How Task Analysis Can Be Used to Derive and Organize the Knowledge for the Control of Autonomous Vehicles
Tony 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 of years as a technique to capture task knowledge and organize it in a framework 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 support processing. In particular, the task context at any time determines what is to 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 which behavior generation knowledge is to be accessed. This results in a methodology that concentrates first and foremost on the task definition. It starts with the definition of the task knowledge in the form of a decision tree that clearly represents the branching of tasks into layers of simpler and simpler subtask sequences. This task decomposition framework is then used to guide the search for and to emplace all of the additional knowledge. This paper explores this process in some detail, showing how this knowledge is represented in a task contextsensitive relationship that supports the very complex realtime processing the computer control systems will have to do.
Proceedings of the AAAI Sprint Symposium Series on Knowledge Representation and Ontology for Autonomous Systems
March 22-24, 2004
Palo Alto , CA, USA
AAAI Sprint Symposium Series on Knowledge Representation and Ontology for Autonomous Systems
, Albus, J.
, Messina, E.
, Schlenoff, C.
and Horst, J.
How Task Analysis Can Be Used to Derive and Organize the Knowledge for the Control of Autonomous Vehicles, Proceedings of the AAAI Sprint Symposium Series on Knowledge Representation and Ontology for Autonomous Systems, Palo Alto , CA, USA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=822481
(Accessed February 26, 2024)