Technical Advisor : Mr. Roni Stern
Academic Advisor : Dr. Meir Kalech
Team members :
 Amit Ofer
 Liron Katav
Project Homepage :
http://mpasproject.weebly.com
 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.
 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.
 The
standard algorithm for this problem is the
A-star (A*) algorithm.
is an extension of Dijkstra’s algorithm, A*
achieves better performance (with respect to
time) by using heuristics.
 A star
 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.
 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.
 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.
MPAS
Geographical
map
environment
Grid map
environment
User
input
State
Change
Controller
Layer
View
Changes
Presentation
layer
-Method Invocation
-Events
Input
Algorithm
Layer
 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
 Set blocking cells
each agent
 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



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).

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.

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.

ARD

Prototype v1.0 includes :
• A-star implementation with 2 agents on a grid
environment
A- Star alorithm
http://en.wikipedia.org/wiki/A_star
 D. Silver, 2005. Cooperative Pathfinding.
 T. Standley, 2010. Finding Optimal Solutions to

Cooperative Pathfinding Problems .
Thank you for listening