"The Crossword puzzle (CP) is a simple problem to illustrate the formalization process of a problem into a CSP. The problem is to place words of a dictionary in a given structure satisfying certain constraints. The variables are the rows and columns in the crossword, and their values are the words in a dictionary."
– Marc Torrens. An Application using the JCL: The Air Travel Planning System. Diploma Thesis, 1997, Chapter 1, Section 1.2.1.
There are many combinatorial problems in artificial intelligence, computer hardware design, production scheduling, timetabling, and product configuration that can be formulated and solved using boolean satisfiability (SAT) or constraint programming (CP). Over the past 10-15 years significant advances have been made in terms of the scalability of these problem solving approaches to the point where we can now solve instances with millions of variables. The'no free lunch' theorem tells us that there is no one best method for solving combinatorial problems. This opens the door for the application of machine learning techniques to improve the use of SAT and CP methods.
However, the selection of test cases in regression testing is challenging as the time available for testing is limited and some selection criteria must be respected. This problem, coined as Test Suite Reduction (TSR), is usually addressed by validation engineers through manual analysis or by using approximation techniques. By associating each test case a cost-value aggregating distinct criteria, such as execution time, priority or importance due to the error-proneness of each test case, we propose several constraint optimization models to find a subset of test cases covering all the test requirements and optimizing the overall cost of selected test cases. Our overall goal is to develop a constraint-based approach of test suite reduction that can be deployed to test a complete product line of conferencing systems in continuous delivery mode.
The paper describes a simple heuristic approach to solving large-scale constraint satisfaction and scheduling problems. The search can be guided by a value-ordering heuristic, the min-conflicts heuristic, that attempts to minimize the number of constraint violations after each step. We demonstrate empirically that on the n-queens problem, a technique based on this approach performs orders of magnitude better than traditional backtracking techniques. We also describe a scheduling application where the approach has been used successfully.
In the experimental part of the thesis (Chapters 3-5), we present a set of magnitude estimation experiments investigating gradience in grammar. First, they demonstrate that an experimental investigation of gradient phenomena can advance linguistic theory by uncovering acceptability distinctions that have gone unnoticed in the theoretical literature. We propose an extension, Linear Optimality Theory, motivated by our experimental results on constraint ranking and the cumulativity of violations. On a theoretical level, our modeling results show that certain properties of gradient data (the hard/soft distinction, context effects, and crosslinguistic effects) do not have to be stipulated, but follow from core assumptions of Linear Optimality Theory.
The 22nd most cited computer science publication on Citeseer (and 4th most cited publication of this century). What's New Free Online AI course, Berkeley's CS 188, offered through edX. Free Online AI course, Berkeley's CS 188, offered through edX. Free Online AI course, Berkeley's CS 188, offered through edX.
The Traveling Salesman Problem is one of the most intensively studied problems in computational mathematics. These pages are devoted to the history, applications, and current research of this challenge of finding the shortest route visiting each member of a collection of locations and returning to your starting point.
Welcome to the On-Line Guide to CONSTRAINT PROGRAMMING designed and maintained by Roman Barták. I have opened this site as an on-line tutorial or, if you want, a textbook for beginners to the area of constraint programming. This area belongs to the less known software technologies but it rapidly evolves and brings a significant commercial interest.
Description: Constraint satisfaction problems (CSPs) are pervasive in AI problems. A constraint satisfaction problem is the problem of assigning values to variables that satisfy some constraints. This constraint satisfaction problem solver (arc consistency) tool is designed to help you learn about solving CSPs with a systematic search technique called arc consistency.
A committee is permitted to organise a hearing with experts, where this is considered essential to its work on a particular subject. Hearings can also be held jointly by two or more committees. Most committees organise regular hearings, as they allow them to hear from experts and hold discussions on the key issues. On this page you will find all the available information relating to committee hearings, including programmes and contributions from speakers.
Provides readable, inductive definitions and offers a unified framework using Getzen systems. This textbook, aimed at junior to senior undergraduate students and first-year graduate students, presents artificial intelligence (AI) using a coherent framework to study the design of intelligent computational agents. This book has been written for both professional programmers and home hobbyists who already know how to program in Java and who want to learn practical AI programming techniques. Provides readable, inductive definitions and offers a unified framework using Getzen systems.