Ebook: Constraint Reasoning for Differential Models

I am specially indebted to Pedro Barahona who supported me, not only as the supervisor of this work, but also in all my academic formation. He is an excellent supervisor which I greatly recommend to any one intending to start a PhD in constraint programming. He has the rare ability of being open minded with respect to new ideas and contribute diligently to their success. Despite his overbooked schedule, he was always present whenever needed. He is a good friend and is a pleasure to work with him.

I am very grateful to Frédéric Benhamou and all his research team for their good hospitality and their crucial support in the early phases of this work. Thanks to Frédéric Goualard and Laurent Granvilliers for their friendship and all our valuable discussions.

Special thanks to Luís Moniz Pereira, for his careful reading and comments on the introduction and conclusions of this work, but above all, for his excellent work in promoting Artificial Intelligence which attracted me in the first place for taking a computer science degree.

My acknowledgements to the Computer Science Department for having conceded me three sabbatical years which were extremely important for the progress of this work.

Many thanks to my colleagues in the Computer Science Department and CENTRIA for their companionship, trust and concern. In particular, I am grateful to the constraint research group for creating a stimulating working environment. Thanks to my good friends Anabela and Cecília for their sincere care and help, promptly sharing with me their own resources whenever I needed.

Thanks to all my family for their love and precious help in handling so many practical problems which allowed me to fully concentrate on my work. I am particularly grateful to my parents for their continuous support and belief on my own choices.

Finally, I am deeply grateful to my wife Teresa, who, from the beginning of this work, has always been by my side, giving me confidence and encouragement in difficult moments and a good reason to keep on searching for success. I can never thank her enough for all the days, weekends and holidays that she has sacrificed for helping me.

The basic motivation of this work was the integration of biophysical models within the interval constraints framework for decision support. Comparing the major features of biophysical models with the expressive power of the existing interval constraints framework, it was clear that the most important inadequacy was related with the representation of differential equations. System dynamics is often modelled through differential equations but there was no way of expressing a differential equation as a constraint and integrate it within the constraints framework.

Consequently, the goal of this work is focussed on the integration of ordinary differential equations within the interval constraints framework, which for this purpose is extended with the new formalism of Constraint Satisfaction Differential Problems. Such framework allows the specification of ordinary differential equations, together with related information, by means of constraints, and provides efficient propagation techniques for pruning the domains of their variables. This enabled the integration of all such information in a single constraint whose variables may subsequently be used in other constraints of the model. The specific method used for pruning its variable domains can then be combined with the pruning methods associated with the other constraints in an overall propagation algorithm for reducing the bounds of all model variables.

The application of the constraint propagation algorithm for pruning the variable domains, that is, the enforcement of local-consistency, turned out to be insufficient to support decision in practical problems that include differential equations. The domain pruning achieved is not, in general, sufficient to allow safe decisions and the main reason derives from the non-linearity of the differential equations. Consequently, a complementary goal of this work proposes a new strong consistency criterion, Global Hull-consistency, particularly suited to decision support with differential models, by presenting an adequate trade-of between domain pruning and computational effort. Several alternative algorithms are proposed for enforcing Global Hull-consistency and, due to their complexity, an effort was made to provide implementations able to supply any-time pruning results.

Since the consistency criterion is dependent on the existence of canonical solutions, it is proposed a local search approach that can be integrated with constraint propagation in continuous domains and, in particular, with the enforcing algorithms for anticipating the finding of canonical solutions.

The last goal of this work is the validation of the approach as an important contribution for the integration of biophysical models within decision support. Consequently, a prototype application that integrated all the proposed extensions to the interval constraints framework is developed and used for solving problems in different biophysical domains.