1. AGENTS for GENERAL AVIATION FREE FLIGHT Primary goal is to facilitate Free Flight for General Aviation (GA) –A new way of managing air traffic –Departure from the highly structured system to a more flexible, distributed system –Individual aircraft allowed to change route, speed and altitude, within limits –More responsibility for aircraft separation safety rested on pilots –Ultimate decision making authority still lies with the air traffic controllers Intelligent Agents can help detect and resolve conflicts

2. What are agents? Essential Characteristics: –Situated (can sense and take actions in dynamic environment) –Goal-oriented –Autonomous –Adaptive (learning) –Social (collaborative) Types of Agents (abstract architectures) –Reactive trigger rules –Deliberative reasoning, planning –Cognitive (Mentalistic) beliefs, desires, intentions –Decision theoreticutility functions

3. Agent Architectures Production Systems –Reactive, trigger rules, CLIPS, SOAR Search Algorithms: A* (WX agent) Planning Algorithms Hierarchical Task Networks (Retsina, TRL) Decision Theoretic –Markov Decision Processes, maximize payoff Cognitive (Mentalistic) –BDI: beliefs, desires, intentions –JACK, PRS, dMARS

4. 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.

5. Agent Interactions Between Aircraft Agents in FMS automatically formulate and negotiate En-route and Terminal trajectories –En route: de-crowd routes or traditional jetways between fixes (hub-and-spoke), more direct flights and custom flight plans, preferences –Terminal Area: efficient sequencing; handle speed variability better Assume communication via ADS-B Allow involved aircraft to determine mutually acceptable compromise, rather than relying on solution by ATC

6. Conflict detection: projected loss-of-separation (~10-15 minutes out) –based on current trajectories/flight plans/intent –range of ADS-B (~160nm) ATC only has to monitor, or occasionally arbitrate –Decentralized computing: reduce bottlenecks and decrease sensitivity to failures/attacks –Off-load approval of minor deviations –ATC Still maintains ultimate approval authority for now, agent solutions will only be advisory, minimize disruption of current procedures

7. Collaboration Models Consensus Protocols –Voting/ranking schemes Negotiation protocols –Make incremental concessions until reach compromise Distributed Constraint Satisfaction –Global knowledge consistency Teamwork –Hierarchical vs. distributed structures –Key concepts: roles and responsibilities –Shared plans: implicit coordination, synchronization –Theoretical basis: Joint Intentions (Other methods: contract networks, auctions...)

8. 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 Role of Simulated “Mental Attitudes”

9. 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

10. CD&R MODULE Logic Flow Control for CD&R Module

11. Shared responsibility (with ground too) Utility function: Flight Plans => score 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) Monotonic Concession Protocol (Zlotkin and Rosenschein ) –define a finite set of alternative trajectories –each agent ranks trajectories by utility, proposes best –take turns proposing next best deal till utilities match Negotiation

12. TRL Agents “Task Representation Language” Developed at Texas A&M Comp. Sci. Dept. –contact: ioerger@cs.tamu.edu knowledge bases –declarative: rule base (“domain knowledge”) –procedural: plans/methods for achieving goals connection to simulator(s) –read state information –trigger actions agents can communicate with each other

13. Example Task Description (task flight-plan-1 () (method (sequence (takeoff KCLL 16) (climb-out 3000) (turn-heading 350) (fly-direct-to KCNW) (descend 500) (land KCNW 17L)))) invoke sub-task command to simulator Things that can be added: interaction with ATC (set new way-points, altitudes...) handling developing weather, emergencies

14. TRL agent sensing messages JARE KB: facts & Horn-clauses Simulation operators results assert, query, retract messages APTE Algorithm TRL Task Decomposition Hierarchy Process Nets Other Agents TRL KB: tasks & methods TRL Agent Architecture

15. The SATS Airport Controller How to simulate this with agents? –encode the formal protocol as plans in TRL AMM agent - simple task, 1st come-1st serve pseudo-ADS-B=TCP/IP (must test robustness of protocol w.r.t. communications failures) test empirically with various scenarios –arbitrary number of aircraft –effects of timing, positions, speeds... –test handoff from ATC, entry to SCA –arrival/departure frequencies (Poisson distr.)