Decentralised Coordination through Local Message Passing Alex Rogers School of Electronics and Computer Science University of Southampton [email protected].

Decentralised Coordination through Local Message Passing

 Alex Rogers

School of Electronics and Computer ScienceUniversity of Southampton

[email protected]

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

Maximise Social Welfare:

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:

Graph Colouring

Graph Colouring Problem Equivalent Factor Graph

Graph Colouring

Equivalent Factor GraphUtility Function

Optimality

Communication Cost

Robustness to Message Loss

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

Energy-Aware Sensor Networks

Applications

Combat Management SystemInsyte

Coordinating Mobile SensorsRuben Stranders

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?