ADAPT: Abstraction Hierarchies to Succinctly Model Teamwork

1. ADAPT: Abstraction Hierarchies to Succinctly Model Teamwork Meirav Hadad1, Avi Rosenfeld2 1Research Division, Elbit Systems Ltd, Rosh Ha'Ayin 48091, Israel 2Department of Industrial Engineering, Jerusalem College of Technology, Jerusalem, Israel Meirav.Hadad@elbitsystems.com, rosenfa@jct.ac.il

2. Modeling the Simulator’s Teamwork • How to Model Teamwork? • SharedPlans • Steam • Taems • Teamcore • Bite • Limitations of Previous Approaches: • Often unclear how to create formal rules • How can we encode the expert’s knowledge • In previous approaches all rules needed to be defined in advance • Very limiting, especially when dynamics must be considered

3. Issues Specific to Simulation • Simulator often has relatively limited resources • What is the size of the model? • What is the memory amount needed per agent? • How fast is the processing time? • Real time required! • Our goal is to simulate hundreds of agents

4. Teamwork Example 1 2 3 4

5. Our Solution: ADAPT • Autonomous Dynamic Agent Planning for Teamwork • Creates two Hierarchical Planners – a group and task planner • Uses abstract search techniques to generally define hierarchies • Only during run time are the exact constraints addressed (elaboration)

6. 2 1 3 4

7. ADAPT: What is Novel? • ADAPT is an A.I. Teamwork Engine • Uses 3 steps • Branching Step (very big number of states) • Refinement Step (add the constraints) • Pruning Step (very small model)

8. Branching Step Identifies possible methods for expanding partial plan • Get all the possible methods from library of task methods and group methods • Example of possible task methods for air attack Option 2 Option 1

9. Refinement Step Adding information based on constraints (DCOP) 1. Receive the set of the all possible task methods 2. Receive the set of the all possible group methods 3. Match the group methods to the task methods: 1. make intersection between the constraints of task and group methods 2. match the sub-tasks’ constraints of to sub-groups, create sub-constraint vector c1, c2, …, cn for each matching 4. Each group member checks its matching level for each sub-constraint vector and put its matching grade at each method

10. Refinement Step

11. Pruning Step Remove Unpromising Candidates 1. Assignment and Matching Tables are built for each method and each method is graded according to the Task Assignment algorithm 2. The method with the greatest assignment grade is selected to be elaborated 3. If successful, association is done

12. Pruning Step

13. A High Level Overview of the Simulator Real Time Control Group KB Editor Group DB Task KB Editor Task DB

14. General Description of an ADAPT Agent AI Engine Cooperation Level Real Time Control Action recommendation Decision Maker Perception Failures Handler Re-planning\ Reassignment Association World State Constraints Constraints Group plan Task Plan Task Planner Group Planner Task KB Group KB

15. Teamwork State Results

16. Actual Run Times

17. Conclusion and Future Work • ADAPT = teamwork into group and task planners • Uses abstract search to build model incrementally • Large savings in teamwork model size • Example how ADAPT used in a realistic simulation domain • How can ADAPT be used in other domains? • Are the savings dependent on domain specifics?