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Complexity Issues in Multiagent Resource Allocation. Paul E. Dunne Dept. of Computer Science University of Liverpool United Kingdom. Overview. Modelling resource allocation. Assessing allocations. Complexity considerations Computational complexity properties. - PowerPoint PPT Presentation
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2nd Agentlink III TFG-MARA, Ljubljana, Slovenia 28th February – 1st March 2005
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Complexity Issues in Multiagent Resource Allocation
Paul E. DunneDept. of Computer Science
University of LiverpoolUnited Kingdom
2nd Agentlink III TFG-MARA, Ljubljana, Slovenia 28th February – 1st March 2005
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Overview1. Modelling resource allocation.2. Assessing allocations.3. Complexity considerations4. Computational complexity
properties.5. A Model for negotiating allocations6. and its properties.7. Open questions and conjectures
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Modelling Resource Allocation• A = {a1 , … , an } – set of n agents.• R = {r1 , … , rm } – resource collection.• U = {u1 , … , un } – utility functions.• Utility function – u – maps subsets of
R to rational values.• An allocation is a partition of R into n
sets - P = <P1 ; … ; Pn > - n,m denotes the set of all allocations.
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Assumptions
• Exactly one agent owns any resource, i.e. R is non-shareable.
• Utility functions have no allocative externality, i.e. for any P, Q n,m with Pi = Qi it holds that ui(Pi ) = ui(Qi ).
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Assessing Allocations
• Qualitative measures.Pareto Optimality
Envy Freeness• Quantitative measures.
Utilitarian Social WelfareEgalitarian Social Welfare
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Qualitative Assessment I
• An allocation, P, is Pareto Optimal if for every allocation, Q, that differs from it should there be an agent for whom
ui(Qi ) > ui(Pi )
then there is another agent for whomui(Pi ) > ui(Qi ).
2nd Agentlink III TFG-MARA, Ljubljana, Slovenia 28th February – 1st March 2005
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Qualitative Assessment II
• An allocation, P, is Envy Free if no agent assigns greater utility to the resource set allocated to another agent within P than it attaches to its own allocation under P.
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Quantitative Assessment
• Utilitarian Social Welfare - u(P)
u(P) = ui(Pi )
• Egalitarian Social Welfare - e(P)
e(P) = min {ui(Pi ) }
• One aim is to find allocations that maximise these.
2nd Agentlink III TFG-MARA, Ljubljana, Slovenia 28th February – 1st March 2005
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Complexity Considerations
• Formulating decision problems.• Representing instances of such
decision problems.• An important issue being how the
collection {u1 , … , un } is described.
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Some decision problems I
• ENVY-FREEInstance: <A,R,U> Question: Is there an envy-free allocation
of R?• PARETO OPTIMAL
Instance: <A,R,U> ; P n,m
Question: Is P Pareto Optimal?
2nd Agentlink III TFG-MARA, Ljubljana, Slovenia 28th February – 1st March 2005
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Some decision problems II
• WELFARE OPTIMISATIONInstance: <A,R,U>; K rational value.Question: Is there an allocation with u(P)
K ?• WELFARE IMPROVEMENT
Instance: <A,R,U>; P n,m
Question: Is there Q n,m with u(Q)> u(P)?
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Representing Utility Functions• Possible optionsEnumerate non-zero valued subsets of R
(‘bundle’ form)
Algorithm that computes u(S) given S (‘program’ form)
Suitable algebraic formula, e.g.
u(S) = TR : |T|k
(T)IS(T)
(‘k-additive’ form)
2nd Agentlink III TFG-MARA, Ljubljana, Slovenia 28th February – 1st March 2005
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Pros and Cons
• Bundle form – ‘easy’ to encode but length of encoding could be exponential in m.
• k-additive form – succinct for constant k but not always possible.
• Program form – can be succinct; problem
Program run-time and termination
2nd Agentlink III TFG-MARA, Ljubljana, Slovenia 28th February – 1st March 2005
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‘Suitable’ Program Form: SLP
• Straight-Line Programs – m input bits encode subset S
t program lines – vr := vb vd – b, d < r
• Can describe as m+t triples <r,b,d>. • Poly-time computable u poly. length SLP
• SLP for u can always be defined.
