Table Of ContentUniversity of Massachusetts Amherst
ScholarWorks@UMass Amherst
Open Access Dissertations
2-2011
Automated Negotiation for Complex Multi-Agent
Resource Allocation
Bo An
University of Massachusetts Amherst
Follow this and additional works at:https://scholarworks.umass.edu/open_access_dissertations
Part of theComputer Sciences Commons
Recommended Citation
An, Bo, "Automated Negotiation for Complex Multi-Agent Resource Allocation" (2011).Open Access Dissertations. 329.
https://scholarworks.umass.edu/open_access_dissertations/329
This Open Access Dissertation is brought to you for free and open access by ScholarWorks@UMass Amherst. It has been accepted for inclusion in
Open Access Dissertations by an authorized administrator of ScholarWorks@UMass Amherst. For more information, please contact
scholarworks@library.umass.edu.
AUTOMATED NEGOTIATION FOR COMPLEX
MULTI-AGENT RESOURCE ALLOCATION
A Dissertation Presented
by
BO AN
Submitted to the Graduate School of the
University of Massachusetts Amherst in partial fulfillment
of the requirements for the degree of
DOCTOR OF PHILOSOPHY
February 2011
Department of Computer Science
(cid:13)c Copyright by Bo An 2011
All Rights Reserved
AUTOMATED NEGOTIATION FOR COMPLEX
MULTI-AGENT RESOURCE ALLOCATION
A Dissertation Presented
by
BO AN
Approved as to style and content by:
Victor Lesser, Chair
Jim Kurose, Member
Shlomo Zilberstein, Member
Michael Zink, Member
Andrew Barto, Department Chair
Department of Computer Science
ACKNOWLEDGMENTS
Itakethisopportunitytoexpressmygratitudetoallthepeoplewhohaveprovided
me with their help and without those help this work would not been possible. First
and foremost, I want to thank my advisor Victor Lesser for his constant support and
encouragement in all aspects of my research, career and even personal issues. Victor
has been not only a great advisor but also a cordial friend and a person mentor. He is
always enthusiastic in sharing his experience and knowledge with me. He has taught
me how to do deep research, to find new research directions by discovering variations
and generalizations, to deliver presentations, and to be confident. He gave me a lot
of freedom in pursuing my broad interests and tried very hard to put me in close
contact with the multiagent systems community. I am grateful for the opportunity
to work with and to learn from such an extraordinary teacher.
I also especially want to thank the other members of my committee, Jim Kurose,
Shlomo Zilberstein, andMichael Zink, for their helpful andinsightful comments. Spe-
cial thanks go to Jim Kurose for advising my synthesis project and Michael Zink for
guidingmyresearch onthecloudcomputing resourceallocationprobleminChapter6.
I gratefully thank the NSF CASA project (contract No. EEC-0313747) for provid-
ing me with funding over the last four years. Working for the NSF CASA project has
taught me much about how to do research which has great practical impact and I am
grateful for all the interactions that I have had with the great people there, includ-
ing Sherief Abdallah, Michael Krainin, Eric Lyons, Dave Pepyne, Brenda Phillips,
David Westbrook, and Michael Zink. I am also very grateful for the University of
Massachusetts Amherst Graduate School Fellowship that partially supported me this
past year.
iv
I want to thank other co-authors who provided me valuable suggestions and guid-
ance including Nicola Gatti for the work on bargaining theory, Kwang Mong Sim for
the work on multi-resource negotiation, and David Irwin for the work on cloud com-
puting resource allocation. I also want to thank Fred Douglis and Fan Ye for advising
my intern research work at IBM research. I am indebted to many other researchers
in multiagent systems and electronic commerce with whom I have had valuable dis-
cussions. I would like to particularly thank Edith Elkind, David Parkes, Tuomas
Sandholm, Katia Sycara, Milind Tambe, Thanos Vasilakos, and Xiaoqin Zhang for
their support toward my research work.
The Multi-Agent Systems Lab has been a family. I have benefited greatly from
many technical discussions with Dan Corkill, Alan Carlin, Yoonheui Kim, He Luo,
Hala Mostafa, Mark Sims, Huzaifa Zafar, Chongjie Zhang, and many others. I also
would like to thank Michele Roberts for helping with a variety of paperworks.
Before coming to UMASS, I had the good fortune to work with many excellent
advisors. I would like to thank Daijie Cheng, Shuangqing Li, Chunyan Miao, Zhiqi
Shen, Kwang Mong Sim, and Lianggui Tang for all their support and advice to build
my career.
Finally, I want to thankmy family for their unconditional love fromthebeginning.
