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Related Concept Videos

Problem-Solving01:29

Problem-Solving

Effective problem-solving consists of two steps: 1. identifying the problem and 2. selecting the appropriate problem-solving strategy (i.e., a plan of action used to find a solution). Humans use four problem-solving strategies:
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light bulb,...
Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
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Formulating and Validating Nursing Diagnosis I01:26

Formulating and Validating Nursing Diagnosis I

A nursing diagnosis is written when the nurse recognizes a cluster of essential patient data indicating health problems treated with independent nursing interventions. The standardized terminologies of a nursing diagnosis help nurses identify and treat patients' problems. Every electronic health record that uses nursing diagnosis must employ standard diagnostic terminology. Developing an efficient, individualized care plan begins with accurate nursing diagnoses.
There are thirteen domains for...
Heuristics01:21

Heuristics

Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
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Normal and Tangetial Components: Problem Solving01:24

Normal and Tangetial Components: Problem Solving

Consider a man with a mass of 70 kg seated in a chair connected to a pin support through a member BC. If the man maintains an upright position, the task is to determine the horizontal and vertical reactions of the chair on the man when the member makes a 45° angle with the horizontal. At this moment, the man has a speed of 5 m/s, increasing at a rate of 1 m/s².

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Related Experiment Video

Updated: May 29, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

A general expert system design for diagnostic problem solving.

P K Fink1, J C Lusth, J W Duran

  • 1Southwest Research Institute, San Antonio, TX 78284.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces the Integrated Diagnostic Model (IDM), an expert system design that combines experiential and physical knowledge. The IDM aims to improve flexible explanations and graceful degradation in expert systems for better diagnostic and repair capabilities.

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Area of Science:

  • Artificial Intelligence
  • Expert Systems
  • Mechanical Engineering

Background:

  • Current expert systems often lack human-like flexibility in explanations and error handling.
  • Limitations include rigid reasoning processes and abrupt failures outside their core knowledge.
  • These shortcomings hinder the practical application of expert systems in complex domains.

Purpose of the Study:

  • To present a novel expert system design, the Integrated Diagnostic Model (IDM).
  • To address limitations in explanation flexibility and graceful degradation in expert systems.
  • To demonstrate the IDM's efficacy in diagnosis and repair tasks.

Main Methods:

  • Developed the Integrated Diagnostic Model (IDM) architecture.
  • Integrated two distinct knowledge sources: a shallow, experiential knowledge base and a deep, physical knowledge base.
  • Implemented and tested the IDM in the mechanical domain for diagnosis and repair.

Main Results:

  • The IDM design integrates experiential and physical knowledge bases.
  • Demonstrated improved functional explanations and graceful degradation capabilities.
  • Successfully applied the IDM to diagnosis and repair problems in the mechanical domain.

Conclusions:

  • The Integrated Diagnostic Model (IDM) offers a promising approach to enhance expert system performance.
  • Integrating diverse knowledge sources improves flexibility and robustness.
  • The IDM shows significant potential for advanced diagnostic and repair applications.