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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):
The Representativeness Heuristic02:13

The Representativeness Heuristic

The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
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...
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...
Theorems of Pappus and Guldinus: Problem Solving01:12

Theorems of Pappus and Guldinus: Problem Solving

Pappus and Guldinus's theorems are powerful mathematical principles that are used for finding the surface area and volume of composite shapes. For example, consider a cylindrical storage tank with a conical top. Finding the surface area or volume can be challenging for such complex shapes. These theorems are particularly useful in calculating the volume and surface area of such systems. Here, the cylindrical storage tank with a conical top can be broken down into two simple shapes: a cylinder...

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Related Experiment Video

Updated: May 29, 2026

Exploring the Role of Deontic Reasoning and World Knowledge in Wason´s Selection Task
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Exploring the Role of Deontic Reasoning and World Knowledge in Wason´s Selection Task

Published on: July 22, 2025

Studies in semi-admissible heuristics.

J Pearl1, J H Kim

  • 1SENIOR MEMBER, IEEE, Cognitive Systems Laboratory, School of Engineering and Applied Science, University of California, Los Angeles, CA 90024.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary

This study presents three enhanced A* search algorithms for improved efficiency. One algorithm, R*, significantly reduces search time for the Traveling Salesman Problem with minimal impact on solution cost.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Algorithm Optimization

Background:

  • The A* search algorithm is a cornerstone in pathfinding and optimization problems.
  • Its efficiency is often tied to the admissibility of heuristic functions, which can be computationally expensive.
  • Relaxing admissibility offers potential for speedups but requires careful management of solution quality.

Purpose of the Study:

  • To introduce and evaluate novel extensions of the A* search algorithm designed to enhance search efficiency.
  • To explore the trade-offs between search speed and solution optimality when relaxing the admissibility condition.
  • To assess the performance of these new algorithms on a computationally challenging problem, the Traveling Salesman Problem.

Main Methods:

  • Development of three A* algorithm variants: A* with relaxed termination, R* with controlled heuristic violation, and R*/* combining features of both.
  • Empirical evaluation using the Traveling Salesman Problem as a benchmark.
  • Comparison of search efficiency (number of expansions, search time) and solution cost against the standard A* algorithm.

Main Results:

  • The modified A* algorithm demonstrates significant advantages in complex scenarios with numerous near-optimal subtours.
  • The R* algorithm achieved a 4:1 reduction in search time compared to A*.
  • This speedup with R* came with only a marginal increase in the final solution cost.

Conclusions:

  • Relaxing the admissibility condition in A* search algorithms can lead to substantial improvements in computational efficiency.
  • The proposed R* algorithm offers a practical approach to balancing speed and optimality for difficult search problems.
  • These findings have implications for optimizing search in various AI and operations research applications.