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Welfare Engineering in Practice ESAW 2004
Welfare Engineering in Practice: On the Variety of Multiagent Resource
Allocation Problems
Yann Chevaleyre1, Ulle Endriss2, Sylvia Estivie1
and Nicolas Maudet1
(1)LAMSADE, Univ. Paris IX-Dauphine(2)Dept. of Computing, Imperial College London
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Welfare Engineering in Practice ESAW 2004
Introduction• Recurring problems like E-auctions, patrol …
– Similarities between these problems ? Not exploited yet…
– Formalize this similarities for a category of problem : Resource allocation problem
• Why???– A lot of theoretical result for resource allocation– Possibility to develop a platform
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Welfare Engineering in Practice ESAW 2004
Talk Overview
• Welfare Engineering• Designer scope• Resource Allocation Framework• Example Applications• Criteria• Conclusion
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Welfare Engineering in Practice ESAW 2004
Welfare Engineering
• Social welfare ordering (quality of the solution)• Social interaction mechanism (to arrive at a solution)• Behaviour profiles (interaction mechanism)
How we can make agents negotiate socially optimal outcomes?
Socially optimal allocation of resources
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Welfare Engineering in Practice ESAW 2004
Talk Overview
• Welfare Engineering• Designer scope• Resource Allocation Framework• Example Applications• Criteria• Conclusion
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Welfare Engineering in Practice ESAW 2004
The Problem of the Designer Scope
• [Wurman et al 02]
• Agent scope
• Mechanism scope
• System scope
• Proprietor role
• End-user role
Agent
Which agent does designer control?
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Welfare Engineering in Practice ESAW 2004
Talk Overview
• Welfare Engineering• Designer scope• Resource Allocation Framework• Example Applications• Criteria• Conclusion
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Welfare Engineering in Practice ESAW 2004
• Finite set of agents A and finite set of discrete resources R• An allocation A is a partitioning of R amongst the agents in A
• Every agent i A has a utility function ui(A)
Resource Allocation by Negotiation
R
A
A
u1(A)u2(A)
u3(A)u4(A)
1 2
3 4
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Welfare Engineering in Practice ESAW 2004
Social Welfare
Social welfare is tied to the welfare
of a society’s weakest member
• Envy-freeness social welfare
• Egalitarian social welfare
• Utilitarian social welfareAnything that increases average utility
is taken to be socially beneficial
Majoring the well being of a society
There is zero probability of having an
agent envying somebody elseResearch issue : the impact of individual utility on social welfare
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Welfare Engineering in Practice ESAW 2004
Our framework (1/2)• Monetary payments
– Deal couple with monetary side payment – Payment function
• Limited money
• Approximating flows– Representation of continuous resources (water,
energy, …)
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Welfare Engineering in Practice ESAW 2004
Our framework (2/2)• Roles
– Sellers– Buyers – …
• Protocol restrictions– Restrictions on the negotiation protocol
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Welfare Engineering in Practice ESAW 2004
Talk Overview
• Welfare Engineering• Designer scope• Resource Allocation Framework• Example Applications• Criteria• Conclusion
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Welfare Engineering in Practice ESAW 2004
Examples of Applications (1/3)• Multiagent Patrolling (1/2)
– The multiagent patrolling problem: how should agents move around an area such that every part of the area is visited the most often ?
– Goal : find strategies which minimize the time between 2 visit on each node ? ?
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Welfare Engineering in Practice ESAW 2004
Examples of Applications (1/3)• Multiagent Patrolling (2/2)
– Multiagent patrolling applies to: • Multi-robot applications (intrusion detection, cleaning team of robots,
delivery)• Video-games (in warcraft-like games, doom-like, …)• Military application (surveillance, tracking intruders)• Internet applications
– Resources : each node– Utility of each agent : how well it patrols over the node it
owns– Resource allocation : agent can exchange nodes in order
to maximize his patrolling performance
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Welfare Engineering in Practice ESAW 2004
Examples of Applications (2/3)
• Allocation of satellite resources [Lemaitre et al 03]
Agents send observation request
Resources initially held by the virtual proprietor
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Welfare Engineering in Practice ESAW 2004
Examples of Applications (3/3)
• E-Auctions– Different kinds of e-auction
• B2C (Business to Consumer) : antique dealer • C2C (Consumer to Consumer) : eBay like• B2B (Business to Business) : FCC, fairmarket…
– Similarities and differences :but all could be represented with a model of resource allocation.
– Roles : sellers and buyers
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Welfare Engineering in Practice ESAW 2004
Talk Overview
• Welfare Engineering• Designer scope• Resource Allocation Framework• Example Applications• Criteria• Conclusion
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Welfare Engineering in Practice ESAW 2004
Criteria for a Social Welfare Selection (1/2)
Proprietor gain– Utility-dependent
• Example : tax on gain
• Example of application uses it : Multiagent Patrolling
– Transaction-dependent• Example : tax on each transaction
• Example of application uses it : e-auctions
– Membership-dependent• Example : Entrance fees
• Example of application uses it : Satellite allocation, e-auctions
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Welfare Engineering in Practice ESAW 2004
Criteria for a Social Welfare Selection (2/2)
Application dynamics Between a run
• Possibility for an application to run several times– Yes : Satellite application, C2C e-auctions
– No : FCC e-auctions
• If yes, whether and how the characteristics could be modified between runs?
– C2C e-auctions : users may be different
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Welfare Engineering in Practice ESAW 2004
Conclusion
• Multiagent resource allocation : A powerful paradigm
• The first idea of social welfare choice in not necessarily the better. [Guttman, Maes 99]
Toward a test platform
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Welfare Engineering in Practice ESAW 2004
References [Guttman, Maes 99] R.H. Guttman and P. Maes. Agent Mediated
integrative negotiation for retail electronic commerce. In Agent Mediated Electronic Commerce, 1999.
[Lemaitre et al 03] M. Lemaitre, G. Verfaillie, H. Fargier, J. Lang, N. Bataille and J.M. Lachiver. Equitable allocation of earth observing satellites resources. In Proc of the 5th ONERA-DLR Aerospace Symposium (ODAS’03), 2003.
[Wurman et al 02] P.R. Wurman, M.P. Wellman, and W.E. Walsh. Specifing rules for electronic auctions. AI Magazine, 23(3):15-23, 2002.