MinneTAC Bibliography

papers.bib

@inproceedings{Bab02,
  author = {Alexander Babanov and John Collins and Maria Gini},
  title = {Risk and Expectations in a-priori Time Allocation in
                 Multi-Agent Contracting},
  booktitle = {Proc. of the First Int'l Conf. on Autonomous Agents
                 and Multi-Agent Systems},
  year = {2002},
  month = {July},
  pages = {53--60},
  volume = {1},
  address = {Bologna, Italy},
  pdf = {http://www.cs.umn.edu/magnet/papers/common/bab02.pdf}
}
@inproceedings{Bab03,
  author = {Alexander Babanov and John Collins and Maria Gini},
  title = {Scheduling Tasks with Precedence Constraints
                 to Solicit Desirable Bid Combinations},
  booktitle = {Proc. of the Second Int'l Conf. on Autonomous Agents
                 and Multi-Agent Systems},
  year = {2003},
  month = {July},
  pages = {345--352},
  address = {Melbourne, Australia},
  pdf = {http://www.cs.umn.edu/magnet/papers/common/bab03.pdf}
}
@incollection{Bab03ev,
  author = {A. Babanov and W. Ketter and M. Gini},
  title = {An Evolutionary Approach for Studying Heterogenous 
                  Strategies in Electronic Markets},
  booktitle = {Engineering Self-Organising Applications (ESOA 2003)},
  publisher = {Kluwer},
  pages = {157--168},
  year = 2004,
  pdf = {http://www.cs.umn.edu/magnet/papers/common/bab03ev.pdf}
}
@article{Bab03aiedam,
  author = {Alexander Babanov and John Collins and Maria Gini},
  title = {Asking the Right Question: Risk and Expectation
                 in Multi-Agent Contracting},
  journal = {Artificial Intelligence for Engineering Design,
                 Analysis and Manufacturing},
  annote = {Special issue on New AI Paradigms for Manufacturing},
  volume = {17},
  number = {4},
  month = {September},
  pages = {173--186},
  year = {2003},
  pdf = {http://www.cs.umn.edu/magnet/papers/common/bab03aiedam.pdf}
}
@mastersthesis{Bab03ms,
  author = {Alexander Babanov},
  title = {Design and Testing of Methods
                 for Scheduling Tasks with Precedence Constraints
                 to Solicit Desirable Bid Combinations},
  school = {University of Minnesota},
  year = {2003},
  month = {July},
  pdf = {http://www.cs.umn.edu/magnet/papers/common/bab03ms.pdf}
}
@inproceedings{Bab04aamas,
  author = {Alexander Babanov and John Collins and Maria Gini},
  title = {Harnessing the Search for Rational Bid Schedules with
                  Stochastic Search and Domain-Specific Heuristics},
  booktitle = {Proc. of the Third Int'l Conf. on Autonomous Agents
                 and Multi-Agent Systems},
  pages = {269--276},
  year = {2004},
  address = {New York},
  month = {July},
  publisher = {AAAI Press},
  pdf = {http://www.cs.umn.edu/magnet/papers/common/bab04aamas.pdf}
}
@techreport{Block09ERIM,
  author = {Carsten Block and John Collins and Wolfgang Ketter and Christof Weinhardt},
  title = {A Multi-Agent Energy Trading Competition},
  institution = {{RSM} Erasmus University},
  year = {2009},
  number = {ERS-2009-054-LIS},
  address = {Rotterdam, The Netherlands},
  abstract = {The energy sector will undergo fundamental changes over the next ten
    years. Prices for fossil energy resources are continuously increasing,
    there is an urgent need to reduce CO2 emissions, and the United States
    and European Union are strongly motivated to become more independent
    from foreign energy imports. These factors will lead to installation
    of large numbers of distributed renewable energy generators, which
    are often intermittent in nature. This trend conflicts with the current
    power grid control infrastructure and strategies, where a few centralized
    control centers manage a limited number of large power plants such
    that their output meets the energy demands in real time. As the proportion
    of distributed and intermittent generation capacity increases, this
    task becomes much harder, especially as the local and regional distribution
    grids where renewable energy generators are usually installed are
    currently virtually unmanaged, lack real time metering and are not
    built to cope with power flow inversions (yet). All this is about
    to change, and so the control strategies must be adapted accordingly.
    While the hierarchical command-and-control approach served well in
    a world with a few large scale generation facilities and many small
    consumers, a more flexible, decentralized, and self-organizing control
    infrastructure will have to be developed that can be actively managed
    to balance both the large grid as a whole, as well as the many lower
    voltage sub-grids. We propose a competitive simulation test bed to
    stimulate research and development of electronic agents that help
    manage these tasks. Participants in the competition will develop
    intelligent agents that are responsible to level energy supply from
    generators with energy demand from consumers. The competition is
    designed to closely model reality by bootstrapping the simulation
    environment with real historic load, generation, and weather data.
