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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:
Principle of Virtual Work: Problem Solving01:13

Principle of Virtual Work: Problem Solving

The principle of virtual work is an essential concept in the field of mechanics and engineering. This is used to solve problems related to the equilibrium of a structure or system. It is based on the assumption that if a system is in equilibrium, the work done by all the forces during a virtual displacement is zero. This principle is applied by considering virtual displacements of the system and the corresponding work done by internal and external forces.
To apply the principle of virtual work,...
Machines: Problem Solving I01:22

Machines: Problem Solving I

A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
Machines: Problem Solving II01:30

Machines: Problem Solving II

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
Application of Integration: Problem Solving01:30

Application of Integration: Problem Solving

The process of breathing involves the periodic intake and expulsion of air, known as the respiratory cycle, which typically lasts about five seconds. Modeling the volume of air inhaled into the lungs as a function of time provides insight into both the dynamics and efficiency of pulmonary ventilation. This volume is determined by integrating the airflow rate over time, which captures the cumulative effect of air entering the lungs.Sinusoidal Model of AirflowAirflow during respiration is not...
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...

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Related Experiment Video

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Automated Robotic Liquid Handling Assembly of Modular DNA Devices
11:22

Automated Robotic Liquid Handling Assembly of Modular DNA Devices

Published on: December 1, 2017

Software-engineering challenges of building and deploying reusable problem solvers.

Martin J O'Connor1, Csongor Nyulas, Samson Tu

  • 1Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.

Artificial Intelligence for Engineering Design, Analysis and Manufacturing : AI EDAM
|April 9, 2013
PubMed
Summary
This summary is machine-generated.

Problem Solving Methods (PSMs) offer reusable algorithms for intelligent systems, but software engineering challenges hinder adoption. Advances in ontologies now present new opportunities for leveraging PSM principles in knowledge-based systems.

Keywords:
Knowledge-Based SystemsProblem Solving MethodsReusable Problem SolversSoftware-Engineering ChallengesTask–Method Decomposition Process

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Area of Science:

  • Artificial Intelligence
  • Software Engineering
  • Knowledge Representation

Background:

  • Problem Solving Methods (PSMs) are reusable software components for algorithms.
  • Integrating PSMs with domain knowledge is key for intelligent systems.
  • Despite conceptual appeal, PSM adoption faces practical challenges.

Purpose of the Study:

  • Analyze reasons for limited PSM adoption.
  • Evaluate PSM principles in complex software development.
  • Identify opportunities for leveraging PSMs with modern ontologies.

Main Methods:

  • Analysis of PSM adoption barriers.
  • Case study of a complex, high-throughput PSM-based system.
  • Examination of control knowledge and domain knowledge integration.

Main Results:

  • PSM principles, especially task-method decomposition, are valuable for knowledge-based systems.
  • Software engineering challenges, particularly knowledge integration, are underestimated.
  • Ontology advancements offer new avenues for PSM application.

Conclusions:

  • PSMs provide powerful abstractions for analytic systems.
  • Overcoming software engineering complexities is crucial for PSM success.
  • Emerging ontology tools can facilitate wider PSM adoption.