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Related Concept Videos

Decision Making01:20

Decision Making

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...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Decision Making: P-value Method01:09

Decision Making: P-value Method

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 have a...
Neuroplasticity01:01

Neuroplasticity

Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
Growth Models with Integration: Problem Solving01:27

Growth Models with Integration: Problem Solving

In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...
Reason and Intuition01:37

Reason and Intuition

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 brain can only use...

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Related Experiment Videos

Evolving neural networks for strategic decision-making problems.

Nate Kohl1, Risto Miikkulainen

  • 1Department of Computer Sciences, The University of Texas at Austin, 1 University Station C0500, Austin, TX, United States. nate@cs.utexas.edu

Neural Networks : the Official Journal of the International Neural Network Society
|April 14, 2009
PubMed
Summary
This summary is machine-generated.

Neuroevolution, or evolving neural networks, struggles with strategic tasks due to "fracture." New methods addressing this fracture significantly improve performance on complex decision-making problems.

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Neuroevolution (NE) excels at low-level control but faces challenges with strategic decision-making.
  • Strategic tasks often exhibit 'fracture,' where optimal actions change abruptly between states.
  • This discontinuity hinders standard NE approaches.

Purpose of the Study:

  • To evaluate the hypothesis that fractured problem landscapes impede NE.
  • To introduce a method for quantifying fracture using function variation.
  • To investigate NE enhancements for tackling fractured environments.

Main Methods:

  • Proposed a metric for measuring function variation to quantify problem fracture.
  • Investigated two methods to mitigate fracture: local receptive fields and cascaded network architectures.
  • Conducted experiments across benchmark domains with varying fracture levels.

Main Results:

  • Demonstrated a significant correlation between problem fracture and NE performance.
  • Showcased that local receptive fields and cascaded architectures substantially improve NE performance in fractured domains.
  • Validated the effectiveness of the proposed fracture measurement.

Conclusions:

  • Fracture is a key challenge limiting NE in strategic domains.
  • The proposed methods (local receptive fields, cascaded networks) offer effective solutions to overcome fracture.
  • These advancements pave the way for applying NE to more complex, strategic artificial intelligence tasks.