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How Task Analysis Can Be Used to Derive and Organize the Knowledgefor the Control of Autonomous Vehicles



Anthony J. Barbera, James S. Albus, Elena R. Messina, Craig I. Schlenoff, John A. Horst


The Real-time Control System (RCS) Methodology has evolved over a number ofyears as a technique to capture task knowledge and organize it in aframework conducive to implementation in computer control systems. The fundamental premise of this methodology is that the present state of the task activities sets the context that identifies the requirements for all of the supportprocessing. In particular, the task context at any time determines what isto be sensed in the world, what world model states are to be evaluated,which situations are to be analyzed, what plans should be invoked, and whichbehavior generation knowledge is to be accessed. This results in amethodology that concentrates first and foremost on the task definition. Itstarts with the definition of the task knowledge in the form of a decisiontree that clearly represents the branching of tasks into layers of simplerand simpler subtask sequences. This task decomposition framework is thenused to guide the search for and to emplace all of the additional knowledge.This paper explores this process in some detail, showing how this knowledgeis represented in a task context-sensitive relationship that supports thevery complex real-time processing the computer control systems will have todo.
To Be Determined


4D/RCS, autonomous vehicle control, knowledge requirements, methodology, task decomposition


Barbera, A. , Albus, J. , Messina, E. , Schlenoff, C. and Horst, J. (2004), How Task Analysis Can Be Used to Derive and Organize the Knowledgefor the Control of Autonomous Vehicles, To Be Determined, [online], (Accessed July 14, 2024)


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Created June 16, 2004, Updated October 12, 2021