    The simulation environment will provide a low-risk platform that
    combines simulated markets and real-world data to develop solutions
    that can be applied to help building the self-organizing intelligent
    energy grid of the future.},
  pdf = {http://publishing.eur.nl/ir/repub/asset/17337/ERS-2009-054-LIS.pdf}
}
@inproceedings{Borg06,
  author = {Brett Borghetti and Eric Sodomka and Maria Gini and
        John Collins},
  title = {A market-pressure-based performance evaluator for {TAC SCM}},
  booktitle = {Workshop on Trading Agent Design and Analysis (TADA)},
  city = {Hakodate, Japan},
  year = {2006}
}
@inproceedings{Col97,
  author = {John Collins and Scott Jamison and Maria Gini and
                 Bamshad Mobasher},
  title = {Temporal Strategies in a Multi-Agent Contracting
                 Protocol},
  booktitle = {AAAI-97 Workshop on AI in Electronic Commerce},
  city = {Providence, RI},
  year = {1997},
  month = {July}
}
@inproceedings{Col98,
  author = {John Collins and Ben Youngdahl and Scott Jamison and
                 Bamshad Mobasher and Maria Gini},
  title = {A Market Architecture for Multi-Agent Contracting},
  booktitle = {Proc. of the Second Int'l Conf. on Autonomous Agents},
  city = {Minneapolis, MN},
  year = {1998},
  pages = {285--292},
  month = {May},
  pdf = {http://www.cs.umn.edu/magnet/papers/agents98/agents98.pdf}
}
@inproceedings{Col99,
  author = {John Collins and Maksim Tsvetovat and Rashmi
                 Sundareswara and Joshua Van Tonder and Maria Gini and
                 Bamshad Mobasher},
  title = {Evaluating Risk: Flexibility and Feasibility in
                 Multi-Agent Contracting},
  booktitle = {Proc. of the Third Int'l Conf. on Autonomous Agents},
  pages = {350--351},
  city = {Seattle, WA},
  year = {1999},
  month = {May},
  pdf = {http://www.cs.umn.edu/magnet/papers/agents99/agents99.pdf}
}
@inproceedings{Col00,
  author = {John Collins and Corey Bilot and Maria Gini and
                 Bamshad Mobasher},
  title = {Mixed-Initiative Decision Support in Agent-Based
                 Automated Contracting},
  booktitle = {Proc. of the Fourth Int'l Conf. on Autonomous Agents},
  pages = {247--254},
  city = {Barcelona, Catalonia, Spain},
  year = {2000},
  month = {June},
  pdf = {http://www.cs.umn.edu/magnet/papers/agents00/paper.pdf}
}
@incollection{Col00amec,
  author = {John Collins and Rashmi Sundareswara and Maria Gini
                 and Bamshad Mobasher},
  title = {Bid Selection Strategies for Multi-Agent Contracting
                 in the presence of Scheduling Constraints},
  booktitle = {Agent Mediated Electronic Commerce II},
  year = {2000},
  publisher = {Springer-Verlag},
  volume = {LNAI1788},
  editor = {A. Moukas and C. Sierra and F. Ygge},
  pdf = {http://www.cs.umn.edu/magnet/papers/amec99book/paper.pdf}
}
@article{Col01internet,
  author = {John Collins and Corey Bilot and Maria Gini and
                 Bamshad Mobasher},
  title = {Decision Processes in Agent-Based Automated
                 Contracting},
  journal = {IEEE Internet Computing},
  annote = {special issue on Virtual Markets},
  month = {March},
  pages = {61--72},
  volume = 5,
  number = 2,
  year = {2001}
}
@inproceedings{Col01aa,
  author = {John Collins and Maria Gini},
  title = {Exploring decision processes in multi-agent automated
                 contracting},
  booktitle = {Proc. of the Fifth Int'l Conf. on Autonomous Agents},
  pages = {81--82},
  city = {Montreal, Canada},
  year = {2001},
  month = {May},
  pdf = {http://www.cs.umn.edu/magnet/papers/agents01/paper.pdf}
}
@incollection{Col01ima,
  author = {John Collins and Maria Gini},
  title = {An integer programming formulation of the bid
                 evaluation problem for coordinated tasks},
  booktitle = {Mathematics of the Internet: E-Auction and Markets},
  pages = {59--74},
  publisher = {Springer-Verlag},
  year = {2001},
  editor = {Brenda Dietrich and Rakesh V. Vohra},
  volume = {127},
  series = {IMA Volumes in Mathematics and its Applications},
  address = {New York},
  pdf = {http://www.cs.umn.edu/magnet/papers/ima01/ipform.pdf}
}
@article{Col02ijec,
  author = {John Collins and Wolfgang Ketter and Maria Gini},
  title = {A Multi-Agent Negotiation Testbed for Contracting
                 Tasks with Temporal and Precedence Constraints},
  journal = {Int'l Journal of Electronic Commerce},
