarely been answered directly.
For meeting requirement rq1 of an ips in stem education, a knowledge model has to represent the knowledge base sufficiently.
Knowledge acquisition, representation, and organization knowledge acquisition is the process of absorbing and storing new information in memory, the success of which is often gauged by how well the information can later be remembered (retrieved from memory).
Feb 13, 2020 semantic knowledge representation for strategic interactions in dynamic situations.
Knowledge representation for health care is an important subfield of artificial intelligence in medicine.
What is representation? symbols standing for things in the world john john loves mary first aid women john the proposition that john loves mary knowledge representation: symbolic encoding of propositions believed (by some agent).
Every cognitive enterprise involves some form of knowledge representation. Humans represent information about the external world and internal mental states,.
Knowledge representation is at the very core of a radical idea for understanding intelligence.
Knowledge representation and reasoning (kr, krr) is the part of artificial intelligence which concerned with ai agents thinking and how thinking contributes to intelligent behavior of agents.
Perspectives; paradigms; advanced topics; kr in ai texts; sample curriculum; alternative curriculum.
Knowledge representation and reasoning is the area of artificial intelligence (ai) concerned with how knowledge can be represented symbolically and manipulated in an automated way by reasoning programs.
Knowledge representation and reasoning (kr, krr) represents information from the real world for a computer to understand and then utilize this knowledge to solve complex real-life problems like communicating with human beings in natural language.
] we present a principled approach to semantic en- tailment that builds on inducing re-representations of text snippets into a hierarchical knowledge.
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