Ringvorlesung Perspektiven der Informatik

1
Risk Based Negotiation of Service Agent
Coalitions
Bastian Blankenburg, Matthias Klusch DFKI Minghua
He, Nick Jennings University of Southampton
2
Collaboration of Service Agents

• Service Provider Agents
• Independent
• Rational

Service Requesters
Deadline t1
Plan ltws2,ws1gt
Deadline t2
Plan ltws3,ws1,ws2gt
3
Service Agent Coalition Formation

• Coalition negotiation
• Set of requests, set of composition plans
• Which plans to execute?
• Do the agents have enough resources?
• Is a plan profitable?
• What about the costs in case of failure?
• How to share the profit (or loss)?
• Stability avoid that agents break their
  coalitions

4
Planning and Coalition Formation

• How to integrate composition planning and
  coalition formation?
• Plan-driven negotiation
• Generate plans first
• Negotiate and implement coalitions
• Dynamics short-term small coalitions
• Coalition-based planning
• Form promising coalitions
• Generate plans within the coalitions
• Dynamics DCF-S to add/remove agents as necessary
  (Klusch/Gerber 2002)
• Mutually controlled negotiation and planning
• Integrates plan-driven negotiation and
  coalition-based planning

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Integration of Composition Planning and Coalition
Negotiation
Plan-driven negotiation
1. Plan
3. Separate
2. Coalesce, execute, share profit
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Example Medical Information Provision
Coalition Proposal C1 reward 250 my
costs 10 deadline 10min my runtime
5-6min
C1my runtime 3-5min my costs 40 Might fail!
Request diagnosis, offer 250, deadline 10min
ws1
ws2
C2 my runtime 1-2min my costs 10 On the safe
side!
Coalition Proposal C2 reward 150 my
costs 15 deadline 10min my runtime
1-2min
ws3
If C2 then I can afford to risk C1!
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Provider Agent Coalitions

• spa2 needs e.g. ca. 5-6 min for C1,
  3-4 min for C2
• Form concurrent coalitions
• Reduce overall risk by dividing resources.
• How to divide the payoff?
• How to find good subset of coalitions in the
  general case?

Coalition Proposal C1 reward 250, deadline
9min
?
Coalition Proposal C2 reward 150, deadline
5min
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Assessing Coalition Risk (1)

• Financial Risk Measures
• Informal Definition
• Combination of the probability of undesirable
  outcomes and their net results
• Coherency (Artzner et al. 1999)
• Translation invariance, positive homogenity,
  monotonicity, subadditivity
• Tail Conditional Expectation TCE
• Expected loss in a worst cases
• Based on Value-at-Risk

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Assessing Coalition Risk (2)
Composition Plan

• Service instances in a plan are executed
  sequentially
• Probability functions for instance runtimes
• Composed service runtime
• Sum of random variables convolution of PDFs
• Equal to point-wise multiplication of Fourier
  Transforms
• Fast approximation with FFT
• Probability of Failure/Success

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Fuzzy Coalition Model

• Fuzzy Coalition
• Bound to request and plan
• Coalition membership degree in 0,1
• Fraction of resources per time
• Determines service instance runtimes, PoF and PoS
• Values of a fuzzy coalition
• Reward r is paid only if of successful
• Expected reward
• Expected value
• Fuzzy coalition structure
• Set of fuzzy coalitions
• Feasibility wrt. resources

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Example
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Stability in SPA Fuzzy Coalitions

• Existing approaches (Aubin BunariuNishizaki,Saka
  wa)
• Shapley value, Core, Nucleolus and others
• Assumption coalition value is proportional to
  membership degrees
• does not hold
• runtime is 1/x.
• PoS/PoF and expected value not proportional
• PoS must not be overestimated!

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Stability in SPA Fuzzy Coalitions (2)

• Recall excess of a coalition
• Excess of a fuzzy coalition
• Any amount of membership can be transferred
• Coalition structure might be too risky for a
  member
• Should such coalitions be considered a feasible
  threat?
• Mutual dependency of risk and payoff
• How is an agents payoff affected by withdrawing
  a certain amount of membership?
• Consider conditional expected values

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Stability in SPA Fuzzy Coalitions (3)

• Kernel
• Surplus
• I can gain more without you, than you without
  me.
• max. excess of coalitions excluding the other
  agent
• With fuzzy coalitions, it is possible to transfer
  membership to multiple other coalitions at the
  same time
• Kernel-stable solution equilibrium of surplusses
• Computation transfer scheme

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Complexity

• Computation of surplus depends on computation of
  TCE and vice versa
• Both have exponential computation time
• How to do it (highly) polynomial
• Compute upper bounds for TCE
• Consider minimum individual rational payoffs
• Use subadditivity when forming additional
  coalitions
• Refine bounds while there is time
• Add some constraints to the game to compute
  surpluses
• Bound the max. coalition size, number of plans
  per coalition and number of coalitions that an
  agent can join

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Rational Service Agent Model

• Service Request Agent
• Represents a SWS request
• Specifies a deadline
• Provides a monetary reward for timely execution
• Service Provider Agent
• Offers one SWS
• Has an SWS composition planning module
• Has Bounded resources,
• May split resources among multiple service
  instance executions,
• Computes probabilistic estimations of service
  instance execution times, by e.g.
• Learning
• Stochastic process modeling (Manolache et al.
  2004)
• Produces a fixed cost for any service execution

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RFCF Approach

• Exponential
• How to make it polynomial
• drawbacks

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RFCF Outline

• Each agent performs in parallel
• Composition Planning
• Coalition Negotiation
• Proposal generation
• Minimize memberships s.t. risk is acceptable
• Maximize payoff / membership
• Proposal evaluation form feasible coalitions
  with
• acceptable risk
• maximal payoff / membership
• Payoff distribution and risk bound update
• Transfer Scheme
• Compute single-coalition TCE and add to coalition
  structure TCE
• Risk Measure Computation
• Compute exact TCE for new random subset of
  coalitions
• until service execution start time

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Example (3)
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Conclusions

• Adavantages
• Anytime approach
• Guaranteed risk bounds wrt. individual risk
  averseness
• Gradually improvement of
• risk assessment
• coalition structure
• Drawbacks/Simplifications
• Complexity
• Exact solution has exponential runtime
• Constrained solution still has highly polynimial
  runtime
• Independent service runtime assumption
• Static setting
• service execution start time
• for the dynamic case when to stop negotiation?