  year = {2002},
  pages = {35--57},
  volume = {7},
  number = {1},
  pdf = {http://www.cs.umn.edu/magnet/papers/common/col02ijec.pdf}
}
@inproceedings{Col02amec,
  author = {John Collins and G{\"u}leser Demir and Maria Gini},
  title = {Bidtree ordering in {IDA}* combinatorial auction
                 winner-determination with side constraints},
  booktitle = {Agent Mediated Electronic Commerce IV},
  year = {2002},
  publisher = {Springer-Verlag},
  pages = {17--33},
  volume = {LNAI2531},
  editor = {J. Padget and Onn Shehory and David Parkes and Norman
		  Sadeh and William Walsh},
  pdf = {http://www.cs.umn.edu/magnet/papers/common/col02amec.pdf}
}
@phdthesis{Col02thesis,
  author = {John Collins},
  title = {Solving Combinatorial Auctions with Temporal Constraints
       in Economic Agents},
  school = {University of Minnesota},
  year = {2002},
  address = {Minneapolis, Minnesota},
  month = {June},
  pdf = {http://www.cs.umn.edu/~jcollins/jec-thesis.pdf}
}
@techreport{Collins06a,
  author = {John Collins and Raghu Arunachalam and
                  Norman Sadeh and Joakim Ericsson and
                  Niclas Finne and Sverker Janson},
  title = {The Supply Chain Management Game for the
                  2006 {Trading Agent Competition}},
  institution = {Carnegie Mellon University},
  year = {2005},
  number = {CMU-ISRI-05-132},
  address = {Pittsburgh, PA 15213},
  month = {November},
  pdf = {http://www.cs.umn.edu/~jcollins/tacscmspec.pdf}
}
@incollection{Col07,
  author = {John Collins and Maria Gini},
  title = {{MAGNET}: A Multi-Agent System using Auctions with Temporal
		and Precedence Constraints},
  booktitle = {Handbooks in Information Systems Series: Business Computing},
  editor = {Gedas Adomavicius and Alok Gupta},
  publisher = {Elsevier},
  year = {2007},
  abstract = { We consider the problem of rational, self-interested,
 economic agents
 who must negotiate with each other in a market environment in order
 to carry out their plans. Customer agents express their plans in the
 form of task networks with temporal and precedence constraints. A
 combinatorial reverse auction allows supplier agents to submit bids
 specifying prices for combinations of tasks, along with time windows
 and duration data that the customer may use to compose a work
 schedule. We describe the consequences of allowing the advertised
 task network to contain schedule infeasibilities, and show how to
 resolve them in the auction winner-determination process.},
  pdf = {papers/his06.pdf}
}
@inproceedings{Collins08AITA,
  author = {John Collins and Wolfgang Ketter and Maria Gini},
  title = {Architectures for Agents in {TAC SCM}},
  booktitle = {AAAI Spring Symposium on Architectures for Intelligent Theory-Based Agents},
  pages = {7--12},
  year = {2008},
  abstract = {An autonomous trading agent is a complex piece of software that must
operate in a competitive economic environment and support a research agenda. We
describe the structure of decision processes in the MinneTAC trading
agent, focusing on the use of evaluators -- configurable,
composable modules for data analysis and prediction that are chained
together at runtime to support agent decision-making. Through a set of
examples, we show how this structure supports sales and procurement
decisions, and how those decision process can be modified in
useful ways by changing evaluator configurations.},
  address = {Stanford University, Palo Alto, California},
  month = {March},
  pdf = {aaai08spring.pdf}
}
@incollection{Collins_SBNi08,
  author = {John Collins and Wolfgang Ketter and Maria Gini},
  title = {Flexible Decision Support in a Dynamic Business Network},
  editor = {Peter Vervest and Diederik van Liere and Li Zheng},
  booktitle = {The Network Experience -- {New} Value from Smart Business Networks},
  pages = {233--246},
  abstract = {We present the design of a service oriented architecture which
  facilitates flexible managerial decision making in dynamic
  business networks. We have implemented and tested this architecture in the
  MinneTAC trading agent, which is designed to compete in the
  Supply Chain Trading Agent Competition. Our design enables managers to break out
  decision behaviors into separate, configurable components,
  and allows dynamic construction of analysis and modeling tools
  from small, single-purpose ``evaluator'' services.
