I -Globe: Distributed Planning and Coordination of

1. I-Globe: Distributed Planning and Coordination of Mixed-initiative Activities (thanks to ARO N62558-06-P-0353) I-GLOBE: Distributed Planning and Coordinationof Team-oriented Activities in a Dynamic EnvironmentQ3 review report Antonín Komenda, Jiří Vokřínek, Michal Pěchouček Agent Technology Center, Gerstner Laboratory,Czech Technical University in Prague Gerhard Wickler, Jeff Dalton, Austin Tate Artificial Intelligence Applications InstituteSchool of Informatics, The University of Edinburgh KSCO 2009, 31st March and 1st April 2009, Southampton, UK

2. Content of the briefing • Integration of ACROSS multi-agent simulation scenario (previously funded by CERDEC, developed at ATG) and PACIFICA (previously DARPA funded scenario, developed at AIAI)

3. IGLOBE Scenario • disaster relief scenario, based on Pacifica, extended with • low-level planning & simulation, rich behavioural models of the actors • detailed model of the environment, destructible entities and others • Dynamic interaction among the actors (=resources) • discrete simulation,100x accelerated,regionscale~ 300km,1 time tick ~ 100ms • PRIMITIVE ACTIONS • build structure, • heal injured, • take snapshot, • move to/fly to, • load resources/unit, • unload resources/unit, • transport unit, • transport resources, • handover goal, • follow unit. • GOALS • build houses, • heal injuries, • build mobile hospital, • explore area, • provide transportation, • unit tracking. • ACTORS • commanders, • ground transport units, • aerial transport units, • surveillance UAV, • surveillance HTUAV, • surveillance helicopter, • builders, medical doctors, • construction resource providers, • medical material providers.

4. Research Issues and Assumptions • Assumptions: • actors may have conflicting objectives, they have their private agenda • there is little predifined planning hierarchy, it emerges from interaction • resources overbooking can occur and execution may cause replanning • planning process is processed on three levels: • strategic: setting and planning set of mission objectives • tactical: delegation and task assignment • individual: planning individual activity (such as trajectory) • The key research concepts we have been focused on are: • distributed planning : responsibility allocation, collaborative plan formation, plan merging and plan synchronization • distributed resource allocation: negotiation among t the actors about available resources and their utilization in time • distributed plan execution: distributed execution of plans, plan accomplishment monitoring and negotiation oriented replanning

5. Approach • System integration: • I-X, AGLOBE, ACROSS and AgentFly (in parts funded by DoD) • Theoretical multi-agent concepts: • Extension of the concept of social commitments, as the specific knowledge representation for integrating the planning and multi-agent resource allocation • Experimental validation on a multi-agent simulation: • Studying how the social commitments can contribute to increased robustness of the whole distributed plan.

6. HTN Planning in I-X • I-X provides an issue-handling style of architecture, with reasoning and functional capabilities provided as plug-ins. Also via plug-ins it allows for sophisticated constraint management, and a wide range of communications and visualisation capabilities. • A More Intelligible Framework for Collaborative Planning: • Human relatable and presentable objective statements, advice and plan representations for outer levels • Detailed search engines, constraint solvers, analyzers and simulations act in this framework in an understandable way to provide feasibility checks, detailed constraints and guidance • Sharing of processes and process products via <I-N-C-A>

7. I-X Architecture Integration The I-X architecture is used in I-Globe on several levels: • the I-X I-Plan planner is used as the main HTN planner of the system • each entity uses its own instance • Multi-layer Planning Architecture uses the I-Plan on the Strategic Layer • the Commander actors uses I-P2 (I-X Control Panel) • allows mixed-initiative planning • allows monitoring based on the state subscription interfaces and protocols • the Sense Maker actor uses I-P2 • for tasking of the Commander actors • as a superior interface for the I-X Control Panels of the Commanders

8. AGENTFLY Accelerated A* space/time planning Planning problem in AGENTFLY is to find an optimal flight plan (sequence of elements defining the smooth path for the UAV) respecting all physical airplane constraints and fulfils specified mission (ordered sequence of way-points including optional time and cruise speed limits). The given flight plan can utilize only available airspace for the airplane (respects defined no-flight zones). Implemented as two phases planning with backtracking: • spatial planning – prepares the spatial part (3D plan) of the flight plan • A* with state space is given by chaining of usable flight plan elements • accelerated by using hierarchical dynamic size of the searching step • time planning – adds the fourth dimension to the flight plan: • 3D plan and optional-waypoint constraints are transformed to 1D problem (distance in time) • identify unsolvable parts and re-invoke appropriate backtracking to spatial planning (e.g. cannot slow down more -> insert holding orbits)

