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Smith, Benjamin D.

Publications and source records attributed to Smith, Benjamin D..

8 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Using Modern Methodologies with Maintenance Software

Jet Propulsion Laboratory uses multi-mission software produced by the Mission Planning and Sequencing (MPS) team to process, simulate, translate, and package the commands that are sent to a spacecraft. MPS works under the auspices of the Multi-Mission Ground Systems and Services (MGSS). This software consists of nineteen applications that are in maintenance. The MPS software is classified as either class B (mission critical) or class C (mission important). The scheduling of tasks is difficult because mission needs must be addressed prior to performing any other tasks and those needs often spring up unexpectedly. Keeping track of the tasks that everyone is working on is also difficult because each person is working on a different software component. Recently the group adopted the Scrum methodology for planning and scheduling tasks. Scrum is one of the newer methodologies typically used in agile development. In the Scrum development environment, teams pick their tasks that are to be completed within a sprint based on priority. The team specifies the sprint length usually a month or less. Scrum is typically used for new development of one application. In the Scrum methodology there is a scrum master who is a facilitator who tries to make sure that everything moves smoothly, a product owner who represents the user(s) of the software and the team. MPS is not the traditional environment for the Scrum methodology. MPS has many software applications in maintenance, team members who are working on disparate applications, many users, and is interruptible based on mission needs, issues and requirements. In order to use scrum, the methodology needed adaptation to MPS. Scrum was chosen because it is adaptable. This paper is about the development of the process for using scrum, a new development methodology, with a team that works on disparate interruptible tasks on multiple software applications.

Scrum↗
NASA NTRS2013

Uplink Summary Generator (ULSGEN) Version 1.0

The Uplink Summary Generator (ULSGEN) provides a convenient means of gathering together a set of uplink related files, parsing and analyzing these files, and producing a summary of their contents, which may then be electronically signed by one or more reviewers to verify the commands. Spacecraft operations personnel view this summary as a final sanity check before actual radiation of the uplink data.

Wang, Yeou-Fang↗
NASA NTRS2005

Model Checking Artificial Intelligence Based Planners: Even the Best Laid Plans Must Be Verified

Automated planning systems (APS) are gaining acceptance for use on NASA missions as evidenced by APS flown On missions such as Orbiter and Deep Space 1 both of which were commanded by onboard planning systems. The planning system takes high level goals and expands them onboard into a detailed of action fiat the spacecraft executes. The system must be verified to ensure that the automatically generated plans achieve the goals as expected and do not generate actions that would harm the spacecraft or mission. These systems are typically tested using empirical methods. Formal methods, such as model checking, offer exhaustive or measurable test coverage which leads to much greater confidence in correctness. This paper describes a formal method based on the SPIN model checker. This method guarantees that possible plans meet certain desirable properties. We express the input model in Promela, the language of SPIN and express the properties of desirable plans formally.

model checking↗
NASA NTRS2001

The RadarSAT-MAMM automated mission planner

The RadarSAT Modified Antarctic Mapping Mission (MAMM) ran from September to November 2000. It consisted of over 2400 synthetic aperture radar (SAR) data takes over Antarctica that had to satisfy coverage and other scientific criteria while obeying tight resource and operational constraints. Developing these plans is a time and knowledge intensive effort. It required over a work-year to manually develop a comparable plan for AMM-1, the precursor mission to MAMM. This paper describes the automated mission planning system for MAMM, which dramatically reduced mission-planning costs to just a few workweeks, and enabled rapid generation of 'what-if' scenarios for evaluating mission-design trades. This latter capability informed several critical design decisions and was instrumental in accurately costing the mission.

RadarSAT Modified Antarctic Mapping Mission (MAMM)↗
NASA NTRS2001

Reducing costs of the modified Antarctic Mapping mission through automated planning

This paper describes the automated mission planning system for MAMM, which dramatically reduced mission-planning costs to just a few workweeks, and enabled rapid generation of 'what-if' scenarios for evaluating mission-design trades. This latter capability informed several critical design decisions and was instrumental in accurately costing the mission.

reduce mission costs↗
NASA NTRS1996

Induction as Knowledge Integration

Two key issues for induction algorithms are the accuracy of the learned hypothesis and the computational resources consumed in inducing that hypothesis. One of the most promising ways to improve performance along both dimensions is to make use of additional knowledge. Multi-strategy learning algorithms tackle this problem by employing several strategies for handling different kinds of knowledge in different ways. However, integrating knowledge into an induction algorithm can be difficult when the new knowledge differs significantly from the knowledge the algorithm already uses. In many cases the algorithm must be rewritten. This paper presents Knowledge Integration framework for Induction (KII), a KII, that provides a uniform mechanism for integrating knowledge into induction. In theory, arbitrary knowledge can be integrated with this mechanism, but in practice the knowledge representation language determines both the knowledge that can be integrated, and the costs of integration and induction. By instantiating KII with various set representations, algorithms can be generated at different trade-off points along these dimensions. One instantiation of KII, called RS-KII, is presented that can implement hybrid induction algorithms, depending on which knowledge it utilizes. RS-KII is demonstrated to implement AQ-11, as well as a hybrid algorithm that utilizes a domain theory and noisy examples. Other algorithms are also possible.

Smith, Benjamin D.↗
NASA NTRS1994

Two frameworks for integrating knowledge in induction

The use of knowledge in inductive learning is critical for improving the quality of the concept definitions generated, reducing the number of examples required in order to learn effective concept definitions, and reducing the computation needed to find good concept definitions. Relevant knowledge may come in many forms (such as examples, descriptions, advice, and constraints) and from many sources (such as books, teachers, databases, and scientific instruments). How to extract the relevant knowledge from this plethora of possibilities, and then to integrate it together so as to appropriately affect the induction process is perhaps the key issue at this point in inductive learning. Here the focus is on the integration part of this problem; that is, how induction algorithms can, and do, utilize a range of extracted knowledge. Preliminary work on a transformational framework for defining knowledge-intensive inductive algorithms out of relatively knowledge-free algorithms is described, as is a more tentative problems-space framework that attempts to cover all induction algorithms within a single general approach. These frameworks help to organize what is known about current knowledge-intensive induction algorithms, and to point towards new algorithms.

Rosenbloom, Paul S.↗