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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.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
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Decision Making: Traditional Method01:14

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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Classification of Systems-I01:26

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Reason and Intuition01:37

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Related Experiment Video

Updated: Sep 23, 2025

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
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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
|May 16, 2022
PubMed
Summary

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.