We report on a series of transfer learning experiments in game domains, in which we use structural analogy from one learned game to speed learning of another related game. We find that a major benefit of analogy is that it reduces the extent to which the source domain must be generalized before transfer. We describe two techniques in particular, minimal ascension and metamapping, that enable analogies to be drawn even when comparing descriptions using different relational vocabularies. Evidence for the effectiveness of these techniques is provided by a large-scale external evaluation, involving a substantial number of novel distant analogs.

The Association for the Advancement of Artificial Intelligence was pleased to present the 2010 Fall Symposium Series, held Thursday through Saturday, November 11-13, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the eight symposia are as follows: (1) Cognitive and Metacognitive Educational Systems; (2) Commonsense Knowledge; (3) Complex Adaptive Systems: Resilience, Robustness, and Evolvability; (4) Computational Models of Narrative; (5) Dialog with Robots; (6) Manifold Learning and Its Applications; (7) Proactive Assistant Agents; and (8) Quantum Informatics for Cognitive, Social, and Semantic Processes. The highlights of each symposium are presented in this report.

A principal one among them is the existence of multiple domains that share the same underlying causal structure for actions. We describe an approach that exploits this shared causal structure to discover a hierarchical task structure in a source domain, which in turn speeds up learning of task execution knowledge in a new target domain. Our approach is theoretically justified and compares favorably to manually designed task hierarchies in learning efficiency in the target domain. We demonstrate that causally motivated task hierarchies transfer more robustly than other kinds of detailed knowledge that depend on the idiosyncrasies of the source domain and are hence less transferable.

This article calls for the research community broaden its focus by conducting research that begins with the discovery of a transfer opportunity, and only arrives at the identification of relevant knowledge and methods after significant processing.

Transfer learning occurs when, after gaining experience from learning how to solve source problems, the same learner exploits this experience to improve performance and/or learning on target problems. In transfer learning, the differences between the source and target problems characterize the transfer distance. CBR can support transfer learning methods in multiple ways. We illustrate how CBR and transfer learning interact and characterize three approaches for using CBR in transfer learning: (1) as a transfer learning method, (2) for problem learning, and (3) to transfer knowledge between sets of problems.

This article argues that currently the largest gap between human and machine learning is learning algorithms' inability to perform deep transfer, that is, generalize from one domain to another domain containing different objects, classes, properties and relations. We argue that second-order Markov logic is ideally suited for this purpose, and propose an approach based on it. Our algorithm discovers structural regularities in the source domain in the form of Markov logic formulas with predicate variables, and instantiates these formulas with predicates from the target domain. Our approach has successfully transferred learned knowledge among molecular biology, Web and social network domains.

There is a Transfer Learning Toolkit for Matlab available on the web. Transfer learning has developed techniques for classification, regression, and clustering (as summarized in Pan and Yang's 2009 survey) and for complex interactive tasks that are often best addressed by reinforcement learning techniques. And transfer learning has been applied to domains as diverse as named entity recognition, image clustering, information retrieval, link prediction, AP physics, and others. As with many human-level AI goals, transfer learning is still a long way from the ability for agents to take advantage of relevant previous learned knowledge and experience to perform (at least) competently and effectively on new tasks the first time they are encountered. However, there is a more practical and more feasible goal for transfer learning against which progress is being made.

IEA / AIE-11 will be held June 28-July 4, 2011, in Syracuse, This page includes forthcoming AAAI sponsored conferences, conferences presented New York USA. by AAAI Affiliates, and conferences held in cooperation with AAAI. IE'11 will be held July 5-8, 2011 at Nottingham The Twenty-Fourth International Florida 2011 Robotics: Science and Systems AAAI Spring Symposium Series will be AI Research Society Conference. RSS 2011 will be held held March 21-23, 2011 in Stanford, Flairs-2011 will be held May 18-20, June 27-30, 2011, at the University of California. ICAPS 2011 will be held June 11-16, Artificial Intelligence. UAI 2011 will ICWSM-11 will be held in July 17-21 in 2011 in Freiburg, Germany.

Sequential decision tasks present many opportunities for the study of transfer learning. A principal one among them is the existence of multiple domains that share the same underlying causal structure for actions. We describe an approach that exploits this shared causal structure to discover a hierarchical task structure in a source domain, which in turn speeds up learning of task execution knowledge in a new target domain. Our approach is theoretically justified and compares favorably to manually designed task hierarchies in learning efficiency in the target domain. We demonstrate that causally motivated task hierarchies transfer more robustly than other kinds of detailed knowledge that depend on the idiosyncrasies of the source domain and are hence less transferable.