Function Allocation for NextGen Airspace via Agents

Commercial aviation transportation is on the rise and hasbecome a necessity in our increasingly global world. Thereis a societal demand for more options, more traffic, moreefficiency, while still maintaining safety in the airspace. Tomeet these demands the Next Generation Air Transportation System (NextGen) concept from NASA calls for technologies and systems offering increasing support from automated decision-aiding and optimization tools. Such systemsmust coordinate with the human operator to take advantage of the functions each can best perform: The automatedtools must be designed to support the optimal allocation oftasks (functions) between the system and the human operators using these systems. Preliminary function allocationmethods must be developed (and evaluated) that focus onthe NextGen Airportal challenges, given a flexible, changingConcept of Operations (ConOps). We have begun making steps toward this by leveragingwork in agents research (namely Adjustable Autonomy) inorder to allow function allocation to become more dynamicand adjust to the goals, demands, and constraints of thecurrent situation as it unfolds. In this paper we introduceDynamic Function Allocation Strategies (DFAS) that arenot static and singular, but rather are represented by allocation policies that vary over time and circumstances. TheNextGen aviation domain is a natural fit for agent basedsystems because of its inherently distributed nature and theneed for automated systems to coordinate on tasks mapswell to the adjustable autonomy problem. While current adjustable autonomy methods are applicable in this context, crucial extensions are needed to push the existing models tolarger numbers of human players, while maintaining criticaltiming. To this end, we have created an air traffic controlsystem that includes: (1) A simulation environment, (2) aDFAS algorithm for providing adjustable autonomy strategies and (3) the agents for executing the strategies and measuring system efficiency. We believe that our system is thefirst step towards showing the efficacy of agent supportedapproach to driving the dynamic roles across human operators and automated systems in the NextGen environment. We present some initial results from a pilot study using thissystem.

Authors

• Nathan Schurr
• Paul Picciano
• Janusz Marecki Milind Tambe

Keywords

• Aviation Transportation
• Human-agent Interaction
• Adjustable Autonomy
• Markov Decision Process