I want to thank my wife Jinghua for her love and support, especially for giving up
everything in China before coming to the states. Thank you. I thank my daughter
Amber. Your birth gives me much joy. This thesis is dedicated to them.
v
ABSTRACT
AUTOMATED NEGOTIATION FOR COMPLEX
MULTI-AGENT RESOURCE ALLOCATION
FEBRUARY 2011
BO AN
B.Sc., CHONGQING UNIVERSITY
M.Sc., CHONGQING UNIVERSITY
M.Sc., UNIVERSITY OF MASSACHUSETTS AMHERST
Ph.D., UNIVERSITY OF MASSACHUSETTS AMHERST
Directed by: Professor Victor Lesser
The problem of constructing and analyzing systems of intelligent, autonomous
agents is becoming more and more important. These agents may include people,
physical robots, virtual humans, software programs acting on behalf of human be-
ings, or sensors. In a large class of multi-agent scenarios, agents may have different
capabilities, preferences, objectives, and constraints. Therefore, efficient allocation of
resources among multiple agents is often difficult to achieve. Automated negotiation
(bargaining) is the most widely used approach for multi-agent resource allocation and
it has received increasing attention in the recent years. However, information uncer-
tainty, existence of multiple contracting partners and competitors, agents’ incentive
to maximize individual utilities, and market dynamics make it difficult to calculate
agents’ rational equilibrium negotiation strategies and develop successful negotiation
vi
agents behaving well in practice. To this end, this thesis is concerned with analyzing
agents’ rational behavior and developing negotiation strategies for a range of complex
negotiation contexts.
First, we consider the problem of finding agents’ rational strategies in bargaining
with incomplete information. We focus on the principal alternating-offers finite hori-
zon bargaining protocol with one-sided uncertainty regarding agents’ reserve prices.
We provide an algorithm based on the combination of game theoretic analysis and
search techniques which finds agents’ equilibrium in pure strategies when they exist.
Ourapproachissound, completeand, inprinciple, canbeappliedtootheruncertainty
settings. Simulation results show that there is at least one pure strategy sequential
equilibrium in 99.7% of various scenarios. In addition, agents with equilibrium strate-
gies achieved higher utilities than agents with heuristic strategies.
Next, we extend the alternating-offers protocol to handle concurrent negotiations
in which each agent has multiple trading opportunities and faces market competition.
We provide an algorithm based on backward induction to compute the subgame
perfect equilibrium of concurrent negotiation. We observe that agents’ bargaining
power are affected by the proposing ordering and market competition and for a large
subset of the space of the parameters, agents’ equilibrium strategies depend on the
values of a small number of parameters. We also extend our algorithm to find a pure
strategy sequential equilibrium in concurrent negotiations where there is one-sided
uncertainty regarding the reserve price of one agent.
Third, we present the design and implementation of agents that concurrently
negotiate with other entities for acquiring multiple resources. Negotiation agents
are designed to adjust 1) the number of tentative agreements and 2) the amount of
concession they are willing to make in response to changing market conditions and
negotiation situations. In our approach, agents utilize a time-dependent negotiation
strategy in which the reserve price of each resource is dynamically determined by 1)
vii
the likelihood that negotiation will not be successfully completed, 2) the expected
agreement price of the resource, and 3) the expected number of final agreements.
The negotiation deadline of each resource is determined by its relative scarcity. Since
agents are permitted to decommit from agreements, a buyer may make more than
one tentative agreement for each resource and the maximum number of tentative
agreements is constrained by the market situation. Experimental results show that
our negotiation strategy achieved significantly higher utilities than simpler strategies.
Finally, we consider the problem of allocating networked resources in dynamic
environment, such as cloud computing platforms, where providers strategically price
resources to maximize their utility. While numerous auction-based approaches have
been proposed in the literature, our work explores an alternative approach where
providers and consumers negotiate resource leasing contracts. We propose a dis-
tributed negotiation mechanism where agents negotiate over both a contract price
and a decommitment penalty, which allows agents to decommit from contracts at a
cost. We compare our approach experimentally, using representative scenarios and
workloads, to both combinatorial auctions and the fixed-price model, and show that
the negotiation model achieves a higher social welfare.
viii
TABLE OF CONTENTS
Page
ACKNOWLEDGMENTS ............................................. iv
ABSTRACT.......................................................... vi
LIST OF TABLES .................................................... x
LIST OF FIGURES................................................... xi
CHAPTER
1. INTRODUCTION ................................................. 1
1.1 Introduction .....................................................1
1.2 Motivating Examples .............................................3
1.2.1 Collaborating, Autonomous Stream Processing Systems
(CLASP) ..............................................3
1.2.2 Global Environment for Network Innovations (GENI) ...........5
1.3 Automated Negotiation for Complex Resource Allocation
Problems ....................................................10
1.3.1 Negotiation with Uncertainty ...............................11
1.3.2 One-to-Many and Many-to-Many Negotiation .................13
1.3.3 Multi-Resource Negotiation.................................15
1.3.4 Negotiation with Decommitment for Dynamic Resource
Allocation in Cloud Computing ..........................18
1.4 Main Contributions ..............................................21
1.5 Thesis Organization..............................................22
2. LITERATURE REVIEW ON AUTOMATED
NEGOTIATION ............................................... 24
2.1 Negotiation as a Mechanism ......................................25
ix
Description:the work on multi-resource negotiation, and David Irwin for the work on I would like to particularly thank Edith Elkind, David Parkes, Tuomas due to various constraints, e.g., computational overhead, communication In System S, a job is an execution unit that accomplishes certain work through.