9. Accelerated A* space/time planning Optimized airspace definition based on combination of transformed components: • octant tree structure • height map • composition of boxes, ellipsoids and cylinders Fast operation for: • test ellipsoid intersection • test corridor box intersection

10. AGENTFLY Demonstration

11. AGLOBE simulation/integration framework • Advanced, scalable and robust multi-agent simulation environment, developed under partial support of AFRL/EOARD and CERDEDC/ARO. • compared to other multi-agent platforms, AGLOBE offers • superior performance, low overhead, distributed load balancing • full agent advanced agent mobility and computational reflection • environmental modeling and scenario simulation support • complex visualization environment • AGLOBE is suitable for development of large scale simulation scenarios and can be seamlessly ported to real-world deployment platforms using a standard methodology. key functionalities: • Modeling and simulation » Demo/visualization support • Scalable experimental testing » Hardware deployment support • Deployment in air-traffic control, distributed diagnostics, adversarial planning, design process modeling, collective underwater robotics, etc

12. Social Commitments • The integration between HTN, MAP, and multi-agent simulation (or operation monitoring) is implemented by means of social commitments – derived from social commitments suggested by [Jennings]. • Social commitment is a special knowledge structure that represents a conditional intent of an actor to implement an action (primitive or complex) and specifies nontrivial rules of decommitment. • Individual commitments is often used for intentional modeling and programming of intelligent agents. • Joint commitments represent an agreement between two or more actors with respect to either a shared goal or mutually dependent individual goals. • integration between human, robots and different planning tools • dynamic adjustment and control of reliability of interaction and • flexible, multi-level re-planning and dynamic coordination

13. Programming commitments in I-GLOBE • Use of commitments in I-GLOBE is motivated by establishing a transparent and efficient coordination mechanism for distributed planning in I-GLOBE • Use of formal analysis of the individual commitments remains an option • Social commitment as a knowledge containing: • task specification, preconditions and postconditions, • references to other commitments (oriented commitment graph), • decommitment rules organized in 3 explicit levels: • Full decommitment– mere report of a failure to achieve the commitment: termination condition, blind decommitment, JPG – Joint persistence goal • Commitment Relaxation – individual change of the quality of service, to be proposed to the service requestor (preagreed) • Commitment Delegation – search for another actor who is available to implement the failed commitment • explicit reference to the commitment participators

14. Programming commitments in I-GLOBE • Commitments and planning? In two phases: • Negotiation about commitment proposals • multi-criteria optimization: quality of service (due time, costs) vs. decommitment flexibility • e.g. low flexibility commitments disallow commitment relaxation while high flexibility commitments try to avoid full decommitment • Implementation of the commitment selection and confirmation • deployment of the decommitment rules (either fixed or subject of further negotiation (such as commitment delegation)

15. Evaluation - Different decommitment rules Evaluation - Execution Time

16. Evaluation - Different decommitment rules Evaluation - Execution Time

17. Evaluation - Execution time Evaluation - Execution Time

18. Evaluation - Negotiation Complexity Evaluation - Negotiation complexity

19. IGLOBE Demonstration

20. IGLOBE Demonstration

21. Future work Summary • commitment-based coordination and AA* planning is further developed in: • TACTICTAL AGENTFLY (W911NF-08-1-0521_1312AM01): Intelligent Software Agent Control of Combined UAV Operations for Tactical Missions (CERDEC and EOARD co-funded) • MANET AGENTFLY (W911NF-08-1-0505_1317CE01): Agent-based Control for Connectivity Maintenance of Tactical MANETs (CERDEC funded) • Based on IGLOBE ACROSS scenario the DEEPA testbed has been implemented: • funded by EOARD and AFRL (FA8655-07-1-3083) • used for testing adversarial planning • integration of distributed constrain satisfaction techniques in collaboration with Drexel University and ACIN • new critical challenge: how to integrate an automated scene reconstruction from 3D with the distributed planning and replanning capabilities – the white-paper for ONR in preparation (in collaboration with CMP) • interaction in semi-trusted environment – adjustable privacy

22. Tactical AGENTFLY