Department of Mechanical & Industrial Engineering, Faculty of Applied Science & Engineering, University of Toronto, Canada
Planning is one of the traditional problems studied in artificial intelligence (AI) because the ability to decide on a set of actions to execute to achieve a goal is an important component of intelligent behaviour. This problem has not traditionally been studied in Operations Research (OR) and, indeed, "planning" refers to a very different problem in OR. However, there is an increasing number of examples of AI planning researchers adopting linear programming and mixed integer linear programming to solve AI planning problems. In this talk, I will introduce the AI planning problem, discuss standard solution techniques, and present examples of three ways that linear and mixed integer linear programming have been used for AI planning: as an alternative approach to solve the planning problem; as an addition to a planning algorithm to deal with numeric resources and constraints; and as a technique to compute better search guidance heuristics within a planning algorithm.
Chris Beck is an Associate Professor and Associate Chair, Research in the Department of Mechanical & Industrial Engineering, University of Toronto. Chris' MSc and PhD degrees both come for the Department of Computer Science, University of Toronto, in the area of Artificial Intelligence. His PhD dissertation studied search heuristics for constraint-directed scheduling. Chris then spent three years at ILOG as a Senior Scientist and Software Engineer on the ILOG Scheduler team before two years as a Staff Scientist at the Cork Constraint Computation Centre. He returned to Toronto in 2004 to join the Department of Mechanical & Industrial Engineering. Chris' research interests include scheduling, constraint programming, hybrid optimization techniques, mixed integer programming, AI planning, reasoning under uncertainty, and queueing theory. Chris was formerly the Letters editor at the Constraints journal and an Associate Editor for the Journal of Scheduling. He is currently an Associate Editor for the Journal of Artificial Intelligence Research and a member of the editorial board of The Knowledge Engineering Review. He is the President-Elect of the Executive Council for the International Conference on Automated Planning and Scheduling and will take on the role of President in June 2014.
Deputy Director, Sony CSL, Paris, France
Markov chains are routinely used in many fields of analysis. They are increasingly used also for generation of content such as music or text, because they faithfully represent local properties of the style of a given corpus. However, in practice, many types of constraints are needed to enforce structural properties of sequences. These properties cannot be learned by Markov processes, by definition of the Markov hypothesis. In this talk I introduce Markov Constraints, a reformulation of Markov sequence generation in the framework of constraint satisfaction that makes it possible to generate Markov sequences enforcing various structural properties. I present some results concerning unary and binary constraints as well as meter, and describe current challenges concerning, the control of plagiarism (max order) and the distribution of sequences. I illustrate these issues with applications in music and text generation.
Francois Pachet received his Ph.D. and Habilitation degrees from Paris 6 University (UPMC). He is a Civil Engineer (Ecole des Ponts and Chaussées) and was Assistant Professor in Artiﬁcial Intelligence and Computer Science, at Paris 6 University, until 1997. He is now director of SONY Computer Science Laboratory Paris, where he leads the music research team. The music team conducts research on interactive music listening, composition and performance. Since its creation, the team developed several award winning technologies (constraint-based spatialization, intelligent music scheduling using metadata) and systems (MusicSpace, PathBuilder, Continuator for interactive music improvisation, etc.). His current goal is to create a new generation of authoring tools able to boost individual creativity. These tools, called Flow Machines, abstract "style" from concrete corpora (text, music, etc.), and turn it into a malleable substance that acts as a texture. Applications range from music composition to text or drawing generation and probably much more.
CEO, COSYTEC SA, Orsay, France
In this talk we will present our experience of 20+ years of industrial deployment of Constraint Programming (CP) technology with COSYTEC. COSYTEC (Complex Systems Technologies) was founded in 1990 in order to bring the CHIP system, first developed at ECRC (European Computer-Industry Research Center) in Munich, into the industrial market. COSYTEC develops innovative software for solving complex business problems in Resource Management & Optimization, especially in Planning and Scheduling applications. We will present different areas where these applications are used, as well as different business sectors where they are deployed. We will talk about the benefits of CP for this kind of applications but also some issues encountered in the design, development and deployment within an operational environment, due to the specific requirements for this kind of applications in an industrial context.
Dr. Mehmet Dincbas is the founder, President and CEO of COSYTEC. He holds a PhD. degree in Computer Science and Artificial Intelligence. After working at the research centers of French Ministry of Defense (Office National d’Etudes et de Recherches Aerospatiales) and France Telecom (Centre National d’Etudes de Telecommunications), he moved to the European Computer-Industry Research Center in Munich/Germany where he was the initiator and leader of the CHIP project. COSYTEC was founded in order to bring this innovative Constraint Logic Programming technology to the industrial market.