50
2
The Complex Systems Challenge
Building software that operates effectively in environments that:– Have no centralised control– Are highly interconnected– Are in constant state of flux– Are highly unpredictable– Involve multiple, individually-motivated actors
3
The Complex Systems Landscape
Grid Computing
Semantic WebWeb Services
Agent Based Computing
Service descriptionService discoveryService composition
Flexible interoperation &reasoning in heterogeneous
environments
Robust, large scaleopen systems
AutonomyRich interactions
“Brain meets Brawn”
Semantic integration
SemanticGrid
OGSA uses WSstandards
PervasiveSystems
Peer-to-Peer
eCommerce
AutonomicComputing
Sensor Networks
4
• Entities offer services in an institutional setting
• Entities connect to services– Service discovery– Service composition– Service procurement
• Entities enact services– Flexible & context sensitive
service delivery
The Computational Model
Agent
EnvironmentSphere of visibility & influence
Electronicinstitution
Interaction
(Jennings, 2000 & 2001)
5
“encapsulated computer system, situated in some environment, and capable of flexible autonomous action in that environment in order to
meet its objectives”
Agents as Service Providers & Consumers
6
“encapsulated computer system, situated in some environment, and capable of flexible autonomous action in that environment in order to
meet its objectives”
Agents as Service Providers & Consumers
• control over internal state and over own behaviour
7
“encapsulated computer system, situated in some environment, and capable of flexible autonomous action in that environment in order to
meet its objectives”
Agents as Service Providers & Consumers
• control over internal state and over own behaviour
• experiences environment through sensors and acts through effectors
8
“encapsulated computer system, situated in some environment, and capable of flexible autonomous action in that environment in order to
meet its objectives”
Agents as Service Providers & Consumers
• reactive: respond in timely fashion to environmental change• proactive: act in anticipation of future goals
• control over internal state and over own behaviour
• experiences environment through sensors and acts through effectors
9
Interactions occur in Electronic Institutions
Service providers and consumers interact in an electronic institution:– Providers offer services– Consumers want services– Institution structures interactions & provides effective
matching of relevant parties
Computational Service Economy
10
permissible participantse.g. buyers, sellers & third parties
interaction states e.g. accepting bids, auction closed
events causing state transitions e.g. bid, time out, bid accepted
valid actionsbid, ask, propose, accept, reject, counter-proposal, critique
reward structureswho pays & who gets paid for what
Computational Service Economies
Mechanism Design
design of electronic institution“rules of the game”
(Dash et al., 2003)
11
design of electronic institution“rules of the game”
Computational Service Economies
shaped by mechanism
decision making employed to achieve trading objectives– from very simple to very complex
maximise benefit– to self (self interest) and/or– to group (social welfare)
Mechanism Design
“how to succeed in the game”
Agent Strategies(Dash et al., 2003)
permissible participantse.g. buyers, sellers & third parties
interaction states e.g. accepting bids, auction closed
events causing state transitions e.g. bid, time out, bid accepted
valid actionsbid, ask, propose, accept, reject, counter-proposal, critique
reward structureswho pays & who gets paid for what
12
In Terms of Decentralised Control
• Aim: To achieve desirable system properties through local actions
– Each agent just trying to maximise its objective function
• Achieved by: Incentivisation– Designer doesn’t necessarily own the agents and so needs
to motivate them to participate & behave in particular ways– Do this by appropriate design of the mechanism
• In particular, the payments and rewards.
13
Applications
14
Environmental Sensor Networks(Martinez, Hart & Ong, 2004)
2003
2006
Briksdalsbreen Glacier, Norway
15
Decentralised Control of Energy Management
• Sensors have limited localisation ability – Information about network topology distributed
• Relaying data is altruistic.– Sensors use their own battery to prolong life of
other sensors.– When there are different stakeholders, must
provide some incentive for sensors to do this.
• Designed distributed mechanism composed of two parts:
– Communication protocol allows sensors to locate each other.
– Payment scheme incentivises relaying of data.
Single hop = (3d)2
Multiple hop = 3d2
d
TransmissionPower
(Rogers et al., 2005)
16
The Communication Protocol
• Sensors have the ability to find the lowest transmission power required to communicate reliably with the centre.
• Since calling and responding sensors use this power, sensors locate nearby sensors that may act as a mediator.
1
2
34
17
The Payment Scheme
• Introduce payments to sensors.– Sensors receive payment for
submitting data.
– Sensors receive a discounted payment for relaying data.
• Selfish agents seek to maximise their payments.
