Wolfram Burgard

Probabilistic approaches have been discovered as one of the most powerful approaches to highly relevant problems in mobile robotics including perception and robot state estimation. Major challenges in the context of probabilistic algorithms for mobile robot navigation lie in the questions of how to deal with highly complex state estimation problems and how to control the robot so that it efficiently carries out its task. In this talk, I will present recently developed techniques for efficiently learning a map of an unknown environment with a mobile robot. I will also describe how this state estimation problem can be solved more effectively by actively controlling the robot. For all algorithms I will present experimental results that have been obtained with mobile robots in real-world environments.

Adnan Darwiche

Decision diagrams have played an influential role in computer science and AI over the past few decades, with OBDDs (Ordered Binary Decision Diagrams) as perhaps the most practical and influential example. The practical influence of OBDDs is typically attributed to their canonicity, their efficient support of Boolean combination operations, and the availability of effective heuristics for finding good variable orders (which characterize OBDDs and their size). Over the past few decades, significant efforts have been exerted to generalize OBDDs, with the goal of defining more succinct representations while retaining the attractive properties of OBDDs. On the theoretical side, these efforts have yielded a rich set of decision diagram generalizations. Practially, however, OBDDs remain as the single most used decision diagram in applications. In this talk, I will discuss a recent line of research for generalizing OBDDs based on a new type of Boolean-function decompositions (which generalize the Shannon decomposition underlying OBDDs).

I will discuss in particular the class of Sentential Decision Diagrams (SDDs), which branch on arbitrary sentences instead of variables, and which are characterized by trees instead of total variable orders. SDDs retain the main attractive properties of OBDDs and include OBDDs as a special case. I will discuss recent theoretical and empirical results, and a soon-to-be-released open source package for supporting SDDs, which suggest a potential breakthrough in the quest for producing more practical generalizations of OBDDs.

Tom Mitchell

We will never really understand learning or intelligence until we can build machines that learn many different things, over years, and become better learners over time.

This talk describes our research to build a Never-Ending Language Learner (NELL) that runs 24 hours per day, forever, learning to read the web. Each day NELL extracts (reads) more facts from the web, and integrates these into its growing knowledge base of beliefs. Each day NELL also learns to read better than yesterday, enabling it to go back to the text it read yesterday, and extract more facts, more accurately. NELL has been running 24 hours/day for over two years now. The result so far is a collection of 15 million interconnected beliefs (e.g., servedWtih(coffee, applePie), isA(applePie, bakedGood)), that NELL is considering at different levels of confidence, along with hundreds of thousands of learned phrasings, morphoogical features, and web page structures that NELL uses to extract beliefs from the web. Track NELL's progress at http://rtw.ml.cmu.edu.

Michael Wooldridge

In economics, an equilibrium is a steady-state situation, which obtains because no participant has any rational incentive to deviate from it. Equilibrium concepts are arguably the most important and widely used analytical weapons in the game theory arsenal.
The concept of Nash equilibrium in particular has found a huge range of applications, in areas as diverse and seemingly unrelated as biology and moral philosophy. However, there remain fundamental problems associated with Nash equilibria and their application.
First, there may be multiple Nash equilibria, in which case, how should we choose between them?
Second, some equilibria may be undesirable, in which case, how can we avoid them?
In this presentation, I will introduce work that we have done addressing these problems from a computational/AI perspective. Assuming no prior knowledge of game theory or economic solution concepts, I will discuss various ways in which we can try to engineer a scenario so that desirable equilibria result, or else engineer out undesirable equilibria.