New this year: Attendance at the tutorials is May 20, 2011.

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.

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.

Case-based reasoning (CBR) is a problem-solving process in which a new problem is solved by retrieving a similar situation and reusing its solution. 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. We describe examples of these approaches from our own and related work and discuss applicable transfer distances for each. We close with conclusions and directions for future research applying CBR to transfer learning.

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.

This issue summarizes the state of the art in structured knowledge transfer, which is an emerging approach to the general problem of knowledge acquisition and reuse. Its goal is to capture, in a general form, the internal structure of the objects, relations, strategies, and processes used to solve tasks drawn from a source domain, and exploit that knowledge to improve performance in a target domain.

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.

The use of the mathematical formalism of quantum mechanics as a modeling instrument in disciplines different from physics is now a well established practice and has historically been motivated by different reasons. Firstly, this is due to the flexibility and richness of quantum structures (vector spaces, inner products, quantum probability, quantum logic connectives, etc.). Secondly, there are two aspects that are seemingly characteristic of quantum entities, i.e. contextuality and entanglement, and that appear instead independently of the microscopic nature of these entities. Thirdly, the fact that since the fifties and sixties several effects have been recognized in a variety of areas, such as, economics, biology, psychology ... in which the application of classical structures (set theory, classical logic, Kolmogorovian probability, etc.) is problematical and generates paradoxes. The Allais [1] and Ellsberg [2] paradoxes in economics, the conjunction fallacy [3] and disjunction effect [4] in decision theory, the representation of concepts and the formalization of meaning in cognitive science [5], are the most important examples of situations in which classical structures do not provide satisfactory results, but more general structures are needed. In particular, the impossibility of formalizing and structuring human and artificial knowledge slackened, notwithstanding the impressive technological success, in the development of some applied research fields, such as artificial intelligence and robotics.

Experiments in cognitive science and decision theory show that the ways in which people combine concepts and make decisions cannot be described by classical logic and probability theory. This has serious implications for applied disciplines such as information retrieval, artificial intelligence and robotics. Inspired by a mathematical formalism that generalizes quantum mechanics the authors have constructed a contextual framework for both concept representation and decision making, together with quantum models that are in strong alignment with experimental data. The results can be interpreted by assuming the existence in human thought of a double-layered structure, a 'classical logical thought' and a 'quantum conceptual thought', the latter being responsible of the above paradoxes and nonclassical effects. The presence of a quantum structure in cognition is relevant, for it shows that quantum mechanics provides not only a useful modeling tool for experimental data but also supplies a structural model for human and artificial thought processes. This approach has strong connections with theories formalizing meaning, such as semantic analysis, and has also a deep impact on computer science, information retrieval and artificial intelligence. More specifically, the links with information retrieval are discussed in this paper.