Representation is a fluent. A mismatch between the real world and an agent's representation of it can be signalled by unexpected failures (or successes) of the agent's reasoning. Such mismatches can be repaired by refining or abstracting an agent's representation. These refinements or abstractions may not be limited to changes of belief, but may also change the signature of the agent's ontology. We describe the implementation and successful evaluation of these ideas in the ORS system. ORS diagnoses failures in plan execution and then repairs the faulty ontologies. Such dynamic ontology repair will be an essential tool in realising the vision of the Semantic Web.

To accomplish the next generation of challenging missions to the Moon, Mars, and beyond, researchers at NASA centers and at academic institutions have made significant progress over the last five years towards developing autonomous systems that can make critical decisions independently of human operators. Autonomy technology will extend the boundary on what can be accomplished in future missions by overcoming limitations due to communications delays, light-speed constraints, mission complexity, and cost. Autonomous systems will enable future space missions by maintaining vehicle health and safety, accomplishing complex science and mission goals, and adapting to changing circumstances or opportunities.

This talk will provide an overview of the current state of autonomy technology applied to deep space exploration, with particular emphasis placed on robotic surface explorers. First, I will motivate and describe the notion of autonomy as an enabler for deep space exploration. Second, I will discuss the contribution of Artificial Intelligence in current autonomy architectures, illustrating how automated systems for planning, plan execution and health management are being integrated into traditional control systems. Third, I will illustrate the challenges for developing autonomous systems through a case study of involving a Mars scenario, in which a rover is required to traverse to a target of interest, extend a flexible arm and acquire a close-up image. The ability to perform this scenario autonomously required advances along a broad technological front, including technology related to target tracking and instrument placement, planning and execution, and automated ground tools for coordinating with science teams. Finally, I will discuss the future challenges in developing autonomy technology for realizing NASA?s goal for humans to return to the Moon and eventually establish a permanent presence on Mars.

The World Wide Web provides a wealth of data that can be harnessed to help improve information retrieval and increase understanding of the relationships between different entities. In many cases, we are often interested in determining how similar two entities may be to each other, where the entities may be pieces of text, descriptions of some object, or even the preferences of a group of people. In this work, we examine several instances of this problem, and show how they can be addressed by harnessing data mining techniques applied to large web-based data sets. Specifically, we examine the problems of: (1) determining the similarity of short texts--even those that may not share any terms in common, (2) learning similarity functions for semi-structured data to address tasks such as record linkage between objects, and (3) measuring the similarity between on-line communities of users as part of a recommendation system. While we present rather different techniques for each problem, we show how measuring similarity between entities in all these domains has a direct application to the overarching goal of improving information access for users of web-based systems.

Conservative estimates of the Web's current size refer to its 10 billion documents and a growth rate that tops 60 terabytes of new information per day. In 2000 the entire World-Wide Web consisted of just 21 terabytes of information, now it grows by 3 times this every single day. This growth frames the information overload problem that is threatening to stall the information revolution going forward. In short, users are finding it increasingly difficult to locate the right information at the right time in the right way. Search engine technologies are struggling to cope with the sheer quantity of information that is available, a problem that is greatly exacerbated by the apparent inability of Web users to formulate effective search queries that accurately reflect their current information needs. This talk will focus on how so-called personalization techniques are being used in response to the information overload problem and the experiences gained and lessons learned when it comes to the deployment of these techniques. In particular, we will focus on the personalization of Web search, taking special care to consider the important privacy issues that such personalization brings to the fore. These issues motivate a unique approach to personalized Web search - Collaborative Web Search (CWS) - which focuses on the delivery of personalization at the level of a community of like-minded searchers.