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Related Concept Videos

Decision Making01:20

Decision Making

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Automatic Rule Generation for Decision-Making in Context-Aware Systems Using Machine Learning.

Roua Jabla1,2, Maha Khemaja3, Félix Buendia1

  • 1Department of Computer Engineering, Universitat Politècnica Valencia, Camino de Vera S/N, Valencia 46022, Spain.

Computational Intelligence and Neuroscience
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This study enhances context-aware systems for dynamic environments by automatically discovering and integrating new rules at runtime. This approach improves decision-making by adapting to rapid environmental changes, outperforming existing methods.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Software Engineering

Background:

  • Dynamic environments present challenges for context-aware systems due to rapid runtime changes.
  • Predefined context-aware rules often lack efficiency in dynamic settings, hindering effective decision-making.

Purpose of the Study:

  • To improve decision-making in dynamic environments by automatically enriching rule knowledge bases.
  • To develop an approach for discovering and integrating new context-aware rules at runtime.

Main Methods:

  • A hybrid approach combining two machine learning algorithms for rule generation.
  • An extended genetic algorithm (GA) for optimizing discovered rules.
  • Automated rule transformation for knowledge base enrichment.

Main Results:

  • The proposed approach demonstrates enhanced effectiveness in dynamic environments.
  • Experimental results show superior performance compared to existing algorithms and state-of-the-art methods.

Conclusions:

  • The automated enrichment of rule knowledge bases is crucial for effective decision-making in dynamic environments.
  • The presented hybrid machine learning and genetic algorithm approach offers a robust solution for adaptive context-aware systems.