1. MokSAF: Agent-based Team Assistance for Time Critical Tasks Katia Sycara The Robotics Institute email: katia@cs.cmu.edu www.cs.cmu.edu/~softagents

2. MokSAF Multi-Agent system that assists human teams in time-critical military planning tasks. Team goal can be decomposed to two tasks per team member: –Coordinated resource allocation; –Geo-spatial planning. Two types of agents: –MokSAF Interface agents display individual and shared routes, and facilitate communication between team members. –Route Planning Agents (RPA) generate and/or critique routes for heterogeneous military platoons through a given landscape. Research Issues - to investigate different approaches for Human/Agent interaction when assisting with team goals.

3. MokSAF Team Goal Commanders coordinate individual platoons on a joint task. Each platoon starts in different location, but arrives at common rendezvous. Have to negotiate: –Composition of each platoon. –Route taken by each platoon. –Resource contention (e.g. Fuel). Commanders can: –Share details of platoon composition. –Share route details. –Re-negotiate rendezvous location and time. –Discuss strategy through typed messages. Arrive at rendezvous (145,122) by 18:00. Avoid: Io Township Stots Bridge Units Required: 3 M603A 3 Hummers 1 AAV-7 …

4. Agents within the MokSAF environment Consists of three different Task Agents which perform route planning. –Naïve RPA – critiques user defined routes –Autonomous RPA – fastest route between two points –Collaborative RPA – refines user defined routes Users interact via individual MokSAF Interface Agents. –Interface agent used to construct, view and share routes. –Commanders communicate with each other through their personal interface agent.

5. MokSAF Interface Agent Intangible Constraints Alpha’s Shared Route Bravo’s Shared Route Information about team- members routes, platoons… Tools for constructing routes for the Naïve and Collaborative agents

6. Naïve Route Planning Agent Input parameters: –A route represented as a sequence of points. Output parameters: –An annotated route (as a sequence of points). Naïve RPA critiques route: –Checks route validity –Identifies constraint violations Used by MokSAF to review and annotate the commander’s proposed route…

7. Autonomous Route Planning Agent Input parameters: –Start and end points of a route. Output parameters: –The fastest route between these points (represented as a sequence of points). Factors considered when generating route: –The platoon characteristics w.r.t. type of terrain. –The volume of fuel required to successfully achieve the goal. Used by MokSAF to generate fastest (optimal) route…

8. Collaborative Route Planning Agent Input parameters: –A corridor represented as a sequence of points and a width. Output parameters: –The fastest route constrained by the corridor (represented as a sequence of points). Behavior: –User retains (some) control of route - as with Naïve RPA –Generation of (localized) optimal route - as with Collaborative RPA Used by MokSAF to refine and optimize the commander’s preferred route…

9. Experimental Tasks 3 commanders start at different locations on the map. Each commander plans a route to a single, shared rendezvous point that is to be reached by a given deadline. Each commander may need to: –Go via one or more fuel depots to refuel. –Avoid constrained regions, but traverse desirable areas. –Coordinate routes, allocation of vehicles and use of fuel depots with other commanders. –Suggest alternatives to the proposed rendezvous point or agreed meeting time. Coordination occurs via communication and plan sharing.

10. Experimental Results The results suggest that commanders were able to identify faster (and more economic) routes with the aided (ie autonomous or collaborative) condition, and hence shared routes faster with team mates. However, commanders complained about lack of control over the final route with the autonomous RPA: –Intangible constraints used to coerce RPA. –Desired route obtained iteratively through trial & error.

11. Experimental Results Commanders identified better routes faster with the Cooperative or Autonomous RPA, than with Naïve RPA –This resulted in better coordination with team mates. Users liked feedback & control gained from Naïve RPA, but found manual generation of routes tedious and slow. Users complained about lack of control over the final route when using Autonomous RPA: –Intangible constraints used to coerce RPA. –Desired route obtained iteratively through trial & error. Users expressed preference for Cooperative RPA, but use of this agent was not optimal for all scenarios.