2nd Agentlink III TFG-MARA, Ljubljana, Slovenia 28th February – 1st March 2005
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Complexity and Representation• The form chosen to represent U
has little effect on the complexity of the decision problems introduced earlier.
• Similarly, many results apply even when only two agent settings are used.
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Complexity – Qualitative Case• ENVY-FREE is NP-complete with SLP
and 2 agents.• PARETO OPTIMAL is coNP-complete
with 2 agents in both SLP and 2-additive utility functions
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Complexity – Quantitative Case• In 2 agent settings using SLP or 2-
additive utility functions:WELFARE OPTIMISATION is NP-complete
WELFARE IMPROVEMENT is NP-complete
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Negotiation Models
• With <A,R,U> there are |A||R| allocations.
• For P and Q distinct allocations, the deal =<P,Q> replaces the allocation P with the the allocation Q.
• It is not necessary for every agent to be given a new allocation within a deal - A
denotes the set of agents whose allocation is changed by implementing the deal.
2nd Agentlink III TFG-MARA, Ljubljana, Slovenia 28th February – 1st March 2005
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Reducing the number of deals• It is not feasible to review every
deal.• 2 methods to restrict the number
of deals in the search space:Structural restrictionsRationality restrictions
2nd Agentlink III TFG-MARA, Ljubljana, Slovenia 28th February – 1st March 2005
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Structural Restrictions
• Limit deals to those in which the number of participating agents is bounded and/or the number of resources exchanged is bounded, e.g. One resource-at-a-time (O-contract)
(at most) k-resources-at-at-time (C(k)-contract)
Exchange (or swap) contracts
2nd Agentlink III TFG-MARA, Ljubljana, Slovenia 28th February – 1st March 2005
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Rationality Restrictions
• Limit deals to those which “improve” an agent’s view of its allocation, e.g.
Individual Rationality (IR) deals<P,Q> is said to be IR if u(Q)> u(P)
• Thus, each agent places greater value on a ‘new’ allocation or (if it loses value) can be ‘compensated’ for its loss.
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Problems with combined restrictions
• Assume <P,Q> is IR.• <P,Q> is always realisable by a
sequence of O-contracts.• <P,Q> is not always realisable by
a sequence of IR O-contracts.• Similarly, replacing O-contracts by
C(k)-contract.
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Associated decision problems
• IRO PATHInstance: <A,R,U> ; IR deal <P,Q>Question: Is there a sequence of IR O-
contracts implementing <P,Q>?• IR(k) PATH Instance: <A,R,U> ; IR deal <P,Q>Question: Is there a sequence of IR C(k)-
contracts implementing <P,Q>?
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Complexity Properties
• In SLP modelIRO PATH is NP-hard
IR(k) PATH is NP-hard k (constant)IR(k) PATH is NP-hard for k=c.|R| with
c0.5• There are difficulties with establishing
membership in NP using the “obvious” algorithm, i.e. “guess a path and check its correctness”
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Length of IR O-contract paths
• Any deal <P,Q> can be implemented by a sequence of at most |R| O-contracts.
• There are IR deals <P,Q> that can be implemented by a sequence of IR O-contracts but the shortest such sequence has length
(2|R|) – (arbitrary U) (2|R|/2) – (monotone U)
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Some Open Questions I
• Using 2-additive utility functions:Complexity of ENVY-FREE? Complexity of IRO PATH?
• Worst-case length of shortest IR O-contract sequence for k-additive utility functions
• Upper bounds on complexity of IRO PATH, noting that IRO PATHNP? is non-trivial.
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Some Open Questions II
• Suppose the requirement for every deal to be an IR O-contract is relaxed? e.g. by allowing a “small” number of “irrational” deals and/or deals which are not O-contracts.
Approximation algorithms Do exponential length paths occur when t
irrational deals are allowed, with the same deal having poly. length with t+1 irrational deals?
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Bibliography
• P.E. Dunne, M. Wooldridge & M. Laurence. The Complexity of Contract Negotiation.Artificial Intelligence, 2005 (in press)• P.E. Dunne.Extremal Behaviour in Multiagent Contract Negotiation.Jnl. of Artificial Intelligence Res., 23, (2005), 41-78Context dependence in mulitagent resource allocation.• Y. Chevaleyre, U. Endriss, S. Estivie, & N. Maudet.Multiagent resource allocation in k-additive domains:
preference representation and complexity.