Sync DCOP-MST Simulator (version 5)

Models

DCOP

Distributed Search.

DCOP_MST

Distributed Search with Mobile Sensor Teams.

Algorithms

• Random
• DSA_MST
• CADSA
• DSSA
• Max-sum_MST
• Max-sum_MST with breakdowns
• CAMS

Big Experiments

• 7 aforementioned algorithms
• 200 steps to each problem
• 20 problems per map per targets' type
• 4 maps: empy, random, warehouse, room
• 2 types of targets: static and dynamic
• 20 small iterations for CAMS
• 2 metrics: collision (col) metric and remained coverage requirement (rcr) metric

Agents:

• number of agents is fixed for everything (in small maps - 20, in big maps - )
• SR of agents is fixed for everything
• MR of agents is fixed for everything
• Cred of agents is fixed for everything

Targets:

• number of targets is fixed for everything (in small maps - 10, in big maps - )
• Req of targets is fixed for everything
• Position of targets fixed while static
• Position of targets changes every 40 steps while dynamic

For one type of target and one map there is the following data structure:

import numpy as np
# per type of targets per map: 
data_structure_for_json = { 
    'alg_name':  # (7 of them)
    np.zeros((200, 20))  # [[200(steps) x 20(problems) matrix]]
}

map	Static Targets	Dynamic Targets
empty (empty-48-48)
random (random-32-32-10)
warehouse (warehouse-10-20-10-2-1)
room (lt_gallowstemplar_n)

Credits

• blog | Python – All possible pairs in List