Concept drift refers to a non stationary learning problem over time. The training and the application data often mismatch in real life problems. In this report we present a context of concept drift problem 1. We focus on the issues relevant to adaptive training set formation. We present the framework and terminology, and formulate a global picture of concept drift learners design. We start with formalizing the framework for the concept drifting data in Section 1. In Section 2 we discuss the adaptivity mechanisms of the concept drift learners. In Section 3 we overview the principle mechanisms of concept drift learners. In this chapter we give a general picture of the available algorithms and categorize them based on their properties. Section 5 discusses the related research fields and Section 5 groups and presents major concept drift applications. This report is intended to give a bird's view of concept drift research field, provide a context of the research and position it within broad spectrum of research fields and applications.

There is growing body of learning problems for which it is natural to organize the parameters into matrix, so as to appropriately regularize the parameters under some matrix norm (in order to impose some more sophisticated prior knowledge). This work describes and analyzes a systematic method for constructing such matrix-based, regularization methods. In particular, we focus on how the underlying statistical properties of a given problem can help us decide which regularization function is appropriate. Our methodology is based on the known duality fact: that a function is strongly convex with respect to some norm if and only if its conjugate function is strongly smooth with respect to the dual norm. This result has already been found to be a key component in deriving and analyzing several learning algorithms. We demonstrate the potential of this framework by deriving novel generalization and regret bounds for multi-task learning, multi-class learning, and kernel learning.

Despite the recent progress towards efficient multiple kernel learning (MKL), the structured output case remains an open research front. Current approaches involve repeatedly solving a batch learning problem, which makes them inadequate for large scale scenarios. We propose a new family of online proximal algorithms for MKL (as well as for group-lasso and variants thereof), which overcomes that drawback. We show regret, convergence, and generalization bounds for the proposed method. Experiments on handwriting recognition and dependency parsing testify for the successfulness of the approach.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University’s Department of Computer Science, is pleased to present the 2010 Spring Symposium Series, to be held Monday through Wednesday, March 22–24, 2010 at Stanford University. The titles of the seven symposia are Artificial Intelligence for Development; Cognitive Shape Processing; Educational Robotics and Beyond: Design and Evaluation; Embedded Reasoning: Intelligence in Embedded Systems Intelligent Information Privacy Management; It’s All in the Timing: Representing and Reasoning about Time in Interactive Behavior; and Linked Data Meets Artificial Intelligence.

So, we have a variety of people here with different interests and backgrounds that I asked to talk about not just the key challenges ahead but potential opportunities and promising pathways, trajectories to solving those problems, and their predictions about how R&D might proceed in terms of the timing of various kinds of development over time. I asked the panelists briefly to frame their comments sharing a little bit about fundamental questions, such as, "What is the research goal?" Not everybody stays up late at night hunched over a computer or a simulation or a robotic system, pondering the foundations of intelligence and human-level AI. We have here today Lise Getoor from the University ipate the liability and insurance industry; and the of Maryland; Devika Subramanian, who other one, that it was a human interface problem, comes to us from Rice University; we have Carlos that people don't necessarily want to go and type Guestrin from Carnegie Mellon University (CMU); a bunch of yes/no questions into a computer to get James Hendler from Rensselaer Polytechnic Institute an answer, even with a rule-based explanation, (RPI); Mike Wellman at the University of that if you'd taken that just a step further and Michigan; Henry Kautz at tjhe University of solved the human problem, it might have worked. Rochester; and Joe Konstan, who comes to us from Related to that, I was remembering a bunch of the Midwest, as our Minneapolis person here on these smart house projects. And I have to admit I the panel. I think everyone Joe Konstan: I was actually surprised when you hates smart spaces. I think of myself at the core there's nobody there, do you warn people and give in human-computer interaction. So I went back them a chance to answer? There's no good answer and started looking at what I knew of artificial to this question. I can tell you if that person is in intelligence to try to see where the path forward bed asleep, the answer is no, don't wake them up was, and I was inspired by the past.