Payment /q
P di
2
Payment /p
Pdi 3
4
Mechanism aligns goals of selfish agents (maximise payment) with goals of the system designer (maximise coverage of
network for as long as possible).
18
The Distributed Mechanism
Without With
19
Comparison with Optimal Results
Optimal
Ours
No hops
20
Self-Organising Networks
21
Non-Technical Problems!
22
Applications
23
Aerial Surveillance(Rogers et al., 2006)
• Autonomous helicopter agents (radar range-bearing sensors)– aim to refine their own estimate of their location and map ground-based
targets of interest.– i.e. selfishly maximise their own information
• Agents may improve information quality of target location by sharing information with others– but bandwidth of communication
network is constrained– individual agents prefer to receive
information from others, rather than share their own.
• Design an information economyto manage the communication exchanges
24
The Currency• To make principled decisions about flow of information
within the system– need a metric of ‘information content’
• Use Fisher information – imprecise measurements contain less information
Information is additive when independent observations are fused.
25
An Individual Agent’s Task
Sensor
Targets
1
2
3
Sensor Model • Sensor tracking or mapping multiple targets.
26
An Individual Agent’s Task
CovarianceEllipses
1
2
3
Sensor Model
r1; µ1
r 2; µ2
r 3; µ3
• The sensor’s position is uncertain.
• The sensor makes noisy range and bearing measurements.
• Sensor tracking or mapping multiple targets.
27
An Individual Agent’s Task
1
2
3
Sensor Model
r1; µ1
r 2; µ2
r 3; µ3
⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡=
333231
232221
131211
PPPPPPPPP
P
• Observation covariance matrix:
• Matrix size determines bandwidth requirement.
• Information content given by:
CovarianceEllipses
28
Multi-Sensor NetworkGiven the currency, need to employ a mechanism that incentivises: – the right agents, – to share the right information, – at the right time
29
The Mechanism
• Choose the Vickrey-Clarke-Groves mechanism to allocate the constrained bandwidth– Produces optimal allocations
• Incentivises sensors to truthfully reveal their valuations (dominant strategy):– Sensors do not have to indulge in sophisticated strategic
behaviour.– Sensors from different stakeholders cannot gain advantage
by misrepresenting their valuations.– Network is resistant to malicious or faulty sensors.
30
Value and Payment
• Agents receive payment for transmitting their observations to others.
– maintain a budget of these payments.
• Agents receive value from observations that are transmitted to them by others.
• Each agent seeks to maximise both the value and the payments that it receives.
Mechanism aligns goals of selfish agents (maximise sum of their value and payment) with goals of the system designer (maximise total information contained in network).
31
Over-Representing Valuations• Strategic agent may attempt to gain an
advantage by exaggerating the value of information it wants (jumping the queue).
• Malicious agent may attempt to waste communication bandwidth by acting as a sink.
• Agent will receive value from observations, but will also make large payments.
• Its own utility is not maximised and its budget will become depleted.
32
Under-Representing Valuations• Strategic agent may attempt to
lessen payments by down playing value of information it wants.
• Agent will make smaller payments but will obtain less value
• Agent will not be maximising its own utility if it does not truthfully report valuations.
33
34
Applications
35
Tracking with Teams of Sensors
• Sensor teams used to focus system resources on high priority targets or locations.
• Need to allocate sensors to efficiently track and identify targets.
• Decentralised control essential for large scale operation in dynamic environments.
1
23
3
1
2
3 2
1
36
Decentralised Combinatorial Optimisation
• Configuring sensors is a decentralised combinatorial optimisation problem.
• Global utility depends on the interactions between sensors.
– Super-additiveProbabilistic classification tasks with heterogeneous sensors (e.g. video, IR and radar).
– Sub-additiveCorrelated information.
)()()( 2121 OOIOIOI ⊗≤+
Information of fused observation bounded by sum of the information content of the individual observations.
37
• Decentralised combinatorial optimisation - global calculation via local message passing.– The sum-product algorithm is used to calculate marginal
probabilities in graphical models in exactly this way.• Computationally efficient (avoids manipulating exponentially sized
matrices).
– The max-sum variant of the algorithm is used to decode error-correcting codes.
• Fastest known algorithm for decoding Turbo codes.
– We can use the max-sum algorithm to perform decentralised combinatorial optimisation when we seek to maximise social welfare (i.e. maximise the sum of individual utility functions).
