© 2010 by Elbit Systems | Elbit Systems Proprietary ADAPT: Abstraction Hierarchies to Succinctly Model Teamwork Meirav Hadad 1, Avi Rosenfeld 2 2 Department of Industrial Engineering, Jerusalem College of Technology, Jerusalem, Israel 1 Research Division, Elbit Systems Ltd, Rosh Ha'Ayin 48091, Israel

© 2010 by Elbit Systems | Elbit Systems Proprietary 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

© 2010 by Elbit Systems | Elbit Systems Proprietary 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

© 2010 by Elbit Systems | Elbit Systems Proprietary Teamwork Example 4

© 2010 by Elbit Systems | Elbit Systems Proprietary 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 ) 5 Our Solution: ADAPT

© 2010 by Elbit Systems | Elbit Systems Proprietary

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) 7

© 2010 by Elbit Systems | Elbit Systems Proprietary 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 1 Option 2

© 2010 by Elbit Systems | Elbit Systems Proprietary 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 c 1, c 2, …, c n for each matching 4. Each group member checks its matching level for each sub- constraint vector and put its matching grade at each method

© 2010 by Elbit Systems | Elbit Systems Proprietary Refinement Step

© 2010 by Elbit Systems | Elbit Systems Proprietary 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

© 2010 by Elbit Systems | Elbit Systems Proprietary Pruning Step

© 2010 by Elbit Systems | Elbit Systems Proprietary A High Level Overview of the Simulator 13 Group KB Editor Task KB Editor Real Time Control Group DB Task DB

© 2010 by Elbit Systems | Elbit Systems Proprietary World State AI Engine Cooperation Level Decision Maker Association Task Planner Group Planner Constraints Task Plan Constraints Group plan Group KB Task KB Real Time Control Failures Handler Perception Action Re-planning\ Reassignment recommendation General Description of an ADAPT Agent

© 2010 by Elbit Systems | Elbit Systems Proprietary Teamwork State Results BITEADAPT MaxADAPT Average # of AgentsTaskGroupTaskGroupTaskGroup

© 2010 by Elbit Systems | Elbit Systems Proprietary Actual Run Times

© 2010 by Elbit Systems | Elbit Systems Proprietary 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? 17 Conclusion and Future Work