(MAICS 2006). AISB'06 Symposium on Exploration Antecedents and Consequences of Versus Exploitation in Naturally Inspired Emotion. (IIS 2006). (ASM 2006). Please Help Us Celebrate 50 Years of AI at IAAI-06 Join Us in Boston!

The Representations for Multimodal Generation Workshop was held on April 23-25, 2005, at Reykjavik University, Reykjavik, Iceland. The overall goal of this workshop is to further the state of research on multimodal generation by enabling (and getting) people in the field to work together on building systems capable of real-time face-to-face dialog with people. This report summarizes the activities and progress of that meeting.

We report on the staging of the first competition on knowledge engineering for AI planning and scheduling systems, held in Monterey, California, in colocation with the ICAPS 2005 conference. The background and motivation is discussed, together with the relationship of this new competition with the current international planning competition. We report on the new competition's format, its outcome, and the benefits we hope it will bring to the research area.

In our post-9/11, Please note that: (1) an excerpt may not robotics at San Francisco State University. As these machines get through small spaces that might be left intelligence into future consumer electronics smarter and smarter, it may soon be far after a mine or building collapses." What of Science is embarking on a A Healthy Little Robot. The hallmarks of the mighty mission: to get schoolchildren excited U.S.News & World Report. December 12, new digital tool-building age are machines about engineering and technology, 2005 (www.usnews.com). "Sure, pets are that are increasingly smart, small, cheap help the US compete in the global economy, cute and seem to improve human health.

Robotics is a remarkable domain that may be successfully employed in the classroom both to motivate students to tackle hard AI topics and to provide students experience applying AI representations and algorithms to real-world problems. This article uses two example robotics problems to illustrate these themes. We show how the robot obstacle-detection problem can motivate learning neural networks and Bayesian networks. We also show how the robot-localization problem can motivate learning how to build complete solutions based on particle filtering. Since these lessons can be replicated on many low-cost robot platforms they are accessible to a broad population of AI students. We hope that by outlining our educational exercises and providing pointers to additional resources we can help reduce the effort expended by other educators. We believe that expanding handson active learning to additional AI classrooms provides value both to the students and to the future of the field itself.

From a computer science and artificial intelligence perspective, robotics often appears as a collection of disjoint, sometimes antagonistic subfields. The lack of a coherent and unified presentation of the field negatively affects teaching, especially to undergraduates. This article presents an alternative synthesis of the various subfields of AI robotics and shows how these traditional subfields fit into the whole. Finally, it presents a curriculum based on these ideas.

These workshops, although quite specialized, have covered areas of great interest for the conference delegates, namely: "Multiagent System Robotics" (MARS), "Biosignal Processing and Classification" (BPC), and "Artificial Neural Networks and Intelligent Information Processing" (ANNIIP). In the program of this conference for publication in the proceedings were included oral presentations (full and for presentation at the conference; papers and short papers) and posters, of these, 166 papers were organized in three simultaneous selected for oral presentation (67 full tracks: "Intelligent Control Systems papers and 99 short papers) and 63 papers and Optimization," "Robotics and Automation," were accepted for poster presentation. Furthermore, less than 60 percent, and the full paper (ICINCO 2005) was held in Barcelona ICINCO 2005 included acceptance ratio was 17 percent.

This homework introduces students the material in the course. For the written component to the concept of human/robot interaction of this homework, students have to and learning on a real robot. The students manually calculate a posterior probability of program their AIBOs to play a guessing game the robot's position given a uniform prior distribution by which one player (either the human or the of robot poses in a grid world. AIBO) guesses a sequence of colored markers Mounting a Charging Station. Students use the that the other player (AIBO or human, respectively) object-detection code written in previous makes up ahead of time. The AIBO communicates homework assignments to find a colored bull'seye to the human by a predefined set of and tower beacon. These two landmarks allow the robot to compute the distance and orientation motions. When guessing the colored sequence, of a charging station. The robot needs the AIBO has to reason about the patterns of to search for and then climb onto the charging the colors as well as about the clues given to it station.

MAP is the problem of finding a most probable instantiation of a set of variables given evidence. MAP has always been perceived to be significantly harder than the related problems of computing the probability of a variable instantiation Pr, or the problem of computing the most probable explanation (MPE). This paper investigates the complexity of MAP in Bayesian networks. Specifically, we show that MAP is complete for NP^PP and provide further negative complexity results for algorithms based on variable elimination. We also show that MAP remains hard even when MPE and Pr become easy. For example, we show that MAP is NP-complete when the networks are restricted to polytrees, and even then can not be effectively approximated. Given the difficulty of computing MAP exactly, and the difficulty of approximating MAP while providing useful guarantees on the resulting approximation, we investigate best effort approximations. We introduce a generic MAP approximation framework. We provide two instantiations of the framework; one for networks which are amenable to exact inference Pr, and one for networks for which even exact inference is too hard. This allows MAP approximation on networks that are too complex to even exactly solve the easier problems, Pr and MPE. Experimental results indicate that using these approximation algorithms provides much better solutions than standard techniques, and provide accurate MAP estimates in many cases.

In a peer-to-peer inference system, each peer can reason locally but can also solicit some of its acquaintances, which are peers sharing part of its vocabulary. In this paper, we consider peer-to-peer inference systems in which the local theory of each peer is a set of propositional clauses defined upon a local vocabulary. An important characteristic of peer-to-peer inference systems is that the global theory (the union of all peer theories) is not known (as opposed to partition-based reasoning systems). The main contribution of this paper is to provide the first consequence finding algorithm in a peer-to-peer setting: DeCA. It is anytime and computes consequences gradually from the solicited peer to peers that are more and more distant. We exhibit a sufficient condition on the acquaintance graph of the peer-to-peer inference system for guaranteeing the completeness of this algorithm. Another important contribution is to apply this general distributed reasoning setting to the setting of the Semantic Web through the Somewhere semantic peer-to-peer data management system. The last contribution of this paper is to provide an experimental analysis of the scalability of the peer-to-peer infrastructure that we propose, on large networks of 1000 peers.