But less impressive are our and related AI TOPICS pages--at www. of it. July 10, 2007 task of fruit picking that currently employs not imply any endorsement whatsoever. Whether hypnotized by computer the migrant labor force. Farmers faster than the eye can scan them. Dow or console, players age 8 to 34 spend are'very, very nervous about the availability Jones and Reuters, the news providers, more time at this today than watching TV, and cost of labor in the near future,' says now offer electronically'tagged' news according to Nielsen. 'Most grew up addicted to also hopes to use algorithms to comb News.

The Internet contains a very large number of information sources providing many types of data from weather forecasts to travel deals and financial information. These sources can be accessed via Web-forms, Web Services, RSS feeds and so on. In order to make automated use of these sources, we need to model them semantically, but writing semantic descriptions for Web Services is both tedious and error prone. In this paper we investigate the problem of automatically generating such models. We introduce a framework for learning Datalog definitions of Web sources. In order to learn these definitions, our system actively invokes the sources and compares the data they produce with that of known sources of information. It then performs an inductive logic search through the space of plausible source definitions in order to learn the best possible semantic model for each new source. In this paper we perform an empirical evaluation of the system using real-world Web sources. The evaluation demonstrates the effectiveness of the approach, showing that we can automatically learn complex models for real sources in reasonable time. We also compare our system with a complex schema matching system, showing that our approach can handle the kinds of problems tackled by the latter.

This dissertation presents several new methods of supervised and unsupervised learning of word sense disambiguation models. The supervised methods focus on performing model searches through a space of probabilistic models, and the unsupervised methods rely on the use of Gibbs Sampling and the Expectation Maximization (EM) algorithm. In both the supervised and unsupervised case, the Naive Bayesian model is found to perform well. An explanation for this success is presented in terms of learning rates and bias-variance decompositions.

This research hypothesized that a practical approach in the form of a solution framework known as Natural Language Understanding and Reasoning for Intelligence (NaLURI), which combines full-discourse natural language understanding, powerful representation formalism capable of exploiting ontological information and reasoning approach with advanced features, will solve the following problems without compromising practicality factors: 1) restriction on the nature of question and response, and 2) limitation to scale across domains and to real-life natural language text.

In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a probabilistic, relational planning rule representation that compactly models noisy, nondeterministic action effects, and show how such rules can be effectively learned. Through experiments in simple planning domains and a 3D simulated blocks world with realistic physics, we demonstrate that this learning algorithm allows agents to effectively model world dynamics.

We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to solve a maximum likelihood problem with an added l_1-norm penalty term. The problem as formulated is convex but the memory requirements and complexity of existing interior point methods are prohibitive for problems with more than tens of nodes. We present two new algorithms for solving problems with at least a thousand nodes in the Gaussian case. Our first algorithm uses block coordinate descent, and can be interpreted as recursive l_1-norm penalized regression. Our second algorithm, based on Nesterov's first order method, yields a complexity estimate with a better dependence on problem size than existing interior point methods. Using a log determinant relaxation of the log partition function (Wainwright & Jordan (2006)), we show that these same algorithms can be used to solve an approximate sparse maximum likelihood problem for the binary case. We test our algorithms on synthetic data, as well as on gene expression and senate voting records data.

'We should be worried, for aaai.org/aitopics/ We are Please note that: (1) an excerpt may not my understanding, I visited USC's'We need to tell the the fact that an item has been selected does University of Massachusetts in Amherst, Robot Wars -- An Attempt to Build an with many of the programs in the omy -- that prompted interest in the technology, Ethical Robotic Soldier. 'We are Technology, in Atlanta, is developing a set those tested uses the sort of artificial intelligence studying the application of the RAHS concepts of rules of engagement for battlefield technology that encourages highlevel and tools to the social, and economic robots to ensure that their use of lethal interactivity.... Call me an industry and financial domains,' Nathan wrote force follows the rules of ethics. In other cheerleader, but what I see at [William in an email interview." Kim conscience.... His approach is to create suggests that computers are already helping Yoon-mi. April 28, 2007 what he calls a'multidimensional mathematical students learn and will become increasingly (www.koreaherald.co.kr). "To literally live decision space of possible behavior important year by year." with robots, that are highly likely to become actions'.... Arkin has started to survey policy Search Engine Spawned from Antiterrorism more intelligent and physically closer makers, the public, researchers and military Efforts Finds Place in Business. "Artificial-intelligence-based that will prevent robots from doing harm Computer Science Takes Steps to Bring search technology to people, and block humans from taking Women to the Fold.

Approximately November 9-11, at the Westin Arlington and IAAI-08, please see www.aaai.org/

The Fifteenth Annual AAAI Robot Competition and Exhibition was held at the Twenty-First National Conference on Artificial Intelligence in Boston, Massachusetts, in July 2006. This article describes the events that were held at the conference, including the Scavenger Hunt, Human Robot Interaction, and Robot Exhibition.

The MAPGEN system represents a successful mission infusion of mixed-initiative planning technology. MAPGEN was deployed as a mission-critical component of the ground operations system for the Mars Exploration Rover mission. Each day, the ground-planning personnel employ MAPGEN to collaboratively plan the activities of the "Spirit and "Opportunity rovers, with the objective of achieving as much science as possible while ensuring rover safety and keeping within the limitations of the rovers' resources. The Mars Exploration Rover mission has now been operating for more than two years, and MAPGEN continues to be employed for activity plan generation for the Spirit and Opportunity rovers. During the multiyear deployment effort and subsequent mission operations experience, we have learned valuable lessons regarding application of mixed-initiative planning technology to mission operations. These lessons have spawned new research in mixed-initiative planning and have influenced the design of a new ground operations system, called M-SLICE, that is baselined for the Mars Science Laboratory mission. In this article, we discuss the mixed-initiative aspects of the MAPGEN system, focusing on the task, control, and awareness issues.