It was founded in 1991 and, since 1994, has been located on the campus of the Autonomous University of Barcelona. IIIA grew out of an AI research group at the Center for Advanced Studies in Blanes (Spain) that started AI research in 1985. On average IIIA has had about 50 members per year during the last 12 years with a peak of almost 80 members in 2012. In total around 200 different people, including visiting researchers as well as master's and Ph.D. students, have been members of IIIA over the past 20 years. Seventy-seven students have completed their Ph.D. work at our Institute, 48 of them during the last 12 years.

Robots are added to human teams to increase the team's skills or capabilities but in order to get the full benefit the teams must trust the robots. We present an approach that allows a robot to estimate its trustworthiness and adapt its behavior accordingly. Additionally, the robot uses case-based reasoning to store previous behavior adaptations and uses this information to perform future adaptations. In a simulated robotics domain, we compare case-based behavior adaption to behavior adaptation that does not learn and show it significantly reduces the number of behaviors that need to be evaluated before a trustworthy behavior is found.

Learning by observation is an important goal in developing complete intelligent robots that learn interactively. We present a visual analogy approach toward an integrated, intelligent system capable of learning skills from observation. In particular, we focus on the task of retrieving a previously acquired case similar to a new, observed skill. We describe three approaches to case retrieval: feature matching, feature transformation, and fractal analogy. SIFT features and fractal encoding were used to represent the visual state prior to the skill demonstration, the final state after the skill has been executed, and the visual transformation between the two states. We discovered that the three methods (feature matching, feature transformation, and fractal analogy) are useful for retrieval of similar skill cases under different conditions pertaining to the observed skills.

In case-based reasoning (CBR), problems are solved by retrieving prior cases and adapting their solutions to fit; learning occurs as new cases are stored. Controlling the growth of the case base is a fundamental problem, and research on case-base maintenance has developed methods for compacting case bases while maintaining system competence, primarily by competence-based deletion strategies assuming static case adaptation knowledge. This paper proposes adaptation-guided case-base maintenance (AGCBM), a case-base maintenance approach exploiting the ability to dynamically generate new adaptation knowledge from cases. In AGCBM, case retention decisions are based both on cases' value as base cases for solving problems and on their value for generating new adaptation rules. he paper illustrates the method for numerical prediction tasks (case-based regression) in which adaptation rules are generated automatically using the case difference heuristic. In comparisons of AGCBM to five alternative methods in four domains, for varying case base densities, AGCBM outperformed the alternatives in all domains, with greatest benefit at high compression.

This page includes forthcoming AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI. AI Magazine also maintains a calendar listing that includes nonaffiliated conferences at www.aaai.org/Magazine/calendar.php. AAAI-14 will be on Principles of Knowledge 6th International Joint Conference held July 27-31 in Quebec City, Quebec, Representation and Reasoning. AIIDE-14 will be held SOCS 2014 will be held August 15-17 Fifth International Conference on October 3-7 in Raleigh, NC, USA in Prague, Czech Republic Social Robotics. HRI 2015 will be held March 1-4 Robotics: Science and Systems 2014. in Portland, Oregon USA RSS 2014 will be held July 12-16 in AAAI Fall Symposium Series.

Nearest neighbor methods are a popular class of nonparametric estimators with several desirable properties, such as adaptivity to different distance scales in different regions of space. Prior work on convergence rates for nearest neighbor classification has not fully reflected these subtle properties. We analyze the behavior of these estimators in metric spaces and provide finite-sample, distribution-dependent rates of convergence under minimal assumptions. As a by-product, we are able to establish the universal consistency of nearest neighbor in a broader range of data spaces than was previously known. We illustrate our upper and lower bounds by introducing smoothness classes that are customized for nearest neighbor classification.

The CBR special track at FLAIRS has come to fill the important role of a North American symposium on CBR and it is well regarded in the community. Ths year we were pleased to accept two full papers and one poster paper.

We study in this paper how explicit user feedback can be used by a case-based reasoning system to improve the quality of its retrieval phase. More specifically, we explore how both ranking feedback and relevance feedback can be exploited to modify the weights of case features. We propose some options to cope with each type of feedback. We also evaluate, in an interactive setting, their impact on a travel scenario where some user provides feedback on a series of queries. Our results indicate that the combined weight-learning scheme proposed in this paper succeeds, on average, to assign more weights to the features used to formulate relevance and ranking feedback.

Instance-based learning methods predict the solution of a case from the solutions of similar cases.However, solutions can be generated from less similar cases as well, provided appropriate case adaptation rules are available to adjust the prior solutions to account for dissimilarities. In fact, case-based reasoning research on adaptation-guided retrieval (AGR) shows that it may be beneficial to base retrieval decisions primarily on the availability of suitable adaptation knowledge, rather than on similarity. This paper proposes a new method for adaptation-guided retrieval for numerical prediction (regression) tasks. The method, EAGR (ensemble of adaptations-guided retrieval) works by retrieving an ensemble of cases, with a case favored for retrieval if there exists an ensemble of adaptation rules suitable for adapting its solution to the current problem. The solution for the input problem is then calculated by applying each retrieved case's ensemble of adaptations to that case, and combining the generated values. The approach is evaluated on four sample domains compared to three baseline methods: k-NN, an adaptation-guided retrieval approach, and a previous approach using ensembles of adaptations without adaptation-guided retrieval. EAGR improves accuracy in the tested domains compared to the other methods.

In cooperation with the Association for the Advancement of Artificial Intelligence (AAAI), the twenty-first International Conference on Case-Based Reasoning (ICCBR), the premier international meeting on research and applications in Case-Based Reasoning (CBR), was held in July 2013 in Saratoga Springs, NY. ICCBR is the annual meeting of the CBR community and the leading conference on this topic. This year ICCBR featured the Industry Day, the fifth annual Doctoral Consortium and three workshops.