Jove
Visualize
Contact Us

Related Concept Videos

Decision Making: P-value Method01:09

Decision Making: P-value Method

7.2K
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...
7.2K
Dynamic Equilibrium02:20

Dynamic Equilibrium

67.3K
A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
67.3K
Mathematical Modeling: Problem Solving01:29

Mathematical Modeling: Problem Solving

523
Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...
523
Modeling with Differential Equations01:25

Modeling with Differential Equations

255
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
255
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

5.8K
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...
5.8K
Alternative Sets of Equilibrium Equations01:31

Alternative Sets of Equilibrium Equations

1.1K
When analyzing the behavior of structures, engineers often rely on the concept of equilibrium. This refers to the state where all forces and moments acting on a system balance each other, resulting in no net movement or rotation. In many cases, equilibrium can be described by a set of standard equations. However, in some situations, alternative sets of equilibrium equations must be used to describe the system's behavior accurately.
One example of such a situation can be observed in a...
1.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same journal

Learning With Diagrams and Visualizations: Visual, Learner, and Contextual Factors.

Wiley interdisciplinary reviews. Cognitive science·2026
Same journal

Functional Neural Architecture of Working Memory in Musicians: An ALE Meta-Analysis and Review.

Wiley interdisciplinary reviews. Cognitive science·2026
Same journal

Collective Memory in Animals.

Wiley interdisciplinary reviews. Cognitive science·2026
Same journal

What Counts as an Environment in Memory Research? Conceptualizing Environment Across Memory Traditions.

Wiley interdisciplinary reviews. Cognitive science·2026
Same journal

Origins and Evolution of Imagination, From Australopithecus to Modern-Day Deep Learning.

Wiley interdisciplinary reviews. Cognitive science·2026
Same journal

Multilevel Perceptual-Motor Coupling: From Action Understanding to Execution.

Wiley interdisciplinary reviews. Cognitive science·2026
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Apr 5, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.9K

Game playing.

Christopher D Rosin1

  • 1Parity Computing, Inc., San Diego, CA, USA.

Wiley Interdisciplinary Reviews. Cognitive Science
|August 26, 2015
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) research has long used game playing to test computational approaches against human expertise. Key AI algorithms like minimax search and Monte Carlo tree search have achieved significant success in complex games.

More Related Videos

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

2.7K

Related Experiment Videos

Last Updated: Apr 5, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.9K
The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

2.7K

Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Computer Science

Background:

  • Game playing is a foundational research area in artificial intelligence (AI).
  • Games offer clear benchmarks for comparing AI computational approaches with human expertise.
  • AI research has historically focused on developing algorithms to surpass human performance in specific games.

Purpose of the Study:

  • To review the historical development of AI in game playing.
  • To highlight key algorithmic advancements and their impact.
  • To discuss current challenges and future research directions in AI game playing.

Main Methods:

  • Review of seminal AI game playing research.
  • Analysis of core algorithms such as minimax search, machine learning from self-play, and Monte Carlo tree search.
  • Examination of the application and generalization of these algorithms across various games.

Main Results:

  • AI has achieved superhuman performance in numerous games.
  • Specific algorithms like minimax (chess), self-play (backgammon), and Monte Carlo tree search (Go) have been pivotal.
  • These methods have demonstrated successful generalization to other game domains.

Conclusions:

  • AI has made substantial progress in game playing, surpassing human expertise in many instances.
  • Continued research is essential for discovering and developing novel algorithmic foundations.
  • The field faces ongoing challenges in advancing AI capabilities within game-playing domains.