Results 1  10
of
49
Specification Faithfulness in Networks with Rational Nodes
, 2004
"... It is useful to prove that an implementation correctly follows a specification. But even with a provably correct implementation, given a choice, would a node choose to follow it? This paper explores how to create distributed system specifications that will be faithfully implemented in networks with ..."
Abstract

Cited by 66 (10 self)
 Add to MetaCart
It is useful to prove that an implementation correctly follows a specification. But even with a provably correct implementation, given a choice, would a node choose to follow it? This paper explores how to create distributed system specifications that will be faithfully implemented in networks with rational nodes, so that no node will choose to deviate. Given a strategyproof centralized mechanism, and given a network of nodes modeled as having rationalmanipulation faults, we provide a proof technique to establish the incentive, communication, and algorithmcompatibility properties that guarantee that participating nodes are faithful to a suggested specification. As a case study, we apply our methods to extend the strategyproof interdomain routing mechanism proposed by Feigenbaum, Papadimitriou, Sami, and Shenker (FPSS) [7], defining a faithful implementation.
Mdpop: Faithful distributed implementation of efficient social choice problems
 In AAMAS’06  Autonomous Agents and Multiagent Systems
, 2006
"... In the efficient social choice problem, the goal is to assign values, subject to side constraints, to a set of variables to maximize the total utility across a population of agents, where each agent has private information about its utility function. In this paper we model the social choice problem ..."
Abstract

Cited by 48 (17 self)
 Add to MetaCart
(Show Context)
In the efficient social choice problem, the goal is to assign values, subject to side constraints, to a set of variables to maximize the total utility across a population of agents, where each agent has private information about its utility function. In this paper we model the social choice problem as a distributed constraint optimization problem (DCOP), in which each agent can communicate with other agents that share an interest in one or more variables. Whereas existing DCOP algorithms can be easily manipulated by an agent, either by misreporting private information or deviating from the algorithm, we introduce MDPOP, the first DCOP algorithm that provides a faithful distributed implementation for efficient social choice. This provides a concrete example of how the methods of mechanism design can be unified with those of distributed optimization. Faithfulness ensures that no agent can benefit by unilaterally deviating from any aspect of the protocol, neither informationrevelation, computation, nor communication, and whatever the private information of other agents. We allow for payments by agents to a central bank, which is the only central authority that we require. To achieve faithfulness, we carefully integrate the VickreyClarkeGroves (VCG) mechanism with the DPOP algorithm, such that each agent is only asked to perform computation, report
Open constraint programming
 ARTIFICIAL INTELLIGENCE 161 (2005) 181–208
, 2005
"... Traditionally, constraint satisfaction problems (CSP) have assumed closedworld scenarios where all domains and constraints are fixed from the beginning. With the Internet, many of the traditional CSP applications in resource allocation, scheduling and planning pose themselves in openworld settings ..."
Abstract

Cited by 38 (5 self)
 Add to MetaCart
(Show Context)
Traditionally, constraint satisfaction problems (CSP) have assumed closedworld scenarios where all domains and constraints are fixed from the beginning. With the Internet, many of the traditional CSP applications in resource allocation, scheduling and planning pose themselves in openworld settings, where domains and constraints must be discovered from different sources in a network. To model this scenario, we define open constraint satisfaction problems (OCSP) as CSP where domains and constraints are incrementally discovered through a network. We then extend the concept to open constraint optimization (OCOP). OCSP can be solved without complete knowledge of the variable domains, and we give sound and complete algorithms. We show that OCOP require the additional assumption that variable domains and relations are revealed in nondecreasing order of preference. We present a variety of algorithms for solving OCOP in the possibilistic and weighted model. We compare the algorithms through experiments on randomly generated problems. We show that in certain cases, open constraint programming can require significantly less information than traditional methods where gathering information and solving the CSP are separated. This leads to a reduction in network traffic and server load, and improves privacy in distributed problem solving.
CustomerDriven Sensor Management
 IEEE Intelligent Systems
, 2006
"... Customerdriven sensor management advocates bringing ecommerce concepts and advances to bear in sensor management. In ecommerce, customer wants essentially drive the production process. Sensor management has traditionally followed a much less capitalistic process, producing information “goods ” base ..."
Abstract

