1 / 12

Application of Multi-Agent Systems to Free Flight and ATM

Application of Multi-Agent Systems to Free Flight and ATM. What are “agents”? What role can they play in aviation? What are the most relevant technologies? What are future directions for theoretical research? What are future directions for applied research?. Roles for Agents in Aviation.

kiley
Download Presentation

Application of Multi-Agent Systems to Free Flight and ATM

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Application of Multi-Agent Systems to Free Flight and ATM • What are “agents”? • What role can they play in aviation? • What are the most relevant technologies? • What are future directions for theoretical research? • What are future directions for applied research?

  2. Roles for Agents in Aviation • Agents are software processes that can simulate decision-making and be adaptive • In cockpit: planning flight path, managing fuel, maintaining stability of flight, monitoring traffic or weather conflicts… • On ground (TRACON, ARTCC): planning trajectories, resolving conflicts, approach metering, handling emergencies, coordination with ground ops, airlines, etc.

  3. Agent Interactions Between Aircraft • The Vision: automatically formulate and negotiate En route and Terminal trajectories • En route: de-crowd trans-oceanic routes, permit airlines wider choices on climb/descent for efficiency, routing around weather cells • Terminal Area: efficient sequencing; handle speed variability better • Handle by on-board computers • Use datalink for Air/Air & Air/Ground comm.

  4. ATC only has to monitor, or occasionally arbitrate • Decentralized computing: reduce bottlenecks and decrease sensitivity to failures/attacks • Off-load approval of minor deviations • Still maintains ultimate authority

  5. What are agents? • Essential Characteristics: • Situated (can sense and take actions in dynamic environment) • Goal-oriented • Autonomous • Social (collaborative) • Types of Agents (abstract architectures) • Reactive (trigger rules) • Deliberative (reasoning, planning) • Cognitive (Mentalistic, BDI: beliefs, desires, intentions) • Utility-based, decision theoretic

  6. Collaboration Models • Negotiation protocols • Contract networks • Bids based on marginal utility • Share justifications and beliefs to compromise • Teamwork • Command hierarchies (with delegation) vs. distributed structure (load-balancing, consensus) • Key concepts: roles and responsibilities • Shared plans: implicit coordination, synchronization • Theory: joint intentions (for robust backup behavior)

  7. Concepts for Development of Multi-Agents for Free Flight • Strategic (trajectory planning/management) vs. Tactical (avoidance maneuvers) • Actionable decisions: • Alter flight path: heading, altitude, speed • Factors: weather, terrain, traffic • Constraints: fuel, speed/alt range • Preferences: time, fuel cost, comfort

  8. Distributed Constraint Satisfaction • Conflict detection – projected interferences • No designated leader with universal authority • Dynamic coalition formation • Initiating agent proposes a solution; others refine it • Negotiation by “argumentation” • State what is wrong with proposed solution and why • Communicate preferences as well as constraints • make up when behind schedule • minimize fuel consumption • maneuver limitations (safety, comfort) • Shared responsibility (with ground too)

  9. Role of Simulated “Mental Attitudes” • Intent – transmit more than position/vector • Desire to avoid weather, flight plan, will be turning north, descending due to turbulence, reason for deviation… • Beliefs • shared info (weather, congestion, aircraft emergencies) • common picture of situation • common knowledge: STAR’s, fixes, active runways, traffic patterns • manage uncertainty

  10. Prior Work on Multi-Agent Systems at Texas A&M • 1999-2001: University XXI program • Coordinated through CTSF at Ft. Hood • Designed TaskableAgents architecture to train brigade TOC staff officers by simulating interactions with battalion TOCs • Interoperated with OneSAF Testbed distributed combat simulation (STRICOM; DIS protocol)

  11. 2001-?: Army Research Lab • Funding to continue work on TaskableAgents • Add HLA-interoperability • Augment command-and-control (C2) reasoning • Add cognitive models of situation assessment

  12. 2000-2005: MURI (DOD/AFOSR), $4.3M • Multi-disciplinary University Research Initiative • Intelligent team and group training (e.g. DMT) • Merge agent technology and cognitive theories to find principled methods for using agents to improve human and team performance • Developing CAST Architecture: “Collaborative Agents for Simulating Teamwork” • Collaborators: • Richard Volz (TAMU, CS), John Yen (PSU, CS) • Wayne Shebilske, Pamela Tsang (Wright State; Dept. of Psychology)

More Related