UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering CSCE 580 Artificial Intelligence Problem Spaces and Search Fall 2008 Jingsong.

Slides:



Advertisements
Similar presentations
Artificial Intelligence By Mr. Ejaz CIIT Sahiwal.
Advertisements

Artificial Intelligence By Mr. Ejaz CIIT Sahiwal.
Computer Science 112 Fundamentals of Programming II Overview of Collections.
Elizabeth City State University Ronald E. McNair Post baccalaureate Achievement Program La’Shanda Dukes and Justin Deloatch Faculty Mentor: Dr. Jamiiru.
Part2 AI as Representation and Search
CS 480 Lec 3 Sept 11, 09 Goals: Chapter 3 (uninformed search) project # 1 and # 2 Chapter 4 (heuristic search)
CPSC 322, Lecture 5Slide 1 Uninformed Search Computer Science cpsc322, Lecture 5 (Textbook Chpt 3.4) January, 14, 2009.
Lecture 16: Tree Traversal.
Artificial Intelligence (CS 461D)
Uninformed Search Jim Little UBC CS 322 – Search 2 September 12, 2014
Artificial Intelligence Lecture No. 7 Dr. Asad Safi ​ Assistant Professor, Department of Computer Science, COMSATS Institute of Information Technology.
Graphs in Computer Science Tim Kimmet, VP Platform Team.
Computing & Information Sciences Kansas State University Lecture 4 of 42 CIS 530 / 730 Artificial Intelligence Lecture 4 of 42 William H. Hsu Department.
UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Blind State-Space Search Notes for Ch.11 of Bratko For CSCE 580 Sp03 Marco.
State-Space Searches. State spaces A state space consists of –A (possibly infinite) set of states The start state represents the initial problem Each.
Midterm 2 Overview Fawzi Emad Chau-Wen Tseng Department of Computer Science University of Maryland, College Park.
CS503: Fifteenth Lecture, Fall 2008 Graphs Michael Barnathan.
Informed Search (no corresponding text chapter). Recall: Wanted " An algorithm and associated data structure(s) that can: 1) Solve an arbitrary 8-puzzle.
UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering CSCE 580 Artificial Intelligence Program 1 Fall 2008 Marco Valtorta
UnInformed Search What to do when you don’t know anything.
UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering CSCE 580 Artificial Intelligence Ch.3 [P]: Searching Fall 2009 Marco Valtorta.
1 Tree Searching Strategies. 2 The procedure of solving many problems may be represented by trees. Therefore the solving of these problems becomes a tree.
Data Structures & Algorithms What The Course Is About s Data structures is concerned with the representation and manipulation of data. s All programs.
1 Solving Problems by Searching. 2 Terminology State State Space Initial State Goal Test Action Step Cost Path Cost State Change Function State-Space.
Implementing the Intelligent Systems Knowledge Units of Computing Curricula 2001 Ingrid Russell Todd Neller.
Blind Search-Part 2 Ref: Chapter 2. Search Trees The search for a solution can be described by a tree - each node represents one state. The path from.
1 Tree Searching Strategies. 2 The procedure of solving many problems may be represented by trees. Therefore the solving of these problems becomes a tree.
Graphs & Graph Algorithms Nelson Padua-Perez Bill Pugh Department of Computer Science University of Maryland, College Park.
Data Structures, Spring 2004 © L. Joskowicz 1 DAST – Final Lecture Summary and overview What we have learned. Why it is important. What next.
UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Heuristic Search Notes for Ch.12 of Bratko and Ch.2 of Shoham For CSCE 580.
Solving Problems by Searching
State-Space Searches.
Artificial Intelligence Course outline Introduction Problem solving Generic algorithms Knowledge Representation and Reasoning Expert Systems Uncertainty.
Instructor: Dr. Sahar Shabanah Fall Lectures ST, 9:30 pm-11:00 pm Text book: M. T. Goodrich and R. Tamassia, “Data Structures and Algorithms in.
Parallel Search Algorithm
MA/CSSE 473 Day 12 Insertion Sort quick review DFS, BFS Topological Sort.
© 2007 Pearson Addison-Wesley. All rights reserved 0-1 Spring(2007) Instructor: Qiong Cheng © 2007 Pearson Addison-Wesley. All rights reserved.
Introduction to search Chapter 3. Why study search? §Search is a basis for all AI l search proposed as the basis of intelligence l inference l all learning.
How are things going? Core AI Problem Mobile robot path planning: identifying a trajectory that, when executed, will enable the robot to reach the goal.
Introduction to search Chapter 3. Why study search? §Search is a basis for all AI l search proposed as the basis of intelligence l all learning algorithms,
Today’s Topics FREE Code that will Write Your PhD Thesis, a Best-Selling Novel, or Your Next Methods for Intelligently/Efficiently Searching a Space.
CS 344 Artificial Intelligence By Prof: Pushpak Bhattacharya Class on 15/Jan/2007.
CS 415 – A.I. Slide Set 5. Chapter 3 Structures and Strategies for State Space Search – Predicate Calculus: provides a means of describing objects and.
State-Space Searches. 2 State spaces A state space consists of A (possibly infinite) set of states The start state represents the initial problem Each.
