Verifying UML/OCL operation contracts

In current model-driven development approaches, software models are the primary artifacts of the development process. Therefore, assessment of their correctness is a key issue to ensure the quality of the final application. Research on model consistency has focused mostly on the models' static aspects. Instead, this paper addresses the verification of their dynamic aspects, expressed as a set of operations defined by means of pre/postcondition contracts. This paper presents an automatic method based on Constraint Programming to verify UML models extended with OCL constraints and operation contracts. In our approach, both static and dynamic aspects are translated into a Constraint Satisfaction Problem. Then, compliance of the operations with respect to several correctness properties such as operation executability or determinism are formally verified

Developmental Learning: A Case Study in Understanding “Object Permanence”

The concepts of muddy environment and muddy tasks set the ground for us to understand the essence of intelligence, both artificial and natural, which further motivates the need of Developmental Learning for machines. In this paper, a biologically inspired computational model is proposed to study one of the fundamental and controversial issues in cognitive science – “Object Permanence.” This model is implemented on a robot, which enables us to examine the robot’s behavior based on perceptual development through realtime experiences. Our experimental result shows consistency with prior researches on human infants, which not only sheds light on the highly controversial issue of object permanence, but also demonstrates how biologically inspired developmental models can potentially develop intelligent machines and verify computationalmodeling that has been established in cognitive science