Incremental heuristic search algorithms combine both incremental and heuristic search to speed up searches of sequences of similar search problems, which is important in domains that are only incompletely known or change dynamically. Incremental search has been studied at least since the late 1960s. Incremental search algorithms reuse information from previous searches to speed up the current search and solve search problems potentially much faster than solving them repeatedly from scratch. Similarly, heuristic search has also been studied at least since the late 1960s. Heuristic search algorithms, often based on A*, use heuristic knowledge in the form of approximations of the goal distances to focus the search and solve search problems potentially much faster than uninformed search algorithms. The resulting search problems, sometimes called dynamic path planning problems, are graph search problems where paths have to be found repeatedly because the topology of the graph, its edge costs, the start vertex or the goal vertices change over time. So far, three main classes of incremental heuristic search algorithms have been developed: * The first class restarts A* at the point where its current search deviates from the previous one (example: Fringe Saving A*). * The second class updates the h-values (heuristic, i.e. approximate distance to goal) from the previous search during the current search to make them more informed (example: Generalized Adaptive A*). * The third class updates the g-values (distance from start) from the previous search during the current search to correct them when necessary, which can be interpreted as transforming the A* search tree from the previous search into the A* search tree for the current search (examples: Lifelong Planning A*, D*, D* Lite). All three classes of incremental heuristic search algorithms are different from other replanning algorithms, such as planning by analogy, in that their plan quality does not deteriorate with the number of replanning episodes. (Wikipedia).
Conducting an Online Job Search
In this video, you’ll learn more about conducting an online job search. Visit https://www.gcflearnfree.org/jobsearchandnetworking/find-a-job-online/1/ to learn even more. We hope you enjoy!
From playlist Searching for a Job
Google and eigenvalues. We describe the Pagerank algorithm, which was one of the algorithms used by Google for their search engine. For this, we rank the websites using an importance vector vector and write the system as a Markov chain, using matrices. Then we diagonalize the matrix using
From playlist Eigenvalues
In this video, you’ll learn some tips and tricks for getting the most out of using Google to search for stuff online. Visit https://edu.gcfglobal.org/en/searchbetter/google-search-tips/1/ to learn even more. We hope you enjoy!
From playlist Search Better
Google maps create link to location short URL tutorial
Recently I tried to send a friend a link to an address on google maps and found the web address to be incredibly long. This tutorial shows you how to shorten the URL to a particular shop or restaurant.
From playlist Technology
Recursion Replacement - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Advanced 1. Incremental Path Planning
MIT 16.412J Cognitive Robotics, Spring 2016 View the complete course: https://ocw.mit.edu/16-412JS16 Instructor: MIT students This is the 2nd advanced lecture in the MIT 16.412 Cognitive Robotics of Spring 2016, led by MIT students. Students took a deep dive into the topic of incremental
From playlist MIT 16.412J Cognitive Robotics, Spring 2016
Indexing 13: heuristics for faster search
We discuss the following heuristics for speeding up query execution: caching of search results, binary search (for conjunctions), skip pointers, early termination, top-docs, and prioritised processing.
From playlist IR7 Inverted Indexing
Lec 13 | MIT 6.01SC Introduction to Electrical Engineering and Computer Science I, Spring 2011
Lecture 13: Optimizing a Search Instructor: Dennis Freeman View the complete course: http://ocw.mit.edu/6-01SCS11 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.01SC Introduction to EECS I
Solving a trigonometric equation with applying pythagorean identity
👉 Learn how to solve trigonometric equations. There are various methods that can be used to evaluate trigonometric equations, they include factoring out the GCF and simplifying the factored equation. Another method is to use a trigonometric identity to reduce and then simplify the given eq
From playlist Solve Trigonometric Equations by Factoring
Bob Hearn - There Are Only 15 - G4G13 Apr 2018
Notes on the proof that there are only 15 convex pentagons that tile the plane.
From playlist G4G13 Videos
Sigmoid functions for population growth and A.I.
Some elaborations on sigmoid functions. https://en.wikipedia.org/wiki/Sigmoid_function https://www.learnopencv.com/understanding-activation-functions-in-deep-learning/ If you have any questions of want to contribute to code or videos, feel free to write me a message on youtube or get my co
From playlist Analysis
Model Evaluation | Stanford CS224U Natural Language Understanding | Spring 2021
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai To learn more about this course visit: https://online.stanford.edu/courses/cs224u-natural-language-understanding To follow along with the course schedule and s
From playlist Stanford CS224U: Natural Language Understanding | Spring 2021
DDPS | Computational Scientific Discovery: Heuristic Search for Communicable Laws and Models
Description: Scientific discovery was long viewed as a uniquely human creative activity, but digital computers have now reproduced many facets of this process. In this talk, I examine previous research on this problem, which posits that discovery involves heuristic search through a space o
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
The Power of Self-Learning Systems - IAS - Demis Hassabis
On May 4, 2019, Demis Hassabis, Co-founder and CEO of DeepMind, discussed the capabilities and power of self-learning systems in a public lecture at the Institute for Advanced Study. He illustrated this with reference to some of DeepMind's recent breakthroughs and spoke about the applicati
From playlist AI talks
How to Find the Matrix of a Linear Transformation
This is a very elementary discussion of linear transformations and matrices. I mention nothing about bases in this video and just give an easy way to identify the matrix. Hopefully this helps someone. The pencils I used in this video: https://amzn.to/3bCpvpt The paper I used in this video
From playlist Linear Algebra
10 4 BFS and Undirected Connectivity 13 min
From playlist Algorithms 1
Master Simplifying trigonometric expressions using Basic identities
Subscribe! http://www.freemathvideos.com Want more math video lessons? Visit my website to view all of my math videos organized by course, chapter and section. The purpose of posting my free video tutorials is to not only help students but allow teachers the resources to flip their classro
From playlist Analytic Trigonometry #Master