'Goal node' presentation slideshows

Toward More Realistic Pathfinding

Toward More Realistic Pathfinding. Authored by: Marco Pinter. Path finding. How do people find paths? Local knowledge Go downhill Follow the person in front of you Follow the scent of food Global knowledge Consider all possible paths from start to goal

1.6k views • 92 slides

Bayesian Networks

Bayesian Networks. Tamara Berg CS 590-133 Artificial Intelligence. Many slides throughout the course adapted from Svetlana Lazebnik , Dan Klein, Stuart Russell, Andrew Moore, Percy Liang, Luke Zettlemoyer , Rob Pless , Killian Weinberger, Deva Ramanan. Announcements.

764 views • 67 slides

Decision-Making Under Uncertainty

Decision-Making Under Uncertainty . Overview. Two models of decision-making under uncertainty Nondeterministic uncertainty Probabilistic uncertainty In particular, what is the rational action in the presence of repeated states?. Game Playing Review. Game playing with an adversary

927 views • 65 slides

Search

Search. Instructor: Kris Hauser http://cs.indiana.edu/~hauserk. Recap. http:// www.cs.indiana.edu/classes/b351 Brief history and philosophy of AI What is intelligence? Can a machine act/think intelligently? Turing machine, Chinese room. Agenda. Problem Solving using Search

612 views • 52 slides

SuperDecisions Software Tutorial

SuperDecisions Software Tutorial. Installing the SuperDecisions Software Glossary (3 slides) Building the decision hierarchy Removing unintended loops Making Pairwise Comparisons Improving Consistency of pairwise comparisons Entering Direct Data instead of Pairwise Comparing

763 views • 52 slides

CMSC 671 Fall 2001

579 views • 47 slides

Heuristic Search

Heuristic Search. Russell and Norvig: Chapter 4 Slides adapted from: robotics.stanford.edu/~latombe/cs121/2003/home.htm. Heuristic Search. Blind search totally ignores where the goals are. A heuristic function that gives us an estimate of how far we are from a goal. Example:

783 views • 38 slides

Informed Search

CMSC 471. Informed Search. Chapter 4. Prof. Marie desJardins Fall 2009. Adapted from materials by Tim Finin, Eric Eaton, and Charles R. Dyer. Outline. Heuristic search Best-first search Greedy search Beam search A, A* Examples Memory-conserving variations of A* Heuristic functions

558 views • 38 slides

Informed Search

CSC 463 Fall 2010. Informed Search. Sections 3.5, 3.6, 4.1, 4.5 Classes 5 and 6. Adapted from materials by Marie desJardins, Tim Finin , Eric Eaton, and Charles R. Dyer. Outline. Heuristic search Best-first search Greedy search Beam search A, A* Examples

563 views • 38 slides

CMSC 671 Fall 2005

470 views • 37 slides

Artificial Intelligence

601 views • 33 slides

IT/CS 811 Principles of Machine Learning and Inference

IT/CS 811 Principles of Machine Learning and Inference. 3. Rote Learning. Prof. Gheorghe Tecuci. Learning Agents Laboratory Computer Science Department George Mason University. Overview. Rote learning issues. Game playing as a performance task. Rote learning in game paying.

428 views • 28 slides

Towards Modelling Information Security with Key-Challenge Petri Nets

392 views • 24 slides

Chapter 3 Knowledge Representation

Chapter 3 Knowledge Representation. Chapter 3 Contents. The need for a good representation Semantic nets Inheritance Frames Object oriented programming Search trees Combinatorial explosion Problem reduction. The Need for a Good Representation.

423 views • 23 slides

Strategies for Determining Actual Cause

567 views • 21 slides

A fast Prunning Algorithm for optimal Sequence Alignment

A fast Prunning Algorithm for optimal Sequence Alignment. Linear Space Bounded Dynamic Programming. Overview. An introduction to alignments Dynamic Programming Other approaches to optimal alignment calculation A*-star algorithm LBD and boundaries Results Outlook on coming improvements.

344 views • 19 slides

A General Backtracking Algorithm

A General Backtracking Algorithm. Let us say that we can formulate a problem as a sequence of n successive decisions, with each decision consisting of a picking one choice out of a predefined set of options.

306 views • 18 slides

BEST FIRST SEARCH - BeFS

DFS is good becoz it allows a solution to be found without all competing branches having to be expanded. Breadth First Search is good because it does not get trapped on dead-end paths.

241 views • 15 slides

Search in Python

226 views • 12 slides

Romania with step costs in km

500 views • 12 slides