Keynote speakers

Michael Kifer
Stony Brook University
AIJ keynote speaker

Rule Interchange Format: Logic Programming's Second Wind?

Recent years have witnessed a strong upswing of interest in the rule systems technology in its own right and in combination with existing Web standards. In particular, the Semantic Web is now seen as a vast playing field for rules -- both by the academia and industry. This renewed interest motivated the development of the Rule Interchange Format (RIF), an upcoming W3C Web standard for exchanging rules among different and dissimilar systems. Despite its name, RIF is not merely a format: it is a collection of concrete rule languages, called RIF dialects, and a framework for defining new ones in harmony with each other. This includes formal specifications of the syntax, semantics, and XML serialization.

In this talk we argue that RIF is a major opportunity to re-introduce rule based technologies into the mainstream of knowledge representation and information processing, and to rekindle the interest in logic programming. First, we will introduce the main principles behind RIF and then discuss the application landscape that could emerge if this standard is embraced by the relevant communities: Logic Programming, Semantic Web, and Knowledge Representation. We will also reflect on the past of logic programming and speculate on how it could benefit from and contribute to RIF in the future.

Michael Kifer

Michael Kifer is a Professor with the Department of Computer Science, State University of New York at Stony Brook. His work spans the areas of Web information systems, knowledge representation, and databases. He has published four text books and numerous articles in these areas. His works on F-logic, HiLog, Annotated Logic, and Transaction Logic are among the most widely cited in Computer Science and, especially, in Semantic Web research. In recent years he had a major role in shaping W3C's Rule Interchange Format standard and was also a key player in Vulcan's SILK project. Twice, in 1999 and 2002, he was a recipient of the prestigious ACM-SIGMOD "Test of Time" awards for his works on F-logic and object-oriented database languages. In 2006, he was a Plumer Fellow at Oxford University's St. Anne's College and in 2008 he received SUNY Chancellor's and President's Award for Excellence in Scholarship.


Avi Pfeffer
Charles River Analytics, Inc.

Practical Probabilistic Programming

Probabilistic models are ever growing in richness and diversity. Creating a new probabilistic model is often a significant challenge, requiring defining the representation and implementing reasoning and learning algorithms. Probabilistic programming promises to make this task easier. By allowing models to be represented using the full power of programming languages, it makes it possible to conceive of and express new models in a structured way. By implementing general-purpose reasoning and learning algorithms, it allows algorithms to be derived automatically for new models. In this talk, I will present a new probabilistic programming language named Figaro that is designed with practicality and usability in mind. Most importantly, both the Figaro language and its reasoning algorithms are designed as modular, extensible libraries within Scala, a programming language that is interoperable with Java. In addition, Figaro can represent models cleanly and naturally that have been difficult to represent in other languages, such as object-based models like Probabilistic Relational Models, and models with undirected relationships with arbitrary constraints. I will illustrate the use of Figaro through a case study in which the goal is to reason about the capabilities and intentions of a decision-making agent.

Avi Pfeffer

Dr. Avi Pfeffer, Senior Scientist at Charles River Analytics, performs research on probabilistic reasoning, machine learning and computational game theory. As an Associate Professor at Harvard, he developed the IBAL and Figaro probabilistic programming languages, which allow the development of probabilistic models using the full power of programming languages. While at Harvard, he also developed methods for reasoning about probabilistic dynamic systems, and produced systems for representing, reasoning about and learning the beliefs, preferences and decision making strategies of people in strategic situations. Dr. Pfeffer completed his doctoral work at Stanford, where he co-invented object-oriented Bayesian networks and probabilistic relational models which help form the foundation of the field of statistical relational learning. Dr. Pfeffer serves as Associate Editor of Artificial Intelligence Journal, and has served on the program committees of numerous conferences. Dr. Pfeffer has received the Sloan Foundation Fellowship, the NSF CAREER Award, and the Arthur Samuel Thesis Award at Stanford.


David Poole
University of British Columbia
PASCAL2 keynote speaker

Probabilistic Relational Learning and Inductive Logic Programming at a Global Scale

This talk is about how to create knowledge at a global scale. The Semantic Web is being developed where semantically interoperable knowledge can be published on the web. This talk is about how to create knowledge, how to evaluate knowledge that has been published, and how to go beyond the sum of human knowledge. If there is some claim of truth, it is reasonable to ask what evidence there is for that claim, and to not believe claims that do not provide evidence. Thus we need to publish data that can provide evidence. Given such data, we can also learn from it. This talk outlines how publishing ontologies, data, and probabilistic hypotheses/theories can let us base beliefs on evidence, and how the resulting world-wide mind can go beyond the aggregation of human knowledge. Much of the world's data is relational, and we want to make probabilistic predictions in order to make rational decisions. Thus probabilistic relational learning and inductive logic programming need to be a foundation of the semantic web. This talk will overview the technology behind this vision and the considerable technical and social problem that remain.

David Poole

David Poole is a Professor of Computer Science at the University of British Columbia. He is known for his work on assumption-based reasoning, diagnosis, relational probabilistic models, combining logic and probability, algorithms for probabilistic inference, representations for automated decision making, probabilistic reasoning with ontologies and semantic science. He is a co-author of a new AI textbook, Artificial Intelligence: Foundations of Computational Agents (Cambridge University Press, 2010), co-author of an older AI textbook, Computational Intelligence: A Logical Approach (Oxford University Press, 1998), co-chair of AAAI-10 (twenty-Fourth AAAI Conference on Artificial Intelligence) and co-editor of the Proceedings of the Tenth Conference in Uncertainty in Artificial Intelligence (Morgan Kaufmann, 1994). He is a former associate editor of the Journal of AI research, is an associate editor of AI Journal and is on the editorial board of AI Magazine. He is the secretary of the Association for Uncertainty in Artificial Intelligence, is a Fellow of the Association for the Advancement Artificial Intelligence (AAAI) and has an entry in the International Directory of Logicians: Who's Who in Logic (2009).