Small Unit Operations SUO/PDA

1. Small Unit Operations SUO/PDA Austin Tate & David Wilkins AIAI & SRI International E-mail: a.tate@ed.ac.uk, wilkins@ai.sri.com www: http://www.aiai.ed.ac.uk/~arpi/SUO/

2. Small Unit Operations SUO/PDA SUO/PDA Objective Demonstrate capability relevant to a SUO/Situation Assessment System (SAS) environment to generate, refine, select, communicate, execute and repair plans across multiple echelons. Limited interfaces available on soldier-borne device. Two examples of COA plan generation and use: - Defensive - Halt an Attack in Restrictive Terrain; - Offensive - Military Operations in Urban Terrain. Schedule Advanced Planning Technology 2QCY99 - SUO Scenario and storyboard/demo script development. Walk-through of technical requirements, mockup of non-working parts. Produce demo script and scenarios. 3QCY99 - Technology and demonstration development first pass, interim demonstration. 4QCY99 & Q1CY00 - Full demonstration development. 1Q & Q2CY00 - Transition and communication of results to SUO contractor. Refinement of demonstration and technology. • Use results of DARPA/AFRL Planning Initiative (ARPI) and Planning & Decision Aids (PDA) work • Multi-Agent Planning Architecture (MPA) and O-Plan Systems Integration Architecture • SIPE-2/CPEF and O-Plan AI Planners • Mixed Initiative Planning Aids • Planning Domain Knowledge Acquisition Tools/Editors • Continuous Planning, Re-planning and Plan Repair • Planning Process Panels • Rich Shared Plan Representations

3. Objective • Demonstrate capability relevant to a SUO/Situation Assessment System (SAS) environment to generate, refine, communicate, execute and repair plans across multiple echelons. • Limited interfaces available on soldier-borne device. • Two examples of COA plan generation and use: • Defensive - Halt an Attack in Restrictive Terrain; • Offensive - Military Operations in Urban Terrain.

4. Approach • Use results of DARPA/AFRL Planning Initiative (ARPI) and Planning & Decision Aids (PDA) work • Multi-Agent Planning Architecture (MPA) and O-Plan Systems Integration Architecture • SIPE-2/CPEF and O-Plan AI Planners • Mixed Initiative Planning Aids • Planning Domain Knowledge Acquisition Tools/Editors • Continuous Planning, Re-planning and Plan Repair • Planning Process Panels • Rich Shared Plan Representations

5. Long-term Contributions to the Soldier • Fast generation of multiple distinct COAs, including ones the commander may not have considered. • Commander can explore more options in detail. • Avoid mistakes: uniformly high plan quality, even during high-stress crises. • Monitor plan execution and respond quickly to events, helping the commander modify the plan appropriately. • Provide relevant information to other echelons, allowing fast communication while preserving bandwidth.

6. Practical Issues/Challenges • Effort to acquire the knowledge base (KB). • KB will cover a small subset of an officer’s knowledge and will add value. • Effort commensurate with capability. • Reduce effort by limiting scenario, relying on human knowledge, etc. • Human can override PDA -- blind spots not fatal • Other DARPA programs address this problem in the larger scale • Assumptions about the world information available from sensors. • Rely only on information which the SUO/SAS can provide • Baseline: SALUTE reports and GPS data. • Input and output burden on soldiers when using SUO/PDA. • PDA would augment officer at time he would consult map or talk on radio • Voice input and limited interface modalities being considered by SUO contractor. • Sensor planning • PDA will plan for awareness requirements • PDA will respond to reports from SAS • Will not model low-level details of sensor operations, or plan their exact deployment

7. Integration/TIEs • Validate MPA by integrating several systems in DARPA Planning Initiative (TIE 97-1): INSPECT (ISI) OPIS (CMU) Advisable Planner (SRI) SIPE-2 (SRI) ACS (UMass) Process Panel (AIAI -UEdin) APAT (ISX) VISAGE (MAYA) • Domain is Air Campaign Planning • thousands of objects, several thousand nodes in each plan • plan down to support mission level (must allocate supporting resources) • air superiority objective only • targets grouped into networks which depend on other networks • network effectiveness is modeled quantitatively

8. DARPA TIE 97-1 Demonstrations • Sept 98 - EFX 98, Ft. Walton Beach FL • May 98 - ARPI Workshop, Monterey CA • Feb 98 - DARPA, Arlington VA • Dec 97 - JFACC PMR, San Pedro CA • Nov 97 - ARPI Workshop, San Francisco CA Increasing Capabilities

9. Technology Transition DARPA SUO program: -prototype planning and decision aid DARPA JFACC program: -building on Cypress, MPA, TIE 97-1 MAPVis

10. SUO Scenarios • Two SUO-SAS scenarios have been chosen: • Military Operations in Urban Terrain (MOUT): Operation San Roberto • Halt an Attack in Restrictive Terrain: Operation Golden Manacle • KA has been performed for both scenarios • Interim demo is in MOUT scenario • Main demo is in Halt an Attack scenario

