Company Template

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Agent-Based Decentralised Control of Complex
Distributed Systems  
Alex Rogers School of Electronics and Computer
Science University of Southampton acr_at_ecs.soton.ac
.uk http//users.ecs.soton.ac.uk/acr/
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Contents

• Agent-Based Decentralised Control
• Cooperative Systems
• Local Message Passing Algorithms
• Max-sum algorithm
• Graph Colouring
• Wide Area Surveillance Scenario
• Competitive Systems
• Game Theory
• Mechanism Design
• Eliciting Effort in Open Information Systems
• Decentralised Energy Systems

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Electronics and Computer Science

• 5 for Electrical and Electronic Engineering
• 5 for Computer Science
• 100 academic staff
• 36 professors
• 150 research fellows
• 250 PhD students
• Research grant income
• 15 million per annum
• 10 million from UK Research Councils

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Intelligence, Multimedia and Agents Research Group

• Design and application of computing systems for
  complex information and knowledge processing
  tasks
• Agent-Based Computing
• Digital Libraries
• Decentralised Information Systems
• E-Business Technologies
• Grid and Distributed Computing
• Human Computer Interaction
• Web Science
• Knowledge Technologies
• Trust and Provenance

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Contents

• Agent-Based Decentralised Control
• Cooperative Systems
• Local Message Passing Algorithms
• Max-sum algorithm
• Graph Colouring
• Wide Area Surveillance Scenario
• Competitive Systems
• Game Theory
• Mechanism Design
• Eliciting Effort in Open Information Systems
• Decentralised Energy Systems

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Agent-Based Decentralised Control

• Multiple conflicting goals and objectives
• Discrete set of possible actions

Agents
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Agent-Based Decentralised Control

• Multiple conflicting goals and objectives
• Discrete set of possible actions

Sensors
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Agent-Based Decentralised Control

• Multiple conflicting goals and objectives
• Discrete set of possible actions
• Some locality of interaction

Agents
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Agent-Based Decentralised Control

• Multiple conflicting goals and objectives
• Discrete set of possible actions
• Some locality of interaction

Maximise Social Welfare
Agents
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Agent-Based Decentralised Control

• Cooperative Systems
• All agents represent a single stakeholder
• We have access to these agents (closed system)
• We can design the strategies that the agents
  adopt and the mechanisms by which they interact
• Competitive Systems
• Agents represent multiple stakeholders
• We can not directly influence the strategies of
  the agents (open system)
• We can only design the protocols and mechanisms
  by which they interact

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Cooperative Systems

• Decentralised control and coordination through
  local computation and message passing.
• Speed of convergence, guarantees of optimality,
  communication overhead, computability

• No direct communication
• Solution scales poorly
• Central point of failure

Central point of control
Agents
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Landscape of Algorithms
Optimality
Complete Algorithms DPOP OptAPO ADOPT
Message Passing Algorithms Sum-Product Algorithm
Iterative Algorithms Best Response
(BR) Distributed Stochastic Algorithm (DSA)
Fictitious Play (FP)
Greedy Heuristic Algorithms
Communication Cost
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Sum-Product Algorithm
Find approximate solutions to global optimisation
through local computation and message passing
A simple transformation allows us to use the
same algorithms to maximise social welfare
Factor Graph
Variable nodes
Function nodes
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Graph Colouring
Graph Colouring Problem
Equivalent Factor Graph
Agent
function / utility
variable / state
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Graph Colouring
Equivalent Factor Graph
Utility Function
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Max-Sum Calculations
Variable to Function Information aggregation
Function to Variable Marginal Maximisation
Decision Choose state that maximises sum of all
messages
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Graph Colouring
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Optimality
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Communication Cost
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Robustness to Message Loss
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Hardware Implementation
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Wide Area Surveillance Scenario
Dense deployment of sensors to detect pedestrian
and vehicle activity within an urban environment.
Unattended Ground Sensor
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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.

duty cycle
time
duty cycle
time
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Energy-Aware Sensor Networks
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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?

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Contents

• Agent-Based Decentralised Control
• Cooperative Systems
• Local Message Passing Algorithms
• Max-sum algorithm
• Graph Colouring
• Wide Area Surveillance Scenario
• Competitive Systems
• Game Theory
• Mechanism Design
• Eliciting Effort in Open Information Systems
• Decentralised Energy Systems

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Competitive Systems

• Controlling open competitive systems is much more
  difficult
• Global credit crisis
• Key challenges
• Understanding the emerging macroscopic properties
  of a system of selfish competitive agents
• GAME THEORY
• Designing protocols and rules of the game such
  that these macroscopic properties are desirable
• COMPUTATIONAL MECHANISM DESIGN

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Game Theory

• For a given game
• What action should a rational player take?
• What is the equilibrium action of all players?
• Nash equilibrium

A Beautiful Mind Genius and Schizophrenia in
the Life of John Nash Sylvia Nasar Faber and
Faber
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Nash Equilibrium

