Consider the problem of protecting endangered species by selecting patches of land to be used for conservation purposes. Typically, the availability of patches changes over time, and recommendations must be made dynamically. This is a challenging prototypical example of a sequential optimization problem under uncertainty in computational sustainability. Existing techniques do not scale to problems of realistic size. In this paper, we develop an efficient algorithm for adaptively making recommendations for dynamic conservation planning, and prove that it obtains near-optimal performance. We further evaluate our approach on a detailed reserve design case study of conservation planning for three rare species in the Pacific Northwest of the United States.

Colorizer is a program that hypothesizes color values that represent a given word or sentence, taking into account both physical descriptions of objects and their emotional connotations. This new application of common sense reasoning uses background knowledge about the world to build a model of the connections between everyday things, and uses this model to guess an appropriate color for a word. Colorizer can run over either static text or real time input, such as a speech recognition stream. It has applications in games, the arts, and webpage design.

In order to get strategic positioning for competition in business organization, the information system must be ahead in this information age where the information as one of the weapons to win the competition and in the right hand the information will become a right bullet. The information system with the information technology support isn't enough if just only on internet or implemented with internet technology. The growth of information technology as tools for helping and making people easy to use must be accompanied by wanting to make fun and happy when they make contact with the information technology itself. Basically human like to play, since childhood human have been playing, free and happy and when human grow up they can't play as much as when human was in their childhood. We have to develop the information system which is not perform information system itself but can help human to explore their natural instinct for playing, making fun and happiness when they interact with the information system. Virtual information system is the way to present playing and having fun atmosphere on working area.

In this paper, we present a method that uses web photos for measuring frame interestingness of a travel video. Web photo collections, such as those on Flickr, tend to contain interesting images because their images are more carefully taken, composed, and selected. Because these photos have already been chosen as subjectively interesting, they serve as evidence that similar images are also interesting. Our idea is to leverage these web photos to measure the interestingness of video frames. Specifically, we measure the interestingness of each video frame according to its similarity to web photos. The similarity is defined based on the scene content and composition. We characterize the scene content using scale invariant local features, specifically SIFT keypoints. We characterize composition by feature distribution. Accordingly, we measure the similarity between a web photo and a video frame based on the co-occurrence of the SIFT features, and the similarity between their spatial distribution. Interestingness of a video frame is measured by considering how many photos it is similar to, and how similar it is to them. Our experiments on measuring frame interestingness of videos from YouTube using photos from Flickr show the initial success of our method.

In this paper, we examine Robot Defense, a computer game that serves as a pedagogical platform for students to explore methods typically covered in an Introductory Artificial Intelligence course. Robot Defense is the synergistic outcome of two NSF funded Course, Curriculum, and Laboratory Improvement (CCLI) projects and was first presented in (Wallace, Russell and Markov 2008). The primary contribution of this paper is to discuss the implementation of the Robot Defense platform and the outcome of its first use in the classroom.

Rob Miller (Massachusetts Institute of Technology) will be held July 22-26, 2007 in Vancouver, Holger Hoos (University of British Columbia) British Columbia, Canada.

Sabater described current research in the area, presenting some of the current research lines and the shortcomings of present approaches. He also outlined some of the topics in which information-fusion and aggregation operators can play a role. The conference papers were published in Springer Verlag's Lecture Notes in Artificial Intelligence series (volume 3885). Further information on the series is available at mdai.cat. The next MDAI conference will be held August 16-18, 2007, in Kitakyushu, Japan.

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system.

Efficiently entering information into a computer is key to enjoying the benefits of computing. This paper describes three intelligent user interfaces: handwriting recognition, adaptive menus, and predictive fillin. In the context of adding a person's name and address to an electronic organizer, tests show handwriting recognition is slower than typing on an on-screen, soft keyboard, while adaptive menus and predictive fillin can be twice as fast. This paper also presents strategies for applying these three interfaces to other information collection domains.

There exist large classes of time series, such as those with nonlinear moving average components, that are not well modeled by feedforward networks or linear models, but can be modeled by recurrent networks. We show that recurrent neural networks are a type of nonlinear autoregressive-moving average (N ARMA) model. Practical ability will be shown in the results of a competition sponsored by the Puget Sound Power and Light Company, where the recurrent networks gave the best performance on electric load forecasting. 1 Introduction This paper will concentrate on identifying types of time series for which a recurrent network provides a significantly better model, and corresponding prediction, than a feedforward network. Our main interest is in discrete time series that are parsimoniously modeled by a simple recurrent network, but for which, a feedforward neural network is highly non-parsimonious by virtue of requiring an infinite amount of past observations as input to achieve the same accuracy in prediction. Our approach is to consider predictive neural networks as stochastic models.