'Informed search' diaporamas de présentation

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

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Heuristic Search

1.12k views • 71 slides

CS 4700: Foundations of Artificial Intelligence

CS 4700: Foundations of Artificial Intelligence. Bart Selman selman@cs.cornell.edu Module: Informed Search Readings R&N - Chapter 3: 3.5 and 3.6. Search. Search strategies determined by choice of node (in queue) to expand Uninformed search: Distance to goal not taken into account

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Cooperating Intelligent Systems

Cooperating Intelligent Systems. Informed search Chapter 4, AIMA 2 nd ed Chapter 3, AIMA 3 rd ed. Romania. Romania. Romania problem. Initial state: Arad Find the minimum distance path to Bucharest. Informed search.

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Informed Search

Informed Search. CS457 David Kauchak Fall 2011. Some material used from : Sara Owsley Sood and others. Admin. Q3 mean: 26.4 median: 27 Final projects proposals looked pretty good start working plan out exactly what you want to accomplish

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Review: Search problem formulation

Review: Search problem formulation. Initial state Actions Transition model Goal state Path cost What is the optimal solution? What is the state space?. Review: Tree search. Initialize the fringe using the starting state While the fringe is not empty

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Administrative

Administrative. Project due Tuesday: Email your code to Yisheng Tang (yit311@lehigh.edu) no later than 11:59PM EST Monday February 6 (the day BEFORE the competition)

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Chapter 2

King Saud University College of Computer and Information Sciences Information Technology Department IT422 - Intelligent systems . Chapter 2. Problem Solving by Searching (1). Objectives. Learn how to formulate a search problem.

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Chapter 2

Shaqra University College of Computer and Information Sciences Information Technology Department CS401- Intelligent systems . Chapter 2. Problem Solving by Searching. Objectives. Learn how to formulate a search problem. Learn the different algorithms used to solve problems by searching.

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Review

616 views • 45 slides

Uninformed Search

Uninformed Search. ECE457 Applied Artificial Intelligence Spring 2008 Lecture #2. Outline. Problem-solving by searching Uninformed search techniques Russell & Norvig, chapter 3. Problem-solving by searching. An agent needs to perform actions to get from its current state to a goal .

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Informed Search I (Beginning of AIMA Chapter 4.1)

Informed Search I (Beginning of AIMA Chapter 4.1). CIS 391 Fall 2008. Outline. PART I Informed = use problem-specific knowledge Best-first search and its variants A * - Optimal Search using Knowledge Proof of Optimality of A* A* for maneuvering AI agents in games Heuristic functions?

449 views • 31 slides

Uninformed Search

Uninformed Search. ECE457 Applied Artificial Intelligence Spring 2007 Lecture #2. Outline. Problem-solving by searching Uninformed search techniques Russell & Norvig, chapter 3. Problem-solving by searching. An agent needs to perform actions to get from its current state to a goal .

1.04k views • 30 slides

Artificial Intelligence Overview

Artificial Intelligence Overview. John Paxton Montana State University February 22, 2005 paxton@cs.montana.edu. Montana State University. A Brief Bio. 1985: The Ohio State University, B.S. 1987: The University of Michigan, M.S. 1990: The University of Michigan, Ph.D.

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COMP 590: Artificial Intelligence

COMP 590: Artificial Intelligence. Today. Course overview What is AI? State of the art in AI today Topics covered in the course. Who is this course for?. An introductory survey of AI techniques for students who have not previously had an exposure to this subject

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Route/Path Planning

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For Monday

For Monday. Read chapter 4 exercise 1 No homework. Program 1. Late Passes. You have 2 for the semester. Only good for programs. Allow you to hand in up to 5 days late IF you have a late pass left. Each good for +.05 on final grade if unused.

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For Wednesday

For Wednesday. Read chapter 5, sections 1-4 Homework: Chapter 3, exercise 23. Then do the exercise again, but use greedy heuristic search instead of A*. Program 1. Any questions?. Problem Formulation. States Initial state Actions Transition model Goal test Path cost.

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How was the project graded?

How was the project graded?. Yan Wang and Dr. Eick both graded the project; cases were our scores disagreed were discussed.

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Applications for Gaming in AI

Applications for Gaming in AI. Sample Projects from Computational Intelligence Course at Washburn University. Outline. Sample projects from this course Challenges. Applications of Informed Search.

207 views • 9 slides