Sum-Product Algorithm
38
Max-Sum Algorithm and Factor Graphs
Variable nodes
Function nodes
1σ 2σ3σ
4σ 5σ • Factor graph composed:– Variables– Functions (which depend on
subsets of these variables).• Use max-sum algorithm to
find variable settings that solves:
• Messages flow:– From each variable to each
connected function.– From each function to each
connected variable.U1 : : : U3
¾1 : : : ¾5
U2(¾3; ¾4; ¾5)
U3(¾2; ¾3; ¾5)U1(¾1; ¾3; ¾4)
argmax¾1 :::¾5
3X
i = 1
Ui
39
Max-Sum Algorithm & Factor Graphs• Messages between functions and variables reflect estimates of
the global utility resulting from the variable being in each possible state.
• Tree structures – no loops:– Converges to global optimum in a time related to the
diameter of the tree.• Graphs with loops:
– Converges to an approximation of the global optimum.• Some (limited) theoretical results regarding convergence in simple
settings, but known to work well in practise. – Messages may be updated asynchronously.– Can be applied to dynamically changing problems.– Can be decentralised.
40
Decentralising a Factor Graph
agentfunction / utility
variable / state
Graph colouring problem Equivalent factor graph
41
42
Decentralised Sensor Scenario
Use decentralised optimisation algorithm to coordinate sensor states.
GOAL:
Maximise uncorrelated information within the sensor network.
Fusion Center
SensorSensorSensor Observation
Estimate
Sensors maintain individual target position estimates.
Observation
Low fidelity sensor with wide coverage.
Collects estimates from sensors and fuses to form coherent world view.
Estimates
43
44
• Finds approximate solution to a global optimisation problem.
• No aggregation of calculations to a single agent.• Operates with asynchronous communication &
calculation.• Degrades gracefully with lossy communication.• Continuous running – no explicit start / stop required• Continuously adapts current solution within a dynamic
setting.
Advantages of this Approach
45
Applications
46
Other ApplicationsBluScreen personal ads
(Payne et al., 2006)
Recommending web pages(Wei et al., 2005)
Virtual organisations for the Grid(Patel et al., 2005)
47
Outlook
48
Summary• Decentralised control is needed in almost all
complex systems• Offers many functional and non-functional
advantages:– Robustness– Natural representation of problem
• Makes control more difficult, BUT– Computational service economy lens and toolset
provides promising way forward
49
ReferencesR. K. Dash, D. C. Parkes & N. R. Jennings (2003) “Computational mechanism design: A call to
arms” IEEE Intelligent Systems 18 (6) 40-47. I. Foster, N. R. Jennings & C. Kesselman (2004) “Brain meets brawn: Why grids and agents need
each other” Proc. 3rd Int. Conf. on Autonomous Agents and Multi-Agent Systems, 8-15.N. R. Jennings (2001) “An agent-based approach for building complex software systems” Comms.
of the ACM 44 (4) 35-41. N. R. Jennings (2000) “On agent-based software engineering” Artificial Intelligence 117 (2) 277-
296. K. Martinez, J. Hart & R. Ong (2004) “Environmental sensor networks” IEEE Computer 37 (6) 50-
56. J. Patel, W. T. L. Teacy, N. R. Jennings, M. Luck, S. Chalmers, N. Oren, T. J. Norman, A. Preece,
P. M. D. Gray, G. Shercliff, P. J. Stockreisser, J. Shao, W. A. Gray, N. J. Fiddian, & S. Thompson (2005) “Agent-based virtual organizations for the Grid” Int J. Multiagent and Grid Systems 1 (4) 237-249.
T. R. Payne, E. David, N. R. Jennings & M. Sharifi (2006) “Auction mechanisms for efficient advertisement selection on public displays” Proc. 17th European Conference on AI, 285-289.
A. Rogers, R. K. Dash, N. R. Jennings, S. Reece and S. Roberts (2006) “Computational mechanism design for information fusion within sensor networks” Proc. 9th Int. Conf. on Information Fusion, Florence, Italy.
A. Rogers, E. David & N. R. Jennings (2005) “Self-organised routing for wireless micro-sensor networks” IEEE Trans. on Systems, Man and Cybernetics (Part A) 35 (3) 349-359.
D. de Roure, N. R. Jennings & N. Shadbolt (2003) “The Semantic Grid: Past, Present and Future”Proc. of the IEEE 93 (3) 669-681.
Y. Z. Wei, L. Moreau & N. R. Jennings (2005) “A market-based approach to recommender systems” ACM Trans on Information Systems 23 (3) 227-266.
50
Questions?
