First Call for Papers The second annual workshop on Agents Learning Interactively from Human Teachers (ALIHT)
A two-day workshop at IJCAI-11 in Barcelona, Spain. http://www.cs.utexas.edu/~bradknox/IJCAI-ALIHT11/Home.html *************** Important dates *************** Paper submission deadline: April 6, 2011 Acceptance notification: May 1, 2011 Preliminary program online: May 6, 2011 Camera ready deadline: May 14, 2011 ALIHT workshop at IJCAI-11: 2 days within July 16-18, 2011 *********** Description *********** As intelligent robotic and software agents populate everyday human environments, they will need the ability to adapt. For such agents, human teachers present a narrow but critical learning environment. Research in interactive learning seeks to enable agents to learn from human instruction, harnessing human expertise to learn tasks and customizing behavior to match human preferences. Machines cannot be imbued by their designers with all of the knowledge and skills that they will need to serve useful, long-term roles in our dynamic world. If humans and machines are to work together in challenging environments, then machines must be able to learn quickly from any knowledgeable human, not just expert programmers. The environments in which humans act are highly diverse and constantly changing --- whether at home, in the office, or out in the field. Even a machine with exhaustive background knowledge must be customized for the particular environment in which it acts. Between humans, the interactive student-teacher framework is known to be effective and, furthermore, is familiar to every human; even young children teach one another games and novel skills. Endowing machines with human-like learning capabilities allows humans to teach such machines as they would teach other humans and thus exploits skills that humans already possess. Another strength of an interactive learning approach is in the generality of the student-teacher mechanism; namely, that it might be applied to a wide range of tasks with minimal, ideally no, modification or specialization. Research in interactive learning encompasses a wide range of approaches and is referenced under many names, including teachable agents, imitation, learning from demonstration, active learning, interactive shaping, and bootstrapped learning. This workshop aims to advance research in interactive learning by bringing together researchers with different approaches and attempting to establish standards for comparing systems, evaluating their performance, combining results from different aspects of the problem, and creating an organizing structure to situate the body of work within. The potential application domains for interactive learning are many --- both for autonomous robots and for virtual agents --- and range across a spectrum of abstractness from fine motor skills to deep, abstract knowledge such as that acquired at a university. An interactive learning system therefore may be formulated in a variety of ways, and we encourage a broad spectrum of applications. In particular, we welcome contributions from both the robotic and software agents communities. One of our main goals is to bridge this divide, to create a single community of research on interactive learning agents. *********** Submissions *********** We invite short and long length papers, reviews, and position papers. Submissions will be judged on technical merit, the potential to generate discussion, and their ability to foster collaboration within the community. The maximum length is 6 pages and papers should follow IJCAI formatting guidelines. Questions should be directed to Brad: bradknox-at-cs.utexas.edu ********* Co-Chairs ********* W. Bradley Knox (University of Texas at Austin) Jacob Beal (Raytheon BBN Technologies) ******************** Organizing Committee ******************** Brenna Argall (Ecole Polytechnique Federale de Lausanne, EPFL) Sonia Chernova (Worchester Polytechnic Institute) Peter Stone (University of Texas at Austin) Matthew E. Taylor (Lafayette College) Andrea Thomaz (Georgia Tech) _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
