Decentralised Coordination through Local Message Passing
Alex Rogers
School of Electronics and Computer ScienceUniversity of Southampton
Overview
• Decentralised Coordination• Landscape of Algorithms
– Optimality vs Communication Costs• Local Message Passing Algorithms
– Max-sum algorithm– Graph Colouring
• Example Application– Wide Area Surveillance Scenario
• Future Work
Decentralised Coordination
Agents
• Multiple conflicting goals and objectives• Discrete set of possible actions• Some locality of interaction
Decentralised Coordination
Agents
Central point of control No direct communication Solution scales poorly Central point of failure Who is the centre?
Decentralised Coordination
Agents
Decentralised control and coordination through local computation and message passing.• Speed of convergence, guarantees of optimality,
communication overhead, computability
Landscape of AlgorithmsComplete
Algorithms
DPOPOptAPOADOPT
Communication Cost
Optimality
Iterative Algorithms
Best Response (BR)Distributed Stochastic
Algorithm (DSA) Fictitious Play (FP)
Greedy Heuristic
AlgorithmsPredictive Algorithms
Dr. David LeslieArchie Chapman
Michalis Smyrnakis
Maike Kaufmann
Dr. George Loukas
Probability Collectives
Message Passing
Algorithms
Sum-ProductAlgorithm
Sum-Product Algorithm
Variable nodes
Function nodes
Factor Graph
A simple transformation:
allows us to use the same algorithms to maximise social welfare:
Find approximate solutions to global optimisation through local computation and message passing:
Wide Area Surveillance Scenario
Dense deployment of sensors to detect pedestrian and vehicle activity within an urban environment.
Unattended Ground Sensor
Energy Constrained Sensors
Maximise event detection whilst using energy constrained sensors:– Use sense/sleep duty cycles
to maximise network lifetime of maintain energy neutral operation.
– Coordinate sensors with overlapping sensing fields.
time
duty cycle
time
duty cycle
Future Work• Continuous action spaces
– Max-sum calculations are not limited to discrete action space
– Can we perform the standard max-sum operators on continuous functions in a computationally efficient manner?
• Bounded Solutions– Max-sum is optimal on tree and limited
proofs of convergence exist for cyclic graphs– Can we construct a tree from the original
cyclic graph and calculate an lower bound on the solution quality?