Treffer: Generating corrective explanations for interactive constraint satisfaction

Title:
Generating corrective explanations for interactive constraint satisfaction
Source:
Principles and practice of constraint programming - CP 2005 (11th international conference, CP 2005, Sitges, Spain, October 1-5, 2005, proceedings)Lecture notes in computer science. :445-459
Publisher Information:
New York, NY: Springer, 2005.
Publication Year:
2005
Physical Description:
print, 17 ref 1
Original Material:
INIST-CNRS
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Cork Constraint Computation Centre, Department of Computer Science, University College Cork, Ireland
ISSN:
0302-9743
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.17324992
Database:
PASCAL Archive

Weitere Informationen

Interactive tasks such as online configuration and e-commerce can be modelled as constraint satisfaction problems (CSPs). These can be solved interactively by a user assigning values to variables. The user may require advice and explanations from a system to help him/her find a satisfactory solution. Explanations of failure in constraint programming tend to focus on conflict. However, what is really desirable is an explanation that is corrective in the sense that it provides the basis for moving forward in the problem-solving process. More specifically, when faced with a dead-end, or when a desirable value has been removed from a domain, we need to compute alternative assignments for a subset of the assigned variables that enables the user to move forward. This paper defines this notion of corrective explanation, and proposes an algorithm to generate such explanations. The approach is shown to perform well on both real-world configuration benchmarks and randomly generated problems.