Problem Solving Views of Problem solving Well-defined problems Much studied in AI Requires search Domain general heuristics for solving problems What about ill-defined problems? No real mechanisms for dealing with these

ByFormal Description of a Problem In AI, we will formally define a problem as a space of all possible configurations where each configuration is called a state thus, we use the term state space an initial state one or more goal states

ByLecture 6: Adversarial Search & Games. Reading: Ch. 6, AIMA. Adversarial search. So far, single agent search – no opponents or collaborators Multi-agent search: Playing a game with an opponent: adversarial search

ByGame Playing. Chapter 5 some slides from UC-Irvine, CS Dept. Game Playing and AI. Game playing (was?) thought to be a good problem for AI research: game playing is non-trivial players need “human-like” intelligence games can be very complex (e.g., chess, go)

ByProblem Solving. Russell and Norvig: Chapter 3 CSMSC 421 – Fall 2006. sensors. environment. ?. agent. actuators. Problem-Solving Agent. sensors. environment. ?. agent. actuators. Formulate Goal Formulate Problem States Actions Find Solution. Problem-Solving Agent.

ByInverse Kinematics (part 1). CSE169: Computer Animation Instructor: Steve Rotenberg UCSD, Winter 2005. Welman, 1993. “Inverse Kinematics and Geometric Constraints for Articulated Figure Manipulation”, Chris Welman, 1993 Masters thesis on IK algorithms

ByThe Bees Algorithm, and Its Applications. Dr. Ziad Salem, HDD, PhD, BsC. Spezs1@hotmail.com Computer Engineering Department Electrical and Electronic Engineering Faculty Aleppo University, Aleppo, Syrian Arab Republic. Outlines. Introduction Intelligent Swarm –based optimisation

ByUninformed Search. Reading: Chapter 3 by today, Chapter 4.1-4.3 by Wednesday, 9/12 Homework #2 will be given out on Wednesday DID YOU TURN IN YOUR SURVEY? USE COURSEWORKS AND TAKE THE TEST. Pending Questions. Class web page: http://www.cs.columbia.edu/~kathy/cs4701

ByARTIFICIAL INTELLIGENCE CSCI/PHIL-4550/6550 (IT’S FOR REAL) DON POTTER Institute for Artificial Intelligence and Computer Science Department UGA. AI @ UGA * - Originated around 1985. * - First MS degree awarded: 1988.

ByProblem Solving. Views of Problem solving. Well-defined problems Much studied in AI Requires search Domain general heuristics for solving problems What about ill-defined problems? No real mechanisms for dealing with these

ByProblem solving by Searching. Problem Formulation. 1. 1. 2. 2. 3. 3. 4. 4. 5. 8. 6. 7. 7. 6. 8. 5. 8-Puzzle problem. Solve the following 8-Puzzle problem by moving tiles left, down, up and right. Initial State. goal State. 1. 1. 1. 2. 2. 3. 3. 3. 2. 4. 4. 8. 5. 8.

ByCS 63. Uninformed Search. Chapter 3. Some material adopted from notes and slides by Marie desJardins and Charles R. Dyer. Today’s class. Goal-based agents Representing states and operators Example problems Generic state-space search algorithm Specific algorithms Breadth-first search

ByInformed Search Algorithms. Chapter 4 Feb 2 2007. Belief States (Chap 3). Graph Search (Chap 3). Graph Search: Analysis. Time and Space complexity: proportional to state space (< O(b d )) Optimality: may not be optimal if algorithm throws away a cheaper path to a node that is already closed

ByProblem Solving Through Search. Problems and Search. Problem formulation: an essential step in building an intelligent agent Search: a fundamental and very common solution technique Chapter 3: “Goal”-oriented problems, and basic search strategies Chapter 4: “Informed” search strategies .

ByUninformed Search. Chapter 3. Some material adopted from notes by Charles R. Dyer, University of Wisconsin-Madison. Big Idea. Newell A & Simon H A. Human problem solving . Englewood Cliffs, NJ: Prentice-Hall. 1972. .

ByHow can we improve searching strategy by using intelligence? Map example: Heuristic: Expand those nodes closest in “as the crow flies” distance to goal 8-puzzle: Heuristic: Expand those nodes with the most tiles in place Intelligence lies in choice of heuristic. Informed Search Methods.

ByCS 63. Informed Search. Chapter 4. Adapted from materials by Tim Finin, Marie desJardins, and Charles R. Dyer. Today’s Class. Iterative improvement methods Hill climbing Simulated annealing Local beam search Genetic algorithms Online search

ByRFID 를 활용한 유비쿼터스 서비스. 2005 년 4 월 15 일 권오병 경희대학교 국제경영학부 obkwon@khu.ac.kr. Level of services. Business viability. Technical viability. Ubiquitous services & Biz model. Ubiquitous services & Biz model. Service Architecture. RFID-Based Ubiquitous Services. Some cases NAMA-RFID

ByInformed search algorithms. Chapter 4. Outline. Best-first search Greedy best-first search A * search Heuristics Memory Bounded A* Search. Best-first search. Idea: use an evaluation function f(n) for each node f(n) provides an estimate for the total cost.

ByBasic search techniques. Points State spaces • Examples Issues in search Depth-first and breadth-first search Cost analysis of blind search • The cost of breadth-first search • The cost of depth-first search Iterative deepening • The cost of iterative deepening Beam search

ByView Goal state PowerPoint (PPT) presentations online in SlideServe. SlideServe has a very huge collection of Goal state PowerPoint presentations. You can view or download Goal state presentations for your school assignment or business presentation. Browse for the presentations on every topic that you want.