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19th November 2008
Agent-Based Decentralised Control of Complex Distributed Systems
Alex Rogers
School of Electronics and Computer ScienceUniversity of Southampton
[email protected]://users.ecs.soton.ac.uk/acr/
19th November 2008
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
19th November 2008
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
19th November 2008
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
19th November 2008
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
19th November 2008
Agent-Based Decentralised Control
Agents
• Multiple conflicting goals and objectives• Discrete set of possible actions
19th November 2008
Agent-Based Decentralised Control
Sensors
• Multiple conflicting goals and objectives• Discrete set of possible actions
19th November 2008
Agent-Based Decentralised Control
Agents
• Multiple conflicting goals and objectives• Discrete set of possible actions• Some locality of interaction
19th November 2008
Agent-Based Decentralised Control
Agents
Maximise Social Welfare:• Multiple conflicting goals and objectives• Discrete set of possible actions• Some locality of interaction
19th November 2008
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
19th November 2008
Cooperative Systems
Agents
Central point of controlDecentralised 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
19th November 2008
Landscape of Algorithms
Complete Algorithms
DPOPOptAPOADOPT
Communication Cost
Optimality
Iterative Algorithms
Best Response (BR)Distributed Stochastic
Algorithm (DSA) Fictitious Play (FP)
Greedy Heuristic
Algorithms
Message Passing
Algorithms
Sum-ProductAlgorithm
19th November 2008
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:
19th November 2008
Graph Colouring
Agentfunction / utility
variable / state
Graph Colouring Problem Equivalent Factor Graph
19th November 2008
Max-Sum Calculations
Variable to Function: Information aggregation
Function to Variable: Marginal Maximisation
Decision:Choose state that maximises
sum of all messages
19th November 2008
Wide Area Surveillance Scenario
Dense deployment of sensors to detect pedestrian and vehicle activity within an urban environment.
Unattended Ground Sensor
19th November 2008
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
19th November 2008
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?
19th November 2008
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
19th November 2008
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
19th November 2008
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 NashSylvia Nasar Faber and Faber
19th November 2008
Nash Equilibrium
• Two strategies s1 and s2 are in Nash equilibrium if:
1. under the assumption that agent i plays s1, agent j can do no better than play s2; and
2. under the assumption that agent j plays s2, agent i can do no better than play s1.
1. Neither agent has any incentive to deviate from a Nash equilibrium
19th November 2008
Nash Equilibrium
Column Player
LEFT MIDDLE RIGHT
Row
Pla
yer
UP 4 , 3 5 , 1 6 , 2
MIDDLE 2 , 1 8 , 4 3 , 6
DOWN 3 , 0 9 , 6 2 , 8
1
2
3
4
NE
19th November 2008
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
19th November 2008
Nash Equilibrium
Column Player
LEFT MIDDLE RIGHT
Row
Pla
yer
UP 4 , 3 5 , 1 6 , 2
MIDDLE 2 , 1 8 , 4 3 , 6
DOWN 3 , 0 9 , 6 2 , 8
1
2
3
4
NE
19th November 2008
First Price AuctionDesirable properties• Efficiency
Allocation• Item assigned to
the highest bidder
Payment• Pay bid ( )
Bidding strategy• Shade bid• Bayes Nash
19th November 2008
Second Price (Vickrey) AuctionDesirable properties• Efficiency
Allocation• Item assigned to
the highest bidder
Payment• Pay second bid
Bidding strategy• Bid true valuation• Dominant strategy
19th November 2008
Open Information System• Information buyer requires a
prediction of an uncertain
• Tomorrow’s temperature
• Requires certain minimum precision or “certainty”
θ0
θ
c(θ)
θ
c(θ)
θ
c(θ)
• Identify cheapest provider• Make prediction of precision of at least θ0
• Truthfully report this prediction to buyer• Ensure provider’s utility is positive in
expectation
19th November 2008
Two Stage Mechanism• Two stage Mechanism:
1. Ask information producers to declare their costs2. 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
19th November 2008
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 Prisoner’s Dilemma• Trust and reputation models• Match making mechanisms to pair producers and buyers
19th November 2008
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
19th November 2008
Micro-CHP
Flywheel Storage
Wireless Sensors
Plug-in Hybrid
Appliances
2016 Zero Carbon Home
19th November 2008
• 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
Research Questions
19th November 2008
• What protocols and trading mechanisms generate desirable system wide properties?– Stable, predictable and low prices– Minimise CO2 emissions through flattening demand
One day
Research Questions
19th November 2008
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 40–47.