PPT-Defining the problem + Uninformed search

CSM6120 Introduction to Intelligent Systems Groups Topics Philosophical issues Neural Networks Genetic Algorithms Bayesian Networks Knowledge Representation semantic networks fuzzy sets rough sets etc. 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 . Rafa. . Rahman. USAID Global Health, Center for Accelerating Innovation and Impact (CII). USAID HESN, MIT Comprehensive Initiative on Technology Evaluation (CITE). Rapid Diagnostic Tests (RDTs) . for Malaria. Problem-solving agents. Example: Romania. On holiday in Romania; currently in Arad. .. Flight leaves tomorrow from . Bucharest. What do we need to define?. Problem Formulation. The process of defining actions, states and goal.. This Lecture. Read Chapter 3.1-3.4. Next Lecture. Read Chapter 3.5-3.7. (Please read lecture topic material before and after each lecture on that topic). You will be expected to know. Overview of uninformed search methods. 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. 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. 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. (Section 3.4). Source: . Fotolia. Uninformed search strategies. A . search strategy . is defined by picking the order of node . expansion. Uninformed. . search strategies use only the information available in the problem . Models To Be Studied in CS 540. State-based Models. Model task as a graph of all possible states. Called a “. state-space graph. ”. A state captures all the relevant information about the past in order to act (optimally) in the future. 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.. 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.