Structures and Strategies For Space State Search
CS.462 Artificial Intelligence SOMCHAI THANGSATHITYANGKUL Lecture 02 : Search.
Artificial Intelligence
Artificial Intelligence LECTURE 4 ARTIFICIAL INTELLIGENCE LECTURES BY ENGR. QAZI ZIA.
Lecture 13 Jianjun Hu Department of Computer Science and Engineering University of South Carolina CSCE350 Algorithms and Data Structure.
Search with Costs and Heuristic Search 1 CPSC 322 – Search 3 January 17, 2011 Textbook §3.5.3, Taught by: Mike Chiang.
Computing & Information Sciences Kansas State University Wednesday, 30 Aug 2006CIS 490 / 730: Artificial Intelligence Lecture 4 of 42 Wednesday, 30 August.
Advanced Artificial Intelligence Lecture 2: Search.
Computer Science CPSC 322 Lecture 6 Iterative Deepening and Search with Costs (Ch: 3.7.3, 3.5.3)
For Friday Read chapter 4, sections 1 and 2 Homework –Chapter 3, exercise 7 –May be done in groups.
Uninformed search strategies A search strategy is defined by picking the order of node expansion Uninformed search strategies use only the information.
Search Techniques CS480/580 Fall Introduction Trees: – Root, parent, child, sibling, leaf node, node, edge – Single path from root to any node Graphs:
1 Data Structures CSCI 132, Spring 2014 Lecture 1 Big Ideas in Data Structures Course website:
Breadth First Search and Depth First Search. Greatest problem in Computer Science Has lead to a lot of new ideas and data structures Search engines before.
Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Lecture 5 of 41 Wednesday 03.
CSCE350 Algorithms and Data Structure Lecture 21 Jianjun Hu Department of Computer Science and Engineering University of South Carolina
Now, Chapter 5: Decrease and Conquer Reduce problem instance to smaller instance of the same problem and extend solution Solve smaller instance Extend.
CPSC 322, Lecture 5Slide 1 Uninformed Search Computer Science cpsc322, Lecture 5 (Textbook Chpt 3.5) Sept, 13, 2013.
Artificial Intelligence Lecture No. 8 Dr. Asad Ali Safi ​ Assistant Professor, Department of Computer Science, COMSATS Institute of Information Technology.
Best-first search is a search algorithm which explores a graph by expanding the most promising node chosen according to a specified rule.
Biointelligence Lab School of Computer Sci. & Eng. Seoul National University Artificial Intelligence Chapter 8 Uninformed Search.
Brute Force and Exhaustive Search Brute Force and Exhaustive Search Traveling Salesman Problem Knapsack Problem Assignment Problem Selection Sort and Bubble.
CS 1010– Introduction to Computer Science Daniel Tauritz, Ph.D. Associate Professor of Computer Science Director, Natural Computation Laboratory Academic.
Electrical Engineering
Artificial Intelligence
Introduction to Computer Science for Majors II
Presentation transcript:

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering CSCE 580 Artificial Intelligence Problem Spaces and Search Fall 2008 Jingsong Wang

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Abstraction and Generality in Java The Interaction between AI theory and program architecture –Implementation of search strategies Generality and reuse –Abstraction mechanisms and design patterns

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Search Algorithms Uninformed algorithms: –Depth-first search (DFS) –Breadth-first search (BFS) –… Informed algorithms –Best-first search –…

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Search Algorithms Model the structure of a problem graphically as a state—space Elements defining a state-space: –A formal representation of possible states of a problem solution –Operators for generating new states from a given state –Some way of recognizing a goal state –A starting state of the problem, represented as the root of the graph

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Search Algorithms DFS – stack BFS – queue Best-first search – priority queue –Heuristic rank –sorted

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Abstracting Problem States The separation of representation and control –States and search engines Goal: to define an abstract representation of problems states that supports the general search algorithm and can be easily specialized through the mechanism of class inheritance

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Abstracting Problem States

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Abstracting Problem States Abstract class

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Abstracting Problem States Interface

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Abstracting Problem States Java’s generic –Collection-Type

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Traversing the Problem Space Implementation of search itself –Define the list of states and the mechanisms for moving through them in search

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Putting the Framework to Use See the code in Eclipse