Cited by 26 (6 self)
 Add to MetaCart
Customerdriven sensor management advocates bringing ecommerce concepts and advances to bear in sensor management. In ecommerce, customer wants essentially drive the production process. Sensor management has traditionally followed a much less capitalistic process, producing information “goods ” based on predefined system goals and priorities. We explore here some of the possibilities of incorporating a customerdriven marketbased approach to sensor management.
Fairness with an honest minority and a rational majority. Cryptology ePrint Archive, Report 2008/097
, 2008
"... Abstract. We provide a simple protocol for secret reconstruction in any threshold secret sharing scheme, and prove that it is fair when executed with many rational parties together with a small minority of honest parties. That is, all parties will learn the secret with high probability when the hone ..."
Abstract

Cited by 21 (3 self)
 Add to MetaCart
(Show Context)
Abstract. We provide a simple protocol for secret reconstruction in any threshold secret sharing scheme, and prove that it is fair when executed with many rational parties together with a small minority of honest parties. That is, all parties will learn the secret with high probability when the honest parties follow the protocol and the rational parties act in their own selfinterest (as captured by a setNash analogue of trembling hand perfect equilibrium). The protocol only requires a standard (synchronous) broadcast channel, tolerates both early stopping and incorrectly computed messages, and only requires 2 rounds of communication. Previous protocols for this problem in the cryptographic or economic models have either required an honest majority, used strong communication channels that enable simultaneous exchange of information, or settled for approximate notions of security/equilibria. They all also required a nonconstant number of rounds of communication.
Resource Allocation Among Agents with MDPInduced Preferences
"... Allocating scarce resources among agents to maximize global utility is, in general, computationally challenging. We focus on problems where resources enable agents to execute actions in stochastic environments, modeled as Markov decision processes (MDPs), such that the value of a resource bundle is ..."
Abstract

Cited by 12 (2 self)
 Add to MetaCart
Allocating scarce resources among agents to maximize global utility is, in general, computationally challenging. We focus on problems where resources enable agents to execute actions in stochastic environments, modeled as Markov decision processes (MDPs), such that the value of a resource bundle is defined as the expected value of the optimal MDP policy realizable given these resources. We present an algorithm that simultaneously solves the resourceallocation and the policyoptimization problems. This allows us to avoid explicitly representing utilities over exponentially many resource bundles, leading to drastic (often exponential) reductions in computational complexity. We then use this algorithm in the context of selfinterested agents to design a combinatorial auction for allocating resources. We empirically demonstrate the effectiveness of our approach by showing that it can, in minutes, optimally solve problems for which a straightforward combinatorial resourceallocation technique would require the agents to enumerate up to 2 100 resource bundles and the auctioneer to solve an NPcomplete problem with an input of that size.
Simple Negotiation Schemes for Agents with Simple Preferences: Sufficiency, Necessity and Maximality
 JOURNAL OF AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS
, 2009
"... We investigate the properties of an abstract negotiation framework where agents autonomously negotiate over allocations of indivisible resources. In this framework, reaching an allocation that is optimal may require very complex multilateral deals. Therefore, we are interested in identifying classe ..."
Abstract

Cited by 12 (5 self)
 Add to MetaCart
We investigate the properties of an abstract negotiation framework where agents autonomously negotiate over allocations of indivisible resources. In this framework, reaching an allocation that is optimal may require very complex multilateral deals. Therefore, we are interested in identifying classes of valuation functions such that any negotiation conducted by means of deals involving only a single resource at a time is bound to converge to an optimal allocation whenever all agents model their preferences using these functions. In the case of negotiation with monetary side payments amongst selfinterested but myopic agents, the class of modular valuation functions turns out to be such a class. That is, modularity is a sufficient condition for convergence in this framework. We also show that modularity is not a necessary condition. Indeed, there can be no condition on individual valuation functions that would be both necessary and sufficient in this sense. Evaluating conditions formulated with respect to the whole profile of valuation functions used by the agents in the system would be possible in theory, but turns out to be computationally intractable in practice. Our main result shows that the class of modular functions is maximal in the sense that no strictly larger class of valuation functions would still guarantee an optimal outcome of negotiation, even when we permit more general bilateral deals. We also establish similar results in the context of negotiation without side payments.