Conventional AI Machine Learning Techniques Environmental Mapping Case Statements Statistical Analysis Computational Intelligence Pattern Recognition.

Slides:



Advertisements
Similar presentations
Heuristic Search techniques
Advertisements

Artificial Intelligence By Mr. Ejaz CIIT Sahiwal.
Problem Solving by Searching Copyright, 1996 © Dale Carnegie & Associates, Inc. Chapter 3 Spring 2007.
Best-First Search: Agendas
Solving Problems with Constraints Karen E. Petrie.
CPSC 322, Lecture 4Slide 1 Search: Intro Computer Science cpsc322, Lecture 4 (Textbook Chpt ) January, 12, 2009.
Chapter 7 Dynamic Programming 7.
§ 8 Dynamic Programming Fibonacci sequence
And Just Games etc.. EVOLUTION OF COMPUTER GAMES PongOdyssey Beginning of the use of microprocessors ATARI VCS system bit.
Table of Contents Graphing Systems of Linear Inequalities It is assumed you already know how to graph a linear inequality, which was discussed in a previous.
Artificial Intelligence
State-Space Searches.
Artificial Intelligence Course outline Introduction Problem solving Generic algorithms Knowledge Representation and Reasoning Expert Systems Uncertainty.
Artificial Intelligence
Mathematical Problem Solving Math 6320 Summer 2006.
Chapter 11: Artificial Intelligence
Lab 3 How’d it go?.
Computer Science 1000 AI – A Brief Overview. Artificial Intelligence definition: many! `` ``The exciting new effort to make computers think... machines.
Artificial Intelligence Dr. Paul Wagner Department of Computer Science University of Wisconsin – Eau Claire.
Chapter 14: Artificial Intelligence Invitation to Computer Science, C++ Version, Third Edition.
Artificial Intelligence: Its Roots and Scope
计算机科学概述 Introduction to Computer Science 陆嘉恒 中国人民大学 信息学院
Chapter 5 Dynamic Programming 2001 년 5 월 24 일 충북대학교 알고리즘연구실.
7 -1 Chapter 7 Dynamic Programming Fibonacci sequence Fibonacci sequence: 0, 1, 1, 2, 3, 5, 8, 13, 21, … F i = i if i  1 F i = F i-1 + F i-2 if.
Artificial Intelligence Tarik Booker. What we will cover… History Artificial Intelligence as Representation and Search Languages used in Artificial Intelligence.
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.
Ambiguous Law of Sines Compute b sin A, then compare to a No solution One Solution Two Solutions One Solution Compute side a to side b No solution One.
Graphing Systems of Linear Inequalities It is assumed you already know how to graph a linear inequality, which was discussed in a previous module. Graphing.
Outline Intro to Representation and Heuristic Search Machine Learning (Clustering) and My Research.
Advanced Artificial Intelligence Lecture 2: Search.
Intelligent Control Methods Lecture 4: Searching in State Space Slovak University of Technology Faculty of Material Science and Technology in Trnava.
CSCI 4310 Lecture 2: Search. Search Techniques Search is Fundamental to Many AI Techniques.
Intro to Planning Or, how to represent the planning problem in logic.
Introduction to Artificial Intelligence CS 438 Spring 2008.
CS483/683 Multi-Agent Systems Lecture 2: Distributed variants of 2 important AI problems: Search and Constraint Satisfaction 21 January 2010 Instructor:
Introduction to Artificial Intelligence (G51IAI) Dr Rong Qu Blind Searches - Introduction.
Ferdowsi University of Mashhad 1 Automatic Semantic Web Service Composition based on owl-s Research Proposal presented by : Toktam ghafarian.
Reinforcement Learning AI – Week 22 Sub-symbolic AI Two: An Introduction to Reinforcement Learning Lee McCluskey, room 3/10
Chapter 2 Approaches/Methods Artificial Intelligence Instructor: Haris Shahzad Artificial Intelligence CS-402.
CHAPTER 2 SEARCH HEURISTIC. QUESTION ???? What is Artificial Intelligence? The study of systems that act rationally What does rational mean? Given its.
Chapter 21 Robotic Perception and action Chapter 21 Robotic Perception and action Artificial Intelligence ดร. วิภาดา เวทย์ประสิทธิ์ ภาควิชาวิทยาการคอมพิวเตอร์
CSCE 552 Fall 2012 AI By Jijun Tang. Homework 3 List of AI techniques in games you have played; Select one game and discuss how AI enhances its game play.
1 Solving Problems with Methods Questions. 2 Problem solving is a process similar to working your way through a maze. But what are these “steps” and what.
 Read o Identify facts and clue words o Identify the question o Understand the vocabulary.
Artificial Intelligence
Polya’s Problem Solving
Schedule for next 2 weeks
Design and Analysis of Algorithm
Introduction to Artificial Intelligence
Problem Solving by Searching
Artificial Intelligence
إعداد المشرفة التربوية نجلاء الجارد
Lecture 1B: Search.
CSE 4705 Artificial Intelligence
CIS 488/588 Bruce R. Maxim UM-Dearborn
A graphing calculator is required for some problems or parts of problems 2000.
Course Outline Advanced Introduction Expert Systems Topics Problem
Search Exercise Search Tree? Solution (Breadth First Search)?
Artificial Intelligence
Problem Spaces & Search
State-Space Searches.
State-Space Searches.
The Rich/Knight Implementation
Example 5A: Solving Simple Rational Equations
State-Space Searches.
More Problem Solving.
The Rich/Knight Implementation
Artificial Intelligence Machine Learning
Presentation transcript:

Conventional AI Machine Learning Techniques Environmental Mapping Case Statements Statistical Analysis Computational Intelligence Pattern Recognition Heuristics Reasoning What is Machine Intelligence?

AI - Environmental Mapping Way Points Pre-Determined Paths No “On the Fly” Calculations If POINT_A -> POINT_B then //Use Path #1 Map:

CI – Heuristics & Reasoning How do you go from point_A -> point_B without knowing what exists between the two points? Heuristics! [1] Draw a picture Assume a solution exists and work backwards Start simple with a generic case [1] Polya, George. How to Solve It. Reissue ed. Princeton Univ Pr, 1982.

CI – Heuristics & Reasoning Picture given Assume end point is reachable within constraints Start with simple points Use reasoning to decide next move

CI – Heuristics & Reasoning Continue process How did we do? Is there a better solution?

Some things to keep in mind! Remember previous obstacles Develop a complete map of environment Must be “On the Fly” programming Parameters Dimensions of the robot Speed/Time

Hybrid Intelligence Combining AI & CI allows for optimization Pattern recognition Priority searches Weighted graphs Multiple node maps