Results


Automated Scheduling for NASA's Deep Space Network

AI Magazine

This article describes the DSN scheduling wngine (DSE) component of a new scheduling system being deployed for NASA's deep space network. The DSE provides core automation functionality for scheduling the network, including the interpretation of scheduling requirements expressed by users, their elaboration into tracking passes, and the resolution of conflicts and constraint violations. It has been integrated with a web application which provides DSE functionality to all DSN users through a standard web browser, as part of a peer-to-peer schedule negotiation process for the entire network. The system has been deployed operationally and is in routine use, and is in the process of being extended to support long-range planning and forecasting, and near-real-time scheduling.


Leveraging Multiple Artificial Intelligence Techniques to Improve the Responsiveness in Operations Planning: ASPEN for Orbital Express

AI Magazine

The challenging timeline for DARPA's Orbital Express mission demanded a flexible, responsive, and (above all) safe approach to mission planning. Mission planning for space is challenging because of the mixture of goals and constraints. These technologies had a significant impact on the success of the Orbital Express mission. Finally, we formulated a technique for converting procedural information to declarative information by transforming procedures into models of hierarchical task networks (HTNs).


Sequential Decision Making in Computational Sustainability via Adaptive Submodularity

AI Magazine

Many problems in computational sustainability require making a sequence of decisions in complex, uncertain environments. In this article, we review the recently discovered notion of adaptive submodularity, an intuitive diminishing returns condition that generalizes the classical notion of submodular set functions to sequential decision problems. We illustrate this concept in several case studies of interest in computational sustainability: First, we demonstrate how it can be used to efficiently plan for resolving uncertainty in adaptive management scenarios. Secondly, we show how it applies to dynamic conservation planning for protecting endangered species, a case study carried out in collaboration with the US Geological Survey and the US Fish and Wildlife Service.


The Eighth International Workshop on Planning and Scheduling for Space (IWPSS)

AI Magazine

The Eighth International Workshop on Planning and Scheduling for Space (IWPSS 2013) was held on March 25–26 2013 at the NASA Ames Research Center, Moffett Field, California. This was the eighth in a regular series that started in 1997.


Adapting Open Information Extraction to Domain-Specific Relations

AI Magazine

Information extraction (IE) can identify a set of relations from free text to support question answering (QA). Until recently, IE systems were domain-specific and needed a combination of manual engineering and supervised learning to adapt to each target domain. A new paradigm, Open IE operates on large text corpora without any manual tagging of relations, and indeed without any pre-specified relations. We explore the steps needed to adapt Open IE to a domain-specific ontology and demonstrate our approach of mapping domain-independent tuples to an ontology using domains from DARPA's Machine Reading Project.


Local Search for Optimal Global Map Generation Using Mid-Decadal Landsat Images

AI Magazine

NASA and the United States Geological Survey (USGS) are collaborating to produce a global map of the Earth using Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM) remote sensor data from the period of 2004 through 2007. Constraints and preferences on map quality make it desirable to develop an automated solution to the map generation problem. This paper formulates a Global Map Generator problem as a Constraint Optimization Problem (GMG-COP) and describes an approach to solving it using local search. The paper also describes the integration of a GMG solver into a graphical user interface for visualizing and comparing solutions, thus allowing for solutions to be generated with human participation and guidance.


A Personal Account of the Development of Stanley, the Robot That Won the DARPA Grand Challenge

AI Magazine

This article is my personal account on the work at Stanford on Stanley, the winning robot in the DARPA Grand Challenge. Between July 2004 and October 2005, my then-postdoc Michael Montemerlo and I led a team of students, engineers, and professionals with the single vision of claiming one of the most prestigious trophies in the field of robotics: the DARPA Grand Challenge (DARPA 2004). The Grand Challenge, organized by the U.S. government, was unprecedented in the nation's history. Instead, this is my personal story of leading the Stanford Racing Team.


NESTA: NASA Engineering Shuttle Telemetry Agent

AI Magazine

The Electrical Systems Division at the NASA Kennedy Space Center has developed and deployed an agent-based tool to monitor the space shuttle's ground processing telemetry stream. The agent provides autonomous monitoring of the telemetry stream and automatically alerts system engineers when predefined criteria have been met. Sandia National Labs' Java Expert System Shell is employed as the rule engine. This article discusses the rule-based telemetry agent used for space shuttle ground processing and explains the problem domain, development of the agent software, benefits of AI technology, and deployment and sustaining engineering of the product.



The 2003 International Conference on Automated Planning and Scheduling (ICAPS-03)

AI Magazine

The 2003International Conference on Automated Planning and Scheduling (ICAPS-03) was held 9 to 13 June 2003 in Trento, Italy. It was chaired by Enrico Giunchiglia (University of Genova), Nicola Muscettola (NASA Ames), and Dana Nau (University of Maryland). Piergiorgio Bertoli and Marco Benedetti (both from ITC-IRST) were the local chair and the workshop-tutorial coordination chair, respectively.