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.

We have explored the use of analogy as a general approach to near and far transfer learning in domains ranging from physics problem solving to strategy games (Klenk and Forbus 2007; Hinrichs and Forbus 2007). Using the same basic analogical mechanism, we have found that the main differences between near and far transfer involve the amount of generalization that must be performed prior to transfer and the way that the matching process treats nonidentical predicates. We present here two extensions of our analogical matcher, minimal ascension and metamapping, that enable far transfer between representations with different relational vocabulary. Evidence for the effectiveness of these techniques is provided by a large-scale external evaluation, involving a substantial number of novel distant analogs.

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.

David McAllester and David Rosenblitt's paper, "Systematic Nonlinear Planning" (published This commentary by Daniel S. Weld describes David Rosenblitt's paper, "Systematic Nonlinear Planning" (McAllester and Rosenblitt 1991), presented 19 years ago at the Ninth National Conference on Artificial Intelligence (AAAI-91), had two major impacts on the field: (1) an elegant algorithm and (2) endorsement of the lifting technique. The paper's biggest impact stems from its extremely clear and simple presentation of a sound and complete algorithm (known as SNLP or POP) for classical planning. While it is easy to define such an algorithm as search through the space of world states, SNLP is a "partialorder" planner, meaning it searches the space of partially specified plans, where only partial constraints on action arguments and ordering decisions are maintained. Here, McAllester and Rosenblitt benefited from David Chapman's elegant TWEAK planner, which greatly clarified previous partial-order algorithms (Chapman 1985). SNLP's key feature is the use of a data structure, called a causal link, to record the planner's commitment to establish a precondition of one action with the postcondition of another.

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.

Transfer learning has recently gained popularity due to the development of algorithms that can successfully generalize information across multiple tasks. This article focuses on transfer in the context of reinforcement learning domains, a general learning framework where an agent acts in an environment to maximize a reward signal. The goals of this article are to (1) familiarize readers with the transfer learning problem in reinforcement learning domains, (2) explain why the problem is both interesting and difficult, (3) present a selection of existing techniques that demonstrate different solutions, and (4) provide representative open problems in the hope of encouraging additional research in this exciting area.

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.

For example, baby boomers are aging. Researchers are actively pursuing interdisciplinary research that enables robots to function autnomously within arbitrary environments alongside people. The goal of the AAAI 2010 Workshop on Enabling Intelligence through Middleware was to examine both the successes and opportunities to provide tools that enable a larger pool of researchers to experiment with embodied, intelligent algorithms. The half-day workshop, attended by over 80 people, was held as part of the Twenty-Fourth AAAI Conference on Artificial Intelligence in Atlanta Georgia on July 12, 2010. The workshop consisted of two parts: (1) invited talks and (2) middleware presentations.

In the field of structural reliability, the Monte-Carlo estimator is considered as the reference probability estimator. However, it is still untractable for real engineering cases since it requires a high number of runs of the model. In order to reduce the number of computer experiments, many other approaches known as reliability methods have been proposed. A certain approach consists in replacing the original experiment by a surrogate which is much faster to evaluate. Nevertheless, it is often difficult (or even impossible) to quantify the error made by this substitution. In this paper an alternative approach is developed. It takes advantage of the kriging meta-modeling and importance sampling techniques. The proposed alternative estimator is finally applied to a finite element based structural reliability analysis.

This project describes the electricity demand and energy consumption management system and its application to Southern Peru smelter. It is composed of an hourly demand-forecasting module and of a simulation component for a plant electrical system. The first module was done using dynamic neural networks with backpropagation training algorithm; it is used to predict the electric power demanded every hour, with an error percentage below of 1%. This information allows efficient management of energy peak demands before this happen, distributing the raise of electric load to other hours or improving those equipments that increase the demand. The simulation module is based in advanced estimation techniques, such as: parametric estimation, neural network modeling, statistic regression and previously developed models, which simulates the electric behavior of the smelter plant. These modules facilitate electricity demand and consumption proper planning, because they allow knowing the behavior of the hourly demand and the consumption patterns of the plant, including the bill components, but also energy deficiencies and opportunities for improvement, based on analysis of information about equipments, processes and production plans, as well as maintenance programs. Finally the results of its application in Southern Peru smelter are presented.