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Related Concept Videos

Heuristics01:21

Heuristics

Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
The Availability Heuristic01:08

The Availability Heuristic

A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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:
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light bulb,...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000...

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Related Experiment Video

Updated: May 29, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

A New Heuristic Search Technique-Algorithm SA.

B Zhang1, L Zhang

  • 1Department of Computer Engineering and Science, Tsinghua University, Beijing, People's Republic of China.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

We developed Algorithm SA, a new heuristic search method using statistical inference. This algorithm guarantees finding a goal node with Algorithm SA, improving search efficiency in uniform m-ary trees.

Related Experiment Videos

Last Updated: May 29, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Algorithm Analysis

Background:

  • Heuristic search algorithms are fundamental in artificial intelligence and computer science for problem-solving.
  • Algorithm A (A*) is a widely used informed search algorithm known for its optimality and efficiency.
  • Statistical inference offers powerful tools for enhancing decision-making processes within algorithms.

Purpose of the Study:

  • To introduce a novel heuristic search algorithm, Algorithm SA, by integrating statistical inference with Algorithm A.
  • To analyze the performance of Algorithm SA in a simplified search space.
  • To determine the probability of finding a goal node and its mean complexity.

Main Methods:

  • Development of Algorithm SA, a heuristic search algorithm incorporating statistical inference.
  • Theoretical analysis of Algorithm SA's performance on a uniform m-ary tree.
  • Derivation of the probability of goal node discovery and mean complexity.

Main Results:

  • Algorithm SA guarantees finding a goal node with a probability of one.
  • The mean complexity of Algorithm SA is determined to be O(N·ln N), where N is the goal depth.
  • Demonstrated improved performance in a simplified search space compared to existing methods.

Conclusions:

  • Algorithm SA represents a significant advancement in heuristic search, offering guaranteed goal discovery.
  • The integration of statistical inference enhances the efficiency and reliability of search algorithms.
  • Algorithm SA shows promise for applications requiring efficient and certain pathfinding.