Incremental learning is an efficient technique for knowledge discovery in a dynamic database, which enables acquiring additional knowledge from new data without forgetting prior knowledge. Rough set theory has been successfully used in information systems for classification analysis. Set-valued information systems are generalized models of single-valued information systems, which can be classif...

This paper synthesizes general constraint satisfaction and classical AI planning into a theory of incremental change that accounts for multiple objectives and contingencies. The hypothesis is that this is a new and useful paradigm for problem solving and re-solving. A truth maintenance-based architecture derived from the theory is useful for contingent assignment problems such as logistics plan...

Implicit theories drastically affect an individual's processing of social information, decision making, and action. The present research focuses on whether individuals who hold the implicit belief that people's moral character is fixed (entity theorists) and individuals who hold the implicit belief that people's moral character is malleable (incremental theorists) make different choices when fa...

One of the main challenges in the design of modern clustering algorithms is that, in many applications, new data sets are continuously added into an already huge database. As a result, it is impractical to carry out data clustering from scratch whenever there are new data instances added into the database. One way to tackle this challenge is to incorporate a clustering algorithm that operates i...

Aim: To investigate the role of implicit theories of ability and achievement goals on self-handicapping strategies in physical education classes. Hypotheses: It was expected that incremental theories of ability would be negatively associated with self-handicapping strategies, whereas fixed theories of ability would enhance pupils’ self-reported use of such strategies. It was also hypothesised t...