Time-Quality Tradeoffs in Reallocative Negotiation with Combinatorial Contract Types

The capability to reallocate items--e.g. tasks, securities, bandwidth slices, Mega Watt hours of electricity, and collectibles--is a key feature in automated negotiation. Especially when agents have preferences over combinations of items, this is highly nontrivial. Marginal cost based reallocation leads to an anytime algorithm where every agent's payoff increases monotonically over time. Different contract types head toward different locally optimal allocations of items, and OCSM-contracts head toward the global optimum. Reaching it can take impractically long, so it is important to trade off solution quality against negotiation time. To construct negotiation protocols that lead to good allocations quickly, we evaluated original (O), cluster (C), swap (S), and multiagent (M) contracts experimentally. O-contracts led to the highest social welfare when the ratio of agents to tasks was large, and C-contract were best when that ratio was small. O-contracts led to the largest number of contracts made. M-contracts were slower per contract, and required a significantly larger number of contracts to be tried to verify that a local optimum had been reached. S-contracts were not competitive because they restrict the search space by keeping the number of items per agent invariant. O-contracts spread the items across agents while C-contracts and M-contracts concentrated them on a few agents.

[1]  Reid G. Smith,et al.  The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver , 1980, IEEE Transactions on Computers.

[2]  Martin Andersson,et al.  Leveled commitment contracting among myopic individually rational agents , 1998, Proceedings International Conference on Multi Agent Systems (Cat. No.98EX160).

[3]  Tuomas Sandholm,et al.  Contract Types for Satisficing Task Allocation: II Experimental Results , 1998 .

[4]  Tuomas Sandholm,et al.  Contract Types for Satisficing Task Allocation , 1998 .

[5]  S. Rassenti,et al.  A Combinatorial Auction Mechanism for Airport Time Slot Allocation , 1982 .

[6]  Mark S. Fox,et al.  Constraint-Directed Negotiation of Resource Reallocations , 1990, Distributed Artificial Intelligence.

[7]  Richard E. Korf,et al.  Depth-First Iterative-Deepening: An Optimal Admissible Tree Search , 1985, Artif. Intell..

[8]  S.J.J. Smith,et al.  Empirical Methods for Artificial Intelligence , 1995 .

[9]  Tuomas Sandholm,et al.  On the Gains and Losses of Speculation in Equilibrium Markets , 1997, IJCAI.

[10]  Andrew Whinston,et al.  Frontiers of Electronic Commerce , 1996 .

[11]  Ronald M. Harstad,et al.  Computationally Manageable Combinational Auctions , 1998 .

[12]  Tuomas Sandholm,et al.  An Implementation of the Contract Net Protocol Based on Marginal Cost Calculations , 1993, AAAI.

[13]  Sandip Sen.  Predicting tradeoffs in contract-based distributed scheduling , 1993 .

[14]  Victor R. Lesser,et al.  Issues in Automated Negotiation and Electronic Commerce: Extending the Contract Net Framework , 1997, ICMAS.

[15]  Martin Andersson,et al.  Sequencing of Contract Types for Anytime Task Reallocation , 1998, AMET.

[16]  Jeffrey S. Rosenschein and Gilad Zlotkin.  Rules of Encounter , 1994 .

[17]  Victor Lesser,et al.  Negotiation among self-interested computationally limited agents , 1996 .