11. Differences between the Two Scenarios MOUT • Offensive • Close confined urban terrain • Company-sized operation • Close combat • Foot soldier threat • Localized sensors • CO, PLT, SQ • Soldier-borne aids • Glance+select modalities Halt an Attack • Defensive • Restrictive wooded terrain • Battalion-sized operation • Wide area move planning • Mechanized threat • Wide area sensors • BN, CO, PLT • Workstation-based aids • Graphics+typing modalities

12. Overall Process from Receipt of Mission to Success In Advance During Mission After Action Deliberative Planning & Rehearsal En-route Rehearsal & Replanning Low Tempo Adaptation & Plan Repair High Tempo Monitoring & Plan Selection After Action Planning & Support Opportunities for PDA Support in MOUT

13. 2nd Plt 1st Plt OP “M” 3rd Plt 4th Plt MOUT Concept of Operations

14. Halt an Attack - Defensive Scenario • Halt a mechanized enemy regiment in restrictive terrain with a SUO battalion • Place obstacles to force enemy into engagement area • Use forward infantry to observe, channel, and delay • Rely on fire support to attrit • Channel enemy to Southwest • Use a CO(+) to defeat enemy in engagement area

15. Defensive Scenario - Plan Generation • BN plan/order is input to PDA • PDA produces plans for each CO and each PLT • KB covers following: • Observing avenues of approach (AAs) • Using sensors to provide and supplement observation and to provide security • Covering AAs with obstacles • Positioning units • Selecting a channelizing path for OPFOR • Nominating positions for fire support (FS) units • Selecting an engagement area (EA) in which to defeat channelized units • Preparing the EA for the battle

16. Battalion Planning & Execution System OPORD FRAGO Reports Company Planning & Execution System OPORD FRAGO Reports Platoon Execution Support System Domain Model Activity Templates & Constraints Main Demo - Halt an Attack Initial Battalion OPORD Creation and Editing Control Panel User Interfaces & C2 Process Management SUO/SAS System World Simulation and Scenario Event Generation AIAI Responsibility SRI Responsibility World Simulator

17. Domain Model Activity Templates & Constraints SUO/PDA Knowledge Acquisition Stage Knowledge Acquisition for Halt an Attack (SRI with AIAI) SIPE Acts Army CALL Bulletins Doctrine, SOP, TTP Process Modelling Methods & Tools Subject Matter Experts O-Plan TF Knowledge Acquisition for MOUT (AIAI with SRI) SUO 3 Repn. AIAI Responsibility SRI Responsibility

18. AIAI Contributing Technology Austin Tate with Jeff Dalton, John Levine, Peter Jarvis AIAI, University of Edinburgh 80 South Bridge, Edinburgh, UK E-mail: a.tate@ed.ac.uk Tel: +44 (131) 650 2732 www:http://www.aiai.ed.ac.uk/project/oplan http://www.aiai.ed.ac.uk/project/ix

19. AIAI Contributing Technology • Generation of multiple qualitatively distinct alternative COAs dependent upon alternative assumptions and advice about the situation. • Support for mixed-initiative incremental plan development, manipulation and use. • Situation-dependant plan repair as situation changes. • Systems integration framework for modular planning and plan analysis systems. • Management of planning and execution process - promotion of intelligent process management and workflow concepts.

20. AIAI Contributing Technology • Shared Models of Tasks, Processes and Plans • Issue-based Problem Solving • Constraint and Domain Management • Planning Process Panels • Web Delivery of Planning Facilities • Process Editor • Previous O-Plan Technology • New I-X Technology

21. Shared Plan Model - a rich plan representation using a common constraint model of activity (<I-N-CA>). Shared Task Model - Mixed initiative model of “mutually constraining the space of behaviour”. Shared Space of Options - explicit option management. Shared Model of Agent Capabilities - handlers for issues, functional capabilities and constraint managers. Shared Understanding of Authority - management of the authority to plan (to handle issues) and act which may take into account options, phases and levels.

22. Requirements Requirements Reports Reports Requirements Requirements Issue Handlers Reports Reports Processing Capabilities Interface Manager Controller Processing Platform(s) PlanWorld Viewers Task & Option Management Model Management Technical & World Viewers Constraint Managers Constraint Managers Data Base Manager Information Sources Plan State Issues Nodes Constraints Constraint Associator Mediators/Mapping Information Sources O-Plan -> I-X

23. Choose (IH) Issues or Implied Constraints I Do (IH) Node Constraints N Detailed Constraints Propagate Constraints CA C=Critical Constraints A=Auxiliary Constraints IH=Issue Handler (Agent Functional Capability) I-Plan and <I-N-CA> Plan State Plan Agenda Plan Entities Plan Constraints Space of Legitimate Plan Elaborations

24. SRI Contributing Technology David Wilkins, Tom Lee SRI International Artificial Intelligence Center Menlo Park, CA E-mail: wilkins@ai.sri.com Tel: 650-859-2057 www: http://www.ai.sri.com/~sipe http://www.ai.sri.com/~cpef

25. Domain Characteristics Intelligent Operations Management • Tasks are complex and open-ended • Operating environments are dynamic and possibly hostile • Complete and accurate knowledge of the world can never be attained • Full automation is neither possible nor desirable • Successful operation requires a mix of • user involvement and control • continuous planning • rapid response to unexpected events • dynamic adaptation of activities

26. CPEF Architecture MPA Messages