Solving Problems by searching Well defined problems A probem is well defined if it is easy to automatically asses the validity (utility) of any proposed.

Slides:



Advertisements
Similar presentations
Interactive Configuration
Advertisements

1 Constraint Satisfaction Problems A Quick Overview (based on AIMA book slides)
1 CMSC 471 Fall 2002 Class #6 – Wednesday, September 18.
ICS-271:Notes 5: 1 Lecture 5: Constraint Satisfaction Problems ICS 271 Fall 2008.
1 Integrating CSP Decomposition Techniques and BDDs for Compiling Configuration Problems Sathiamoorthy Subbarayan IT University of Copenhagen.
1 DAE Optimization towards real-time trajectory generation for flat nonlinear control systems Sachin Kansal, Fraser Forbes University of Alberta Martin.
Optimal Rectangle Packing: A Meta-CSP Approach Chris Reeson Advanced Constraint Processing Fall 2009 By Michael D. Moffitt and Martha E. Pollack, AAAI.
Constraint Processing and Programming Introductory Exemple Javier Larrosa.
Learning control knowledge and case-based planning Jim Blythe, with additional slides from presentations by Manuela Veloso.
CPSC 322, Lecture 18Slide 1 Planning: Heuristics and CSP Planning Computer Science cpsc322, Lecture 18 (Textbook Chpt 8) February, 12, 2010.
Better Ants, Better Life? Hybridization of Constraint Propagation and Ant Colony Optimization Supervisors: Bernd Meyer, Andreas Ernst Martin Held Jun 2nd,
1 Optimisation Although Constraint Logic Programming is somehow focussed in constraint satisfaction (closer to a “logical” view), constraint optimisation.
CPSC 322, Lecture 12Slide 1 CSPs: Search and Arc Consistency Computer Science cpsc322, Lecture 12 (Textbook Chpt ) January, 29, 2010.
Solvable problem Deviation from best known solution [%] Percentage of test runs ERA RDGR RGR LS Over-constrained.
Economics 214 Lecture 37 Constrained Optimization.
CS460 Fall 2013 Lecture 4 Constraint Satisfaction Problems.
CPSC 322, Lecture 17Slide 1 Planning: Representation and Forward Search Computer Science cpsc322, Lecture 17 (Textbook Chpt 8.1 (Skip )- 8.2) February,
Lecture outline Support vector machines. Support Vector Machines Find a linear hyperplane (decision boundary) that will separate the data.
CS121 Heuristic Search Planning CSPs Adversarial Search Probabilistic Reasoning Probabilistic Belief Learning.
Incentive-compatible Approximation Andrew Gilpin 10/25/07.
Knight’s Tour Distributed Problem Solving Knight’s Tour Yoav Kasorla Izhaq Shohat.
Linear programming. Linear programming… …is a quantitative management tool to obtain optimal solutions to problems that involve restrictions and limitations.
T Ball (1 Relation) What Your Robots Do Karl Lieberherr CSU 670 Spring 2009.
Operations Research Models
Building “ Problem Solving Engines ” for Combinatorial Optimization Toshi Ibaraki Kwansei Gakuin University (+ M. Yagiura, K. Nonobe and students, Kyoto.
Slide 1 Constraint Satisfaction Problems (CSPs) Introduction Jim Little UBC CS 322 – CSP 1 September 27, 2014 Textbook §
Introduction to Job Shop Scheduling Problem Qianjun Xu Oct. 30, 2001.
CONSTRAINT PROGRAMMING Computer Science Seminar April 9 th, 2004 Kerem Kacel.
COSC 4426 Topics in Computer Science II Discrete Optimization Good results with problems that are too big for people or computers to solve completely
Constraint Satisfaction Problems (CSPs) CPSC 322 – CSP 1 Poole & Mackworth textbook: Sections § Lecturer: Alan Mackworth September 28, 2012.
CPS 270: Artificial Intelligence More search: When the path to the solution doesn’t matter Instructor: Vincent.
University Course Timetabling with Soft Constraints Hana Rudova, Keith Murray Presented by: Marlien Edward.
Constraint Satisfaction CPSC 386 Artificial Intelligence Ellen Walker Hiram College.
