Detecting Types of Variables for Generalization in Constraint Acquisition
- Resource Type
- Conference
- Authors
- Daoudi, Abderrazak; Lazaar, Nadjib; Mechqrane, Younes; Bessiere, Christian; Bouyakhf, El Houssine
- Source
- 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI) Tools with Artificial Intelligence (ICTAI), 2015 IEEE 27th International Conference on. :413-420 Nov, 2015
- Subject
- Computing and Processing
Yttrium
Reactive power
Classification algorithms
Vocabulary
Programming
Image edge detection
Data structures
Constraint Acquisition
Generalization Queries
Graph Community Detection
- Language
- ISSN
- 1082-3409
During the last decade several constraint acquisition systems have been proposed for assisting non-expert users in building constraint programming models. GENACQ is an algorithm based on generalization queries that can be plugged into many constraint acquisition systems. However, generalization queries require the aggregation of variables into types which is not always a simple task for non-expert users. In this paper, we propose a new algorithm that is able to learn types during the constraint acquisition process. The idea is to infer potential types by analyzing the structure of the current constraint network and to use the extracted types to ask generalization queries. Our approach gives good results although no knowledge on the types is provided.