CS B551: Elements of Artificial Intelligence Instructor: Kris Hauser http://cs.indiana.edu/~hauserk Announcements HW1 solutions out Project proposal template available on website Proposal and HW2 due on 10/6 Topics Constraint Satisfaction Problems (CSPs) Constraint Propagation

ByConstraint Satisfaction Problems. Russell and Norvig: Chapter 5 CMSC 421 – Fall 2006. Outline. Constraint Satisfaction Problems (CSPs) Backtracking for CSP Local search for CSPs Problem structure and decomposition. Exercise #2: Sudoku. But, before we get into all that, let’s do a puzzle…

BySolving problems by searching. Chapter 3. Why Search?. To achieve goals or to maximize our utility we need to predict what the result of our actions in the future will be. There are many sequences of actions, each with their own utility. We want to find, or search for, the best one.

ByConstraint Satisfaction Problems. Instructor: Kris Hauser http://cs.indiana.edu/~hauserk. Constraint Propagation. Place a queen in a square Remove the attacked squares from future consideration. Constraint Propagation. 5 5 5 5 5 6 7.

ByArtificial Intelligence. CS482, CS682, MW 1 – 2:15, SEM 201, MS 227 Prerequisites: 302, 365 Instructor: Sushil Louis, sushil@cse.unr.edu , http://www.cse.unr.edu/~sushil. Questions . Rational agents and performance metrics

BySolving problems by searching. Chapter 3. Outline. Problem-solving agents Problem types Problem formulation Example problems Basic search algorithms. Example: vacuum world. Single-state , start in #5. Solution?. Example: vacuum world.

ByConstraint Satisfaction. satisfies additional structural properties of the problem may depend on the representation of the problem the problem is defined through a set of variables and a set of domains variables can have possible values specified by the problem

ByTwo types of search problems. Rubik’s Cube. N-Queens Problem. No specific start state Goal state is unknown (only have goal test) Solution path does not matter. Start state is given Goal state is known ahead of time Solution path matters. Local search algorithms.

ByComputer Science CPSC 502 Lecture 3 Constraint Satisfaction Problems (Ch. 4 ). Lecture Overview. Finish Search Constraint Satisfaction Problems Variables/Features Constraints CSPs Generate-and-Test Search Arc Consistency. Course Overview. Representation. Reasoning Technique.

BySOLVING PROBLEMS BY SEARCHING. by Gülce HANER. Outline. Problem- solving agents Example problems (Toy problems & Real world problems ) Searching for solutions Uninformed search strategies Avoiding repeated states Searching with partial information. 3.1 PROBLEM SOLVING AGENTS.

ByConstraint Satisfaction Problems. Chapter 6. Outline. Constraint Satisfaction Problems (CSP) Backtracking search for CSPs Local search for CSPs. Constraint satisfaction problems (CSPs). Standard search problem:

ByCS 4700: Foundations of Artificial Intelligence. Carla P. Gomes gomes@cs.cornell.edu Module: CSP1 (Reading R&N: Chapter 5). Outline. Constraint Satisfaction Problems (CSP) Backtracking search for CSPs. Motivational Example: 8-Queens. Goal: place 8 non-attacking Queens. .

BySearching for Solutions. Traversal of the search space from the initial state to a goal state legal sequence of actions as defined by successor function (operators) General procedure check for goal state expand the current state the fringe (frontier) is the set of nodes not yet visited

BySolving problems by searching. Chapter 3. Example: The 8-puzzle. states? actions? goal test? path cost?. Example: The 8-puzzle. states? locations of tiles actions? move blank left, right, up, down goal test? = goal state (given) path cost? 1 per move

ByConstraint Satisfaction Problems. Russell and Norvig: Chapter 3, Section 3.7 Chapter 4, Pages 104-105 Slides adapted from: robotics.stanford.edu/~latombe/cs121/2003/home.htm. Intro Example: 8-Queens. Generate-and-test, with no redundancies “only” 8 8 combinations.

ByBasic search methods. 2012/03/6. Outline. Problem-solving agents A kind of goal-based agent Problem types Problem formulation Basic search algorithms BFS, DFS…. Problem-solving agent. Four general steps in problem solving: Goal formulation What are the successful world states

ByCSCE 580 Artificial Intelligence Ch.3: Uninformed (Blind) Search. Fall 2008 Marco Valtorta mgv@cse.sc.edu. Acknowledgment. The slides are based on the textbook [AIMA] and other sources, including other fine textbooks The other textbooks I considered are:

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.

BySolving problems by searching. Chapter 3. Why Search?. To achieve goals or to maximize our utility we need to predict what the result of our actions in the future will be. There are many sequences of actions, each with their own utility. We want to find, or search for, the best one.

BySolving problems by searching. Chapter 3. Outline. Problem types Problem formulation Example problems Basic search algorithms. Example: Romania. On holiday in Romania; currently in Arad. Flight leaves tomorrow from Bucharest. Example: Romania. On holiday in Romania; currently in Arad.

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