The Best Paper award went to Sidney D'Mello, Blair Lehman, and Natalie Person for "Expert Tutors' Feedback Is Immediate, Direct, and Discriminating" in the special track on Intelligent Tutoring Systems. The Best Student Paper award went to Rong Hu, Brian Mac Namee, and Sarah Jane Delany for "Off to a Good Start: Using Clustering to Select the Initial Training Set in Active Learning" in the general conference. The Best Poster award went to Robert Holder for "Problem Space Analysis for Library Generation and Algorithm Selection in Real-Time Systems" in the general conference. In addition to a diverse assortment of papers and British Columbia, who presented "What Should posters presented at the conference, FLAIRS-23 featured the World-Wide Mind Believe? Information about FLAIRS-24, University, who presented "Rational Ways of Talking"; including the call for papers, is available online at and Janet L. Kolodner of the Georgia Institute www.flairs-24.info. of Technology, who presented "How Can We Help Université de Paris-Sorbonne, who presented "Reasoning in Natural Language Using Combinatory Games"; and David Poole of the University of

AAAI/SIGART Doctoral Consortium, and the second AAAI Educational Advances in Artificial Intelligence Symposium, to name only a few of the AAAI is pleased to present the 2011 Spring Symposium Series, to highlights. For complete information be held Monday through Wednesday, March 21-23, 2011, at on these programs, including Tutorial Stanford University.

The Prom is a game where the player manages the social life of a group of high school students and creates the situations from which dramatic, thought provoking or at least funny stories can unfold. The setting of The Prom involves a group of alternative high school kids (e.g. Emos, Goths, Geeks, etc.) and their dramatic lives as they prepare for the upcoming school prom. Through creating friendships, making people become enemies, controlling who gets to be in the "in" crowd and much more, the player can shape the social world of the characters. Each character has a distinct personality represented by interests (e.g. what bands they like), needs (e.g. a character may need to demonstrate a certain degree of dominance over others), traits (e.g. being a particularly jealous person), social networks (e.g. to what degree a characters like, are attracted to or respect one another) and social status (e.g. who is dating who).The social artificial intelligence system Comme il Faut ( CiF ) drives this gameplay experience by simulating per character needs and traits, social statuses, social networks, social history and most importantly to gameplay, the outcomes and effects of social games. CiF is a playable computational model of social interactions designed specifically to allow autonomous characters to play social games. By giving player controls to navigate a social, rather than physical, space, The Prom is being created to demonstrate how CiF and social games can create a practically limitless numbers of possibly compelling stories and gameplay.

Recent years have seen a growing interest in interactive narrative systems that dynamically adapt story experiences in response to users’ actions, preferences, and goals. However, relatively little empirical work has investigated runtime models of user knowledge for informing interactive narrative adaptations. User knowledge about plot scenarios, story environments, and interaction strategies is critical in a range of interactive narrative contexts, such as mystery and detective genre stories, as well as narrative scenarios for education and training. This paper proposes a dynamic Bayesian network approach for modeling user knowledge in interactive narrative environments. A preliminary version of the model has been implemented for the Crystal Island interactive narrative-centered learning environment. Results from an initial empirical evaluation suggest several future directions for the design and evaluation of user knowledge models for guiding interactive narrative generation and adaptation.

Chris Jurney (Lead Programmer, Double Fine Productions) Sumit Basu (Microsoft Research) Chris Jurney is a rock and roll experimental game For those who can play an instrument or have a respectable programmer at Double Fine Productions, with 11 singing voice, music can be a wonderful years experience in games and simulation. He has means of creative expression, social engagement, shipped 4 titles in the games industry: Company of and fun. For many others, though, it can be frustrating Heroes, Frontline: Fuel of War, Dawn of War 2, and and inaccessible: even if an inspired youth Brutal Legend. Jurney frequently speaks on the topic has great musical ideas, she may not have the of game AI, having presented at the Game Developers knowledge or ability to get her latest song out from Conference (GDC), GDC China, Columbia her head and into her MP3 player. In this talk, Basu will show three vignettes of how he and his colleagues University, the University of Pennsylvania, and the have used interactive machine learning to New Jersey and Philadelphia chapters of the International extend the creative reach of aspiring musicians: a Game Developers Association (IGDA).