'Unexpanded node' presentation slideshows

DCP 1172 Introduction to Artificial Intelligence

DCP 1172 Introduction to Artificial Intelligence. Lecture notes for Chap. 4 [AIMA] Chang-Sheng Chen. Last time: Problem-Solving. Problem solving: Goal formulation Problem formulation (states, operators) Search for solution Problem formulation : Initial state Operators Goal test

1.15k views • 96 slides

CS 541: Artificial Intelligence

1.15k views • 93 slides

Artificial Intelligence

1.09k views • 75 slides

CPSC 7373: Artificial Intelligence

CPSC 7373: Artificial Intelligence. Jiang Bian, Fall 2012 University of Arkansas at Little Rock. Lecture 3: Problem solving by searching. Outline Problem-solving agents Problem types Problem formulation Example problems Basic search algorithms. Problem-solving agents. Example: Romania.

836 views • 67 slides

Midterm Review

Midterm Review. Topic 1 - Agents. Agents and environments Rationality PEAS (Performance measure, Environment, Actuators, Sensors) Environment types Agent types. PEAS: Specifying an automated taxi driver. P erformance measure : safe, fast, legal, comfortable, maximize profits

668 views • 66 slides

Solving problems by searching

Solving problems by searching. Chapter 3. Outline. Problem-solving agents Problem types Problem formulation Example problems Basic search algorithms. Problem-solving agents. August 2008. Formulating Problems . Initial State Successor Function (description of possible actions)

742 views • 57 slides

A General Introduction to Artificial Intelligence

A General Introduction to Artificial Intelligence. Outline. Best-first search. Greedy best-first search A * search. Characteristics of Heuristics Variations of A* Memory Bounded A*. Online (Real Time) A*. Local search algorithms Hill-climbing search. Simulated annealing search.

656 views • 55 slides

C hapter 4

C hapter 4. Informed Search and Exploration. O utline. Best-First Search Greedy Best-First Search A * Search Heuristics Variances of A* Search. T ree Search (Reviewed, Fig. 3.9). A search strategy is defined by picking the order of node expansion.

821 views • 49 slides

Reading Material

572 views • 48 slides

Blind and Informed Search Methods

Blind and Informed Search Methods. Filename: eie426-search-methods-0809.ppt. Contents:. Problem types Problem formulation Example problems Basic search algorithms: - breadth-first search - depth-first search Best-first search - greedy search - A* search. Example: Romania.

689 views • 45 slides

Symbolic AI: Problem Solving

E. Trentin, DIISM. Symbolic AI: Problem Solving. Example: Romania. On holiday in Romania; currently in Arad. Flight leaves tomorrow from Bucharest Formulate goal : be in Bucharest Formulate problem : states : various cities actions : drive between cities Find solution :

569 views • 42 slides

Informed search algorithms

Informed search algorithms. Chapter 4. Material. Chapter 4 Section 1 - 3 Exclude memory-bounded heuristic search . Outline. Best-first search Greedy best-first search A * search Heuristics Local search algorithms Hill-climbing search Simulated annealing search Local beam search

414 views • 41 slides

Informed Search

Informed Search. Introduction to Artificial Intelligence CS440/ECE448 Lecture 4 FIRST HOMEWORK OUT TODAY CHECK CLASS WEB PAGE!. Last lecture. Uninformed search Breadth-first search Uniform-cost search Depth-first search Depth-limited search Iterative deepening search This lecture

625 views • 36 slides

Best-first search

Best-first search. Idea: use an evaluation function f(n) for each node estimate of "desirability" Expand most desirable unexpanded node Implementation : Order the nodes in fringe in decreasing order of desirability or order does not matter pick the desirable one Special cases:

386 views • 31 slides

Problem Solving: Informed Search Algorithms

Edmondo Trentin, DIISM. Problem Solving: Informed Search Algorithms. Best-first search. Idea: use an evaluation function f(n) for each node n f(n) is an estimated "measure of desirability" of nodes Rule: expand most desirable unexpanded node Implementation :

396 views • 26 slides

CSC3203: AI for Games Informed search (1)

CSC3203: AI for Games Informed search (1). Patrick Olivier p.l.olivier@ncl.ac.uk. Depth-first. Iterative deepening. Breadth-first. Uninformed search: summary. Informed search strategies. Best-first search Greedy best-first search A* search Local beam search Simulated annealing search

384 views • 23 slides