1. Technical Advisor : Mr. Roni Stern Academic Advisor : Dr. Meir Kalech Team members :  Amit Ofer  Liron Katav Project Homepage : http://mpasproject.weebly.com

2.  Path finding refers to the problem of searching the shortest route between two points.  Multi-agent path finding problem involves navigating units from their starting position to their respective goals, whilst going around any static obstacles and other moving units along the way.

3.  The problem is becoming increasingly important in many real-life applications, including motion planning in robotics, air traffic control, vehicle routing, military operation planning and computers games.

4.  The standard algorithm for this problem is the A-star (A*) algorithm.  A star is an extension of Dijkstra’s algorithm, A* achieves better performance (with respect to time) by using heuristics.

5.  The problem with the A-star algorithm is that its complexity grows exponentially with the number of mobile units on the map, making it not practical for real time applications.  For this reason the modern research focuses on finding a more efficient algorithms that solve the multi-agent pathfinding problem.

6.  Our goal is to develop a simulator that will help to observe the different behaviors and compare the performance of various multi-agent pathfinding algorithms.  The algorithms that will be tested are:  A-star (A*), 1968.  Hierarchical Cooperative A* (HCA*),2005.  Operator Decomposition + Independence Detection, 2010.

7.  The algorithms will be tested on two environments:  Grid map – a tiled based map where each unit can move to one of the 8 adjacent tiles.  Geographical map – a real world map where the mobile units are limited to moving on the roads.

8. Geographical map environment Grid map environment MPAS

9. Algorithm Layer Presentation layer Controller Layer -Method Invocation -Events User input View Changes Input State Change

10.  Choose the number of agents  In the grid map environment:  Choose the size of the grid map  In the geographical map environment:  Choose the map  Load grid maps  Save grid maps  Clear map  Sets the starting and finishing cells for each agent  Set blocking cells

11.  Choose the algorithm to be tested  Choose the heuristic to be used  Start the simulation  Stop the simulation  Running the simulation Step by step  Generate random scenario  Restart simulation

12.  Speed  The system should launch in less than 1 minute.  It gives an output in no more than 15 minutes (for an average problem’s size).  Capacity  Up to 1 Million vertices (1000 *1000 on grid or 1 Million on geographical-map)  Up to 100 agents that will run simultaneously.  Portability  The system should operate on Linux and Windows (XP/Vista/7).  The system should be able to run on a standard pc computer (though calculation times may vary according to system specs).

13.  Usability  The system GUI should be user-friendly and easy to use.  The system should be simple to manage for the common user.  The learning pace of the system should be quick.  Availability  The system should be able to operate at any time of day and no matter the amount of applications running at the background of the Operating System.

14.  Extensibility  All algorithms will implement a predefined interface. Thus the simulator will be easy to extend by adding more algorithms that will implement this interface.  Platform Constraints  The application will be developed in Java.  The computer that will run the system should not be older than 3 years and include JRE and java version 1.6 or higher.

17. ARD Prototype v1.0 includes : A-star implementation with 2 agents on a grid environment

18.  A- Star alorithm http://en.wikipedia.org/wiki/A_star http://en.wikipedia.org/wiki/A_star  D. Silver, 2005. Cooperative Pathfinding.  T. Standley, 2010. Finding Optimal Solutions to Cooperative Pathfinding Problems.

19. Thank you for listening