Home | Call for Papers | Programme Committee | Dates | Submission | Local Info |
Keynote Speakers | Accepted Papers | Schedule
Unfortunately Martijn van Otterlo will be unable to speak due to circumstances beyond his control.
We are, however, pleased to announce that Lorenza Saitta from Università del Piemonte Orientale has accepted our invitation and will replace Martijn in the schedule. Lorenza has an upcoming book on the topic of abstraction in Artificial Intelligence and Complex Systems. Details of her talk are below.
Our other keynote speaker is Chris Beck from the University of Toronto. Chris Beck is a joint speaker with SoCS 2013.
Unlike mathematical programming and SAT solving, Constraint Programming (CP) is based on the idea that both modeling and solving of combinatorial optimization problems can be based on conjunctions of loosely coupled, recurring, combinatorial sub-problems (aka "global constraints"). This rich representational approach means that, for better or for worse, pretty much anything can be expressed as a global constraint. Much of CP's success, however, has come from exploiting only one aspect of the rich constraint definition: global constraint propagation. In this talk, I will investigate how work in CP, SAT, AI planning, and mathematical programming can be understood as more seriously pursuing the implications of a rich constraint definition and how the interplay between local and global information can lead to dynamic problem reformulations and a flexible hybrid solver architecture.
Abstraction is a pervasive activity in human perception, conceptualization and reasoning; it enters the vocabulary of almost all disciplines, both scientific and humanistic, as well as everyday life. No wonder, then, that providing a definition of abstraction, at the same time precise and general, has been, up to now, unsuccessful. The complexity of abstraction can be clearly understood by comparing the (formal or informal) alternative notions proposed along the centuries in many disciplines, such as Philosophy, Computer Science, Cognition, Perception, Art, and Mathematics.
Formal models of abstraction have been proposed as well, mostly in Artificial Intelligence. An overview and comparison of those models let a characterization of abstraction emerge, allowing precise boundaries to be set between abstraction and cognate notions, such as generalization, approximation and reformulation. To this aim we exploit an approach based on the notions of information and information state space, where abstraction corresponds to a process of information reduction.
Abstraction is not only a conceptually interesting notion, but it has also universal applicability. We concentrate, for the purpose of illustrating its power, on the fields of Complex Systems and Machine Learning.
We conclude with the description of some novel problems, where abstraction has not yet played a role, but it will.