PPT-Uninformed (also called blind) search algorithms

This Lecture Read Chapter 3134 Next Lecture Read Chapter 3537 Please read lecture topic material before and after each lecture on that topic You will be expected to know Overview of uninformed search methods. Jim Little. UBC CS 322 – Search . 2. September . 12, . 2014. Textbook . §. 3.5. 1. CPSC 322, Lecture 4. Slide . 2. Search. is a key computational mechanism in many . AI agents . We will study the basic principles of search on the simple . Lab 01. September 7, 2011. Agenda. Introductions. Course Survey. Disability Etiquette. Preamble. Quiz. Video: “The Ten Commandments”. Discussion. Communication Etiquette. Communicating/interacting . Building Goal-Based Agents. 2. We have a . goal. to reach. Driving from point A to point B. Put 8 queens on a chess board such that no one attacks another. Prove that John is an ancestor of Mary. We have information about where we are now at the . A . search strategy . is defined by picking the order of node . expansion. Uninformed. . search strategies use only the information available in the problem . definition. Breadth-first search. Depth-first search. . Problem Solving Agents . Solutions and Performance. Uninformed Search Strategies. Avoiding Repeated States/Looping. Partial Information. Summary. Problem Solving Agent . Problem-solving agents. Uninformed (Blind) Search Algorithms. Project 1 is out, check class homepage. Due in two weeks 9/27/2010 Monday before class. Projects for students in different groups (480/580/796) could be different later on. Problem - a well defined task.. Sort a list of numbers.. Find a particular item in a list.. Find a winning chess move.. Algorithms. A series of precise steps, known to stop eventually, that solve a problem.. Deconvolution. A.J. Bell and T.J. . Sejnowski. Computational Modeling of Intelligence. 11.03.11.(Fri). Summarized by . Joon. . Shik. Kim. Abstract. Self-organizing learning algorithm that maximizes the information transferred in a network of nonlinear units.. Winter 2018. Introduction to Artificial Intelligence. Prof. Richard Lathrop. Reading: R&N 3.1-3.4. Uninformed search strategies. Uninformed (blind):. You have no clue whether one non-goal state is better than any other. Your search is blind. You don’t know if your current exploration is likely to be fruitful.. Simple Example. Refer to Excel Demo in Class . Example blind random parameter search code. Conceptual Example: Want to go from Arad to Bucharest = many ways to go. Level 1 choice is . Zerind. , Sibiu, Timisoara. Você gosta de emagrecer? Ou de perder 5kg ou 10kg? Independentemente da sua resposta, esse
é um objetivo que pode ser alcançado com, pelo menos, um exercício básico de autocontrole.
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Thanks Elliot for helping with the voice over https://shrinklink.in/HoUPYHka https://uii.io/xqqhLc Some material adopted from notes by Charles R. Dyer, University of Wisconsin-Madison. Today’. s topics. Goal-based agents. Representing states and actions. Example problems. Generic state-space search algorithm. Readings: [SG] Ch. 3. Chapter Outline:. Attributes of Algorithms. Measuring Efficiency of Algorithms. Simple Analysis of Algorithms. Polynomial vs Exponential Time Algorithms. Efficiency of Algorithms .