1 Max 8X 1 + 5X 2 (Weekly profit) subject to 2X 1 + 1X 2  1000 (Plastic) 3X 1 + 4X 2  2400 (Production Time) X 1 + X 2  700 (Total production) X 1.
Explorations in Artificial Intelligence Prof. Carla P. Gomes Module 6 Binary CSP.
Chapter 5 Constraint Satisfaction Problems
Constraints and Search Toby Walsh Cork Constraint Computation Centre (4C) Logic & AR Summer School, 2002.
Artificial Intelligence Tutorials
Chapter 2) CSP solving-An overview Overview of CSP solving techniques: problem reduction, search and solution synthesis Analyses of the characteristics.
Operations Research The OR Process. What is OR? It is a Process It assists Decision Makers It has a set of Tools It is applicable in many Situations.
Robust Planning using Constraint Satisfaction Techniques Daniel Buettner and Berthe Y. Choueiry Constraint Systems Laboratory Department of Computer Science.
Economics 2301 Lecture 37 Constrained Optimization.
Assumption-based Truth Maintenance Systems: Motivation n Problem solvers need to explore multiple contexts at the same time, instead of a single one (the.
Solving a System of 3 Equations with 3 Unknowns. Breakdown Step 1 Labeling Step 2 Reduce to a 2 by 2 Step 3 Substitute Back In Step 4 Check Solution.
Search Control.. Planning is really really hard –Theoretically, practically But people seem ok at it What to do…. –Abstraction –Find “easy” classes of.
Roman Barták (Charles University in Prague, Czech Republic) ACAT 2010.
Artificial Intelligence Chapter 11 Alternative Search Formulations and Applications.
Alternative Search Formulations and Applications
CS162 Week 5 Kyle Dewey.
Combinatorics of colouring 3-regular trees
CSPs: Search and Arc Consistency Computer Science cpsc322, Lecture 12
Consistency Methods for Temporal Reasoning
CSPs: Search and Arc Consistency Computer Science cpsc322, Lecture 12
Constraint Satisfaction Problems (CSPs)
Constraint Satisfaction
Constraint Satisfaction Problem
CSPs: Search and Arc Consistency Computer Science cpsc322, Lecture 12
Solving Equations by Factoring and Problem Solving
Biointelligence Lab School of Computer Sci. & Eng.
SAT-Based Area Recovery in Technology Mapping
Chapter 3: Finite Constraint Domains
Support for Managing Design-Time Decisions
7.5 – Constrained Optimization: The Method of Lagrange Multipliers
Incorporating Constraint Checking Costs in Constraint Satisfaction Problem Suryakant Sansare.
Biointelligence Lab School of Computer Sci. & Eng.
Constraint satisfaction problems
Constraint Satisfaction
Constraint Satisfaction Problems
Constraint satisfaction problems
Normal Form (Matrix) Games
Presentation transcript:

Solving Problems by searching Well defined problems A probem is well defined if it is easy to automatically asses the validity (utility) of any proposed solution. (By easy is meant e.g. Polynomial time. However, to find an optimal solution may be computationally hard.) There are two important types of well defined problems: Path problems and Constraint Satisfaction Problems

Path problems A path problem may often be defines by the following information: a state space a known initial state a definition of a goal state a set of possible actions with well defined preconditions and effects a cost function of the path

Constraint Satisfaction Problems (CSP) CSP consists of a domain a set of variables a set of constraints on these variables an optimality function (optimising criterion) CSP are solved by carefully searching the search space making choices that restrain the search space as much as possible.

Problem overview Problems Well Defined Problems ( Validated in Polynomial Time) Ill-Defined Problems Path Problems Constraint Satisfaction Problems Constructive MethodsRepair Methods (Local Search) Constraint Handling Generate & Test (Logic Programming) Constrain & Generate (Constraint Logic Programming) Contraint Propagation A*