• Two strategies s1 and s2 are in Nash equilibrium
  if
• under the assumption that agent i plays s1, agent
  j can do no better than play s2 and
• under the assumption that agent j plays s2, agent
  i can do no better than play s1.
• Neither agent has any incentive to deviate from a
  Nash equilibrium

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Nash Equilibrium
Column Player Column Player Column Player
LEFT MIDDLE RIGHT
Row Player UP 4 , 3 5 , 1 6 , 2
Row Player MIDDLE 2 , 1 8 , 4 3 , 6
Row Player DOWN 3 , 0 9 , 6 2 , 8
NE
1
3
2
4
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Computational Mechanism Design

• Mechanism design concern the analysis and design
  of systems in which the interactions between
  strategic, autonomous and rational agents leads
  to predictable global outcomes.
• Design interactions to ensure the system has
  desirable and predictable Nash equilibrium
• Computational mechanism design
• Limited communication
• Incomplete information
• Bounded computation

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Nash Equilibrium
Column Player Column Player Column Player
LEFT MIDDLE RIGHT
Row Player UP 4 , 3 5 , 1 6 , 2
Row Player MIDDLE 2 , 1 8 , 4 3 , 6
Row Player DOWN 3 , 0 9 , 6 2 , 8
NE
1
3
2
4
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First Price Auction

• Desirable properties
• Efficiency
• Allocation
• Item assigned to the highest bidder
• Payment
• Pay bid ( )
• Bidding strategy
• Shade bid
• Bayes Nash

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Second Price (Vickrey) Auction

• Desirable properties
• Efficiency
• Allocation
• Item assigned to the highest bidder
• Payment
• Pay second bid
• Bidding strategy
• Bid true valuation
• Dominant strategy

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Open Information System

• Information buyer requires a prediction of an
  uncertain
• Tomorrows temperature
• Requires certain minimum precision or
  certainty

c(?)
?
c(?)

• Identify cheapest provider
• Make prediction of precision of at least ?0
• Truthfully report this prediction to buyer
• Ensure providers utility is positive in
  expectation

?
?0
c(?)
?
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Two Stage Mechanism

• Two stage Mechanism
• Ask information producers to declare their costs
• Ask cheapest producer to make measurement and
  reward him with a payment using a strictly
  proper scoring rule calculated from the second
  lowest cost
• Payment is made once the event is verified
• Desirable system wide properties
• Dominant strategy to truthfully declare costs
• Information buyer can always identify cheapest
  supplier
• Dominant strategy to commit effort and truthfully
  reveal prediction

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Challenges

• Solution concepts
• Mechanisms with dominant strategy solutions are
  rare
• How do we automate the design process?
• Decentralised Mechanisms
• Remove need for a central auctioneer
• Payment Free Mechanism
• Non-transferable utility
• Induce cooperative behaviour through reciprocity
• Iterated Prisoners Dilemma
• Trust and reputation models
• Match making mechanisms to pair producers and
  buyers

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Contents

• Agent-Based Decentralised Control
• Cooperative Systems
• Local Message Passing Algorithms
• Max-sum algorithm
• Graph Colouring
• Wide Area Surveillance Scenario
• Competitive Systems
• Game Theory
• Mechanism Design
• Eliciting Effort in Open Information Systems
• Decentralised Energy Systems

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2016 Zero Carbon Home
Wireless Sensors
Appliances
Flywheel Storage
Micro-CHP
Plug-in Hybrid
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Energy Exchange
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Research Questions

• How to coordinate energy use and make optimal
  energy trading decisions within the home to
  minimise energy consumption / costs?
• Load management through smart appliances
• Predicting load (occupancy, activity, weather
  conditions)
• Understanding and learning thermal
  characteristics of home
• Price prediction in external and local markets
• Optimal use of storage devices
• Optimal decisions to buy electricity / use CHP

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Research Questions

• What protocols and trading mechanisms generate
  desirable system wide properties?
• Stable, predictable and low prices
• Minimise CO2 emissions through flattening demand

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Publications

• Farinelli, A., Rogers, A., Petcu, A. and
  Jennings, N. R. (2008) Decentralised Coordination
  of Low-Power Embedded Devices using the Max-Sum
  Algorithm. In Proceedings of the Seventh
  International Conference on Autonomous Agents and
  Multi-Agent Systems (AAMAS 2008), pp. 639-646,
  Estoril, Portugal.
• Papakonstantinou, A., Rogers, A., Gerding, E. and
  Jennings, N. (2008) A Truthful Two-Stage
  Mechanism for Eliciting Probabilistic Estimates
  with Unknown Costs. In Proceedings of the
  Eighteenth European Conference on Artificial
  Intelligence (ECAI 2008), pp. 448-452, Patras,
  Greece.
• R. K. Dash, N. R. Jennings, and D. C. Parkes.
  (2003) Computational Mechanism Design A Call to
  Arms. IEEE Intelligent Systems, pages 4047.

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Questions

• Thank you for your attention.