Results 1 
5 of
5
Solving distributed constraint optimization problems using cooperative mediation
 In Proceedings of AAMAS
, 2004
"... Distributed Constraint Optimization Problems (DCOP) have, for a long time, been considered an important research area for multiagent systems because a vast number of realworld situations can be modeled by them. The goal of many of the researchers interested in DCOP has been to find ways to solve th ..."
Abstract

Cited by 188 (6 self)
 Add to MetaCart
(Show Context)
Distributed Constraint Optimization Problems (DCOP) have, for a long time, been considered an important research area for multiagent systems because a vast number of realworld situations can be modeled by them. The goal of many of the researchers interested in DCOP has been to find ways to solve them efficiently using fully distributed algorithms which are often based on existing centralized techniques. In this paper, we present an optimal, distributed algorithm called optimal asynchronous partial overlay (OptAPO) for solving DCOPs that is based on a partial centralization technique called cooperative mediation. The key ideas used by this algorithm are that agents, when acting as a mediator, centralize relevant portions of the DCOP, that these centralized subproblems overlap, and that agents increase the size of their subproblems as the problem solving unfolds. We present empirical evidence that shows that OptAPO performs better than other known, optimal DCOP techniques. 1.
Bumping strategies for the multiagent agreement problem
 In Proceedings of Autonomous Agents and MultiAgent Systems, (AAMAS
, 2005
"... We introduce the Multiagent Agreement Problem (MAP) to represent a class of multiagent scheduling problems. MAP is based on the Distributed Constraint Reasoning (DCR) paradigm and requires agents to choose values for variables to satisfy not only their own constraints, but also equality constraints ..."
Abstract

Cited by 27 (1 self)
 Add to MetaCart
(Show Context)
We introduce the Multiagent Agreement Problem (MAP) to represent a class of multiagent scheduling problems. MAP is based on the Distributed Constraint Reasoning (DCR) paradigm and requires agents to choose values for variables to satisfy not only their own constraints, but also equality constraints with other agents. The goal is to represent problems in which agents must agree on scheduling decisions, for example, to agree on the start time of a meeting. We investigate a challenging class of MAP – private, incremental MAP (piMAP) in which agents do incremental scheduling of activities and there exist privacy restrictions on information exchange. We investigate a range of strategies for piMAP, called “bumping ” strategies. We empirically evaluate these strategies in the domain of calendar management where a personal assistant agent must schedule meetings on behalf of its human user. Our results show that bumping decisions based on scheduling difficulty models of other agents can significantly improve performance over simpler bumping strategies.
Distributed Constraint Reasoning under Unreliable Communication
 In Proceedings of Distributed Constraint Reasoning Workshop at Second International Joint Conference on Autonomous Agents and MultiAgent Systems
, 2004
"... We investigate how algorithms for Distributed Constraint Reasoning (DCR) can be modified to operate effectively over unreliable communication infrastructure. While DCR algorithms typically assume that communication is perfect, this assumption is problematic because unreliable communication is a ..."
Abstract

Cited by 10 (1 self)
 Add to MetaCart
(Show Context)
We investigate how algorithms for Distributed Constraint Reasoning (DCR) can be modified to operate effectively over unreliable communication infrastructure. While DCR algorithms typically assume that communication is perfect, this assumption is problematic because unreliable communication is a common feature of many realworld multiagent domains.
Bumping strategies for the private incremental multiagent agreement problem
 In AAAI Spring Symposium on Persistant Agents
"... We introduce the Multiagent Agreement Problem (MAP) to represent a class of multiagent scheduling problems. MAP is based on the Distributed Constraint Reasoning (DCR) paradigm and requires agents to choose values for variables to satisfy not only their own constraints, but also equality constraints ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
We introduce the Multiagent Agreement Problem (MAP) to represent a class of multiagent scheduling problems. MAP is based on the Distributed Constraint Reasoning (DCR) paradigm and requires agents to choose values for variables to satisfy not only their own constraints, but also equality constraints with other agents. The goal is to represent problems in which agents must agree on scheduling decisions, for example, to agree on the start time of a meeting. We investigate a challenging class of MAP – private, incremental MAP (piMAP) in which agents do incremental scheduling of activities and there exist privacy restrictions on information exchange. We investigate a range of strategies for piMAP, called bumping strategies. We empirically evaluate these strategies in the domain of calendar management where a personal assistant agent must schedule meetings on behalf of its human user. Our results show that bumping decisions based on scheduling difficulty models of other agents can significantly improve performance over simpler bumping strategies.
Protecting Privacy through Distributed Computation in Multiagent Decision Making Online Appendix 2: DFS Tree Generation Algorithm
"... 1: if x has at least one neighbor then 2: if x is the root then 3: openx ← all neighbors of x 4: Remove a random neighbor y0 from openx and add it to childrenx 5: Send a CHILD message to y0 6: loop 7: Wait for an incoming message of type type from a neighbor yi 8: if openx = ∅ then / / first time x ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
1: if x has at least one neighbor then 2: if x is the root then 3: openx ← all neighbors of x 4: Remove a random neighbor y0 from openx and add it to childrenx 5: Send a CHILD message to y0 6: loop 7: Wait for an incoming message of type type from a neighbor yi 8: if openx = ∅ then / / first time x is visited 9: openx ← all neighbors of x except yi 10: parentx ← yi 11: else if type = CHILD and yi ∈ openx then 12: Remove yi from openx and add it to pseudo childrenx 13: Send PSEUDO message to yi 14: next 15: else if type = PSEUDO then