Language processing has a large practical potential in intelligent interfaces if we take into account multiple modalities of communication. Multi-modality refers to the perception of different coordinated media used in delivering a message as well as the combination of various attitudes in relation to communication. In particular, the integration of natural language processing and hypermedia allows each modality to overcome the constraints of the other, resulting in a novel class of integrated environments for complex exploration and information access. Information presentation is a key element of such environments; generation techniques can contribute to their quality by producing texts ex novo or flexibly adapting existing material to the current situation. A great opportunity arises for intelligent interfaces and language technology of this kind to play an important role for individual-oriented cultural tourism. In the article, reference is made to some prototypes developed at IRST that were conceived for this specific area. A recent project concentrated on the combination of two forms of navigation taking place at the same time -- one in information space, the other in physical space. Collaboration, an important topic for intelligent interfaces, is also discussed.

Disaster rescue is one of the most serious social issues that involves very large numbers of heterogeneous agents in the hostile environment. The intention of the RoboCup Rescue project is to promote research and development in this socially significant domain at various levels, involving multiagent teamwork coordination, physical agents for search and rescue, information infrastructures, personal digital assistants, a standard simulator and decision-support systems, evaluation benchmarks for rescue strategies, and robotic systems that are all integrated into a comprehensive system in the future. For this effort, which was built on the success of the RoboCup Soccer project, we will provide forums of technical discussions and competitive evaluations for researchers and practitioners. Although the rescue domain is intuitively appealing as a large-scale multiagent and intelligent system domain, analysis has not yet revealed its domain characteristics. The first research evaluation meeting will be held at RoboCup-2001, in conjunction with the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-2001), as part of the RoboCup Rescue Simulation League and RoboCup/AAAI Rescue Robot Competition. In this article, we present a detailed analysis of the task domain and elucidate characteristics necessary for multiagent and intelligent systems for this domain. Then, we present an overview of the RoboCup Rescue project.

The Fourth Robotic Soccer World Championships (RoboCup-2000) was held from 27 August to 3 September 2000 at the Melbourne Exhibition Center in Melbourne, Australia. In total, 83 teams, consisting of about 500 people, participated in RoboCup-2000, and about 5000 spectators watched the events. RoboCup-2000 showed dramatic improvement over past years in each of the existing robotic soccer leagues (legged, small size, mid size, and simulation) and introduced RoboCup Jr. competitions and RoboCup Rescue and Humanoid demonstration events. The RoboCup Workshop, held in conjunction with the championships, provided a forum for the exchange of ideas and experiences among the different leagues. This article summarizes the advances seen at RoboCup-2000, including reports from the championship teams and overviews of all the RoboCup events.

In this document we propose a new improvement for boosting techniques as proposed in Friedman '99 by the use of non-convex cost functional. The idea is to introduce a correlation term to better deal with forecasting of additive time series. The problem is discussed in a theoretical way to prove the existence of minimizing sequence, and in a numerical way to propose a new "ArgMin" algorithm. The model has been used to perform the touristic presence forecast for the winter season 1999/2000 in Trentino (italian Alps).

A standard multilayer network trained with backpropagation [6] was not able to solve the date-calculation task.

Model Predictive Control was developed in the late 70's and came into widespread use, particularly in the refining industry, in the 80's. The economic benefit of this approach to control has been documented [1,2] .

He intended to build a model mimicking the behavior of the autistic savant without the need either to develop arithmetical skills or to encode explicit knowledge about regularities in the structure of dates. A standard multilayer network trained with backpropagation [6] was not able to solve the date-calculation task. Although the network was able to learn the examples used for training, it did not manage to generalize to novel date-day combinations.

Model Predictive Control was developed in the late 70's and came into widespread use, particularly in the refining industry, in the 80's. The economic benefit of this approach to control has been documented [1,2].

He intended to build a model mimicking the behavior of the autistic savant without the need either to develop arithmetical skills or to encode explicit knowledge about regularities in the structure of dates. A standard multilayer network trained with backpropagation [6] was not able to solve the date-calculation task. Although the network was able to learn the examples used for training, it did not manage to generalize to novel date-day combinations.

Model Predictive Control was developed in the late 70's and came into widespread use, particularly in the refining industry, in the 80's. The economic benefit of this approach to control has been documented [1,2].