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Planning and executing a trip requires assembling a wide variety of interacting information from a large number of sources, including information on flight schedules and prices, hotel locations and reviews, ground transportation options, weather conditions, airport delays, flight cancellations, etc. Much of this information is now available on the Internet and it can be used to enable travelers to better plan and execute their trips. This paper describes the use of software agents for extracting, integrating and mining online data sources to improve the ability to plan, monitor, and optimize travel. These agents can dynamically extract data from online travel sources, integrate this data to support interactive travel planning, continuously monitor all aspects of a trip to ensure a trip goes smoothly, and exploit data mining to make predictions that can either save a traveler money or improve the likelihood of a successful trip.

my first and most influential teachers. For their encouragement, understanding, and love. ii Acknowledgements I would very much like to thank my thesis advisor Craig Knoblock for the many enjoyable years of mentorship, support, and friendship. Craig has always given me the freedom to explore my own paths towards solving a problem, encouraged me to take chances, while at the same time challenging me to back up my claims and to sometimes consider alternative approaches. Through him, I learned how to read research papers as well as how to write them. His thoughts and advice greatly influenced and improved this thesis. I am extremely grateful for his guidance and I know that it will continue to inspire me as I work with and mentor others.

The information resources on the Web are vast, but much of the Web is based on a browsing paradigm that requires someone to actively seek information. Instead, one would like to have information agents that continuously attend to one's personal information needs. Such agents need to be able to extract the relevant information from web sources, integrate data across sites, and execute efficiently in a networked environment. In this paper I describe the technologies we have developed to rapidly construct and deploy information agents on the Web. This includes wrapper learning to convert online sources into agent-friendly resources, query planning and record linkage to integrate data across different sites, and streaming dataflow execution to efficiently execute agent plans. I also describe how we applied this work within the Electric Elves project to deploy a set of agents for continuous monitoring of travel itineraries. 1

Software agents can be used to automate many of the tedious, time-consuming information processing tasks that humans currently have to complete manually. However, to do so, agent plans must be capable of representing the myriad of actions and control flows required to perform those tasks. In addition, since these tasks can require integrating multiple sources of remote information – typically, a slow, I/O-bound process – it is desirable to make execution as efficient as possible. To address both of these needs, we present a flexible software agent plan language and a highly parallel execution system that enable the efficient execution of expressive agent plans. The plan language allows complex tasks to be more easily expressed by providing a variety of operators for flexibly processing the data as well as supporting subplans (for modularity) and recursion (for indeterminate looping). The executor is based on a streaming dataflow model of execution to maximize the amount of operator and data parallelism possible at runtime. We have implemented both the language and executor in a system called THESEUS. Our results from testing THESEUS show that streaming dataflow execution can yield significant speedups over both traditional serial (von Neumann) as well as non-streaming dataflow-style execution that existing software and robot agent execution systems currently support. In addition, we show how plans written in the language we present can represent certain types of subtasks that cannot be accomplished using the languages supported by network query engines. Finally, we demonstrate that the increased expressivity of our plan language does not hamper performance; specifically, we show how data can be integrated from multiple remote sources just as efficiently using our architecture as is possible with a state-of-the-art streaming-dataflow network query engine.

This paper summarizes the current state of art in the domain of automated travel planning. Requirements for planning systems are identified taking into account both functionality and personalization aspects of such systems. A new algorithm that allows planning routes between any two locations and utilizes various means of transportation is discussed.

f XML queries, while Theseus provides an expressive language for expressing information gathering and monitoring plans. The Theseus language supports capabilities that go beyond network query engines in that it supports recursion, notification operations, and writing and reading from databases to support monitoring tasks. We developed an approach to increase the potential parallelism in a streaming dataflow execution system. This optimization technique, called speculative execution [7,8], predicts the results of an operation based on data and patterns that it has seen in the past. The predicted results can then be used to speculate about the operations that will need to be performed later in the plan. The system decides where to speculate by analyzing a plan and determining the critical paths. On these paths it then inserts a "speculate" operation, which uses H. Munoz-Avila and F. Ricci (Eds.): ICCBR 2005, LNCS 3620, pp. 2--3, 2005. c # Springer-Verlag Berlin Heidelberg 2005 Learn