The aim of the I-Ex project is to provide a means to integrate and structure the expedition activity with information from disparate sources and allow access to artificial intelligence technologies where appropriate. This support builds on the more general I-X architecture. I-X's foundations in AI planning technologies allow the invocation of automated planners that are able to suggest potential ways by which to achieve task sub-goals, based on knowledge of the capabilities of other - computer or human - agents that are currently available.

One of life's harsh little truths is that there are unfortunately a lot of people living unfulfilling lives. There are so many twists and turns that make us deviate from our hopes and dreams, leading to an awful lot of compromise. It's impossible to just flip a switch and have it all change to whatever we're dreaming of, but there at least a few ways to finally take the reigns and hopefully chase down a little more fulfillment and happiness. One of our favorite resources for this is The 67 Steps by Tai Lopez. If you want to know more about it then The 67 Steps Rocks!

Building an optimized schedule for upstream oil and gas operations is not an easy task. Getting equipment to the right place at the right time, with all the right people, across a number of pads spread over multiple geographic areas, make scheduling a complex challenge. Changing business goals make it even more challenging. Actenum's upstream schedule and optimization software turns operations scheduling from a painstaking manual task into a strategic advantage. Well and asset managers can immediately understand the impact on goals such as costs and production when moving equipment, removing rigs, adding crews, and making other resource decisions .

Creating and managing integrated schedules for upstream assets is a complex and challenging task. Schedulers must assign rigs and other equipment to well delivery, facility, and maintenance activities in the most efficient sequence, while keeping business goals in mind and avoiding offset well violations. DSO/Upstream scheduling and optimization software tames the complexity of integrated, optimized rig scheduling with powerful decision support technology and advanced analytics that rapidly identifies optimal schedules to meet business goals. Build and manage rig schedules, and incorporate other resources and activities for a comprehensive operations schedule. "What if?" scenarios show schedulers exactly how decisions will influence costs, production, equipment use, and financial goals.

The National Information Infrastructure (NII) will have profound effects on the lives of every citizen. It promises to deliver to people in their homes and offices a vast array of information in many forms, changing the ways in which business is conducted, offering new educational opportunities, bringing geographically dispersed library resources and entertainment materials to everyone's doorstep. It will connect people to people, and help them with their jobs and tasks. For the NII to be useful, however, people will need easy and efficient access to its resources. Today's computers are complex and difficult to use, even for experts. The NII will be orders of magnitude more complex than current systems; it could easily become a labyrinth of databases and services that is inconvenient for experts and inaccessible to many Americans. The field of artificial intelligence (AI) can play a pivotal role in meeting major challenges of the NII. AI uses the theoretical and experimental tools of ...

But it was one giant leap for computer-kind, with a state of the art artificial intelligence system being given primary command of a spacecraft. Known as Remote Agent, the software operated NASA's Deep Space 1 spacecraft and its futuristic ion engine during two experiments that started on Monday, May 17, 1999. For two days Remote Agent ran on the on-board computer of Deep Space 1, more than 60,000,000 miles (96,500,000 kilometers) from Earth. The tests were a step toward robotic explorers of the 21st century that are less costly, more capable and more independent from ground control. A second remote agent experiment was conducted on Friday, May 21, starting at 7:15 a.m.

This course is a graduate level introduction to automatic discourse processing. The emphasis will be on methods and models that have applicability to natural language and speech processing. The class will cover the following topics: discourse structure, models of coherence and cohesion, plan recognition algorithms, and text segmentation. We will study symbolic as well as machine learning methods for discourse analysis. We will also discuss the use of these methods in a variety of applications ranging from dialogue systems to automatic essay writing.

Topics covered include: representation and inference in first-order logic, modern deterministic and decision-theoretic planning techniques, basic supervised learning methods, and Bayesian network inference and learning. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5504 (Techniques in Artificial Intelligence).

Imagine that you could tell your phone that you want to drive from your house in Boston to a hotel in upstate New York, that you want to stop for lunch at an Applebee's at about 12:30, and that you don't want the trip to take more than four hours. Then imagine that your phone tells you that you have only a 66 percent chance of meeting those criteria -- but that if you can wait until 1:00 for lunch, or if you're willing to eat at TGI Friday's instead, it can get that probability up to 99 percent. That kind of application is the goal of Brian Williams' group at MIT's Computer Science and Artificial Intelligence Laboratory -- although the same underlying framework has led to software that both NASA and the Woods Hole Oceanographic Institution have used to plan missions. At the annual meeting of the Association for the Advancement of Artificial Intelligence (AAAI) this month, researchers in Williams' group will present algorithms that represent significant steps toward what Williams describes as "a better Siri" -- the user-assistance application found in Apple products. But they would be just as useful for any planning task -- say, scheduling flights or bus routes.

For the last decade, scientists have deployed increasingly capable underwater robots to map and monitor pockets of the ocean to track the health of fisheries, and survey marine habitats and species. In general, such robots are effective at carrying out low-level tasks, specifically assigned to them by human engineers -- a tedious and time-consuming process for the engineers. When deploying autonomous underwater vehicles (AUVs), much of an engineer's time is spent writing scripts, or low-level commands, in order to direct a robot to carry out a mission plan. Now a new programming approach developed by MIT engineers gives robots more "cognitive" capabilities, enabling humans to specify high-level goals, while a robot performs high-level decision-making to figure out how to achieve these goals. For example, an engineer may give a robot a list of goal locations to explore, along with any time constraints, as well as physical directions, such as staying a certain distance above the seafloor.