  The result of our design is that the network can easily be
  configured to test a new theory and analyze the
  impact of various approaches to different aspects of the agent's
  decision processes, such as procurement, sales,
  production, and inventory management. Additionally we describe
  visualizers that allow managers to see and manipulate the configuration of
  the network, and to construct economic dashboards that can display the
  current and historical state of any node in the network.},
  publisher = {Springer Verlag},
  year = 2008,
  pdf = {papers/sbn.pdf}
}
@techreport{Collins08TR,
  author = {John Collins and Wolfgang Ketter and Maria Gini
                  and Amrudin Agovic},
  title = {Software architecture of the {MinneTAC} supply-chain
                  trading agent},
  institution = {University of Minnesota, Department of Computer
                  Science and Engineering},
  year = {2008},
  number = {08-031},
  address = {Minneapolis, MN},
  month = {October},
  abstract = {The MinneTAC trading agent is designed to compete
  in the Supply-Chain Trading
  Agent Competition. It is also designed to support the needs of a group of
  researchers, each of whom is interested in different decision problems
  related to the competition scenario. The design of MinneTAC breaks
  out each basic behavior into a separate, configurable component, and
  allows dynamic construction of analysis and modeling tools from
  small, single-purpose ``evaluators''.
  The agent is
  defined as a set of ``roles'', and a working agent is one for which a
  component is supplied for each role. This allows each researcher to
  focus on a single problem and work independently, and it allows
  multiple researchers to tackle the same problem in different ways.
  A working MinneTAC agent is
  completely defined by a set of configuration files that map the
  desired roles to the code that implements them, and that set
  parameters for the components.
  We describe the design of
  MinneTAC, and we evaluate its effectiveness in
  support of our research agenda and its competitiveness in the
  TAC-SCM game environment.},
  pdf = {http://www.cs.umn.edu/tech_reports_upload/tr2008/08-031.pdf}
}
@article{Collins08ECRA,
  title = {Flexible decision control in an autonomous trading agent},
  journal = {Electronic Commerce Research and Applications},
  volume = {8},
  number = {2},
  pages = {91--105},
  year = {2009},
  issn = {1567-4223},
  author = {John Collins and Wolfgang Ketter and Maria Gini},
  abstract = {Modern electronic commerce creates significant challenges
                  for decision-makers. The trading agent competition
                  for supply-chain management (TAC SCM) is an annual
                  competition among fully-autonomous trading agents
                  designed by teams around the world. Agents attempt
                  to maximize profits in a supply-chain scenario that
                  requires them to coordinate Procurement, Production,
                  and Sales activities in competitive markets. An
                  agent for TAC SCM is a complex piece of software
                  that must operate in a competitive economic
                  environment. We report on results of an informal
                  survey of agent design approaches among the
                  competitors in TAC SCM, and then we describe and
                  evaluate the design of our MinneTAC trading
                  agent. We focus on the use of evaluators -
                  configurable, composable modules for data analysis,
                  modeling, and prediction that are chained together
                  at runtime to support agent decision-making. Through
                  a set of examples, we show how this structure
                  supports Sales and Procurement decisions, and how
                  those decision process can be modified in useful
                  ways by changing evaluator configurations.},
  pdf = {papers/ecra07.pdf}
}
@article{Collins10-AIMag,
  author = {John Collins and Wolfgang Ketter and Norman Sadeh},
  title = {Pushing the limits of rational agents: the {Trading
              Agent Competition for Supply Chain Management}},
  journal = {AI Magazine},
  year = {2010},
  volume = {31},
  number = {2},
  month = {June},
  pages = {63--80},
  abstract = {Over the years, competitions have been important catalysts
   for progress in Artificial Intelligence.
   We describe one such competition, the Trading Agent Competition for Supply
   Chain Management (TAC SCM). We discuss its significance in the context of
   today's global market economy as well as AI research, the ways in which
   it breaks away
   from limiting assumptions made in prior work,
   and some of the advances it has engendered over the past six years.
   TAC SCM requires autonomous supply chain entities, modeled as agents, to
   coordinate their internal
   operations while concurrently trading in multiple dynamic and highly
   competitive markets.
   Since its introduction in 2003,
   the competition has attracted over 150 entries and brought together
    researchers from AI and beyond
   in the form of 75 competing teams from 25 different countries.},
  pdf = {http://reports-archive.adm.cs.cmu.edu/anon/isr2009/CMU-ISR-09-129.pdf}
}
@article{Collins10EJIS,
  author = {John Collins and Wolfgang Ketter and Maria Gini},
  title = {Flexible decision support in dynamic
                  interorganizational networks},
  journal = {European Journal of Information Systems},
  year = {2010},
  month = {March},
  abstract = {An effective Decision Support System (DSS) should help its users
  improve decision-making in complex, information-rich, environments.
  We present a feature gap analysis that shows that current decision
  support technologies lack important qualities for a new
  generation of agile business models that require easy, temporary
  integration across organizational boundaries. We enumerate these
  qualities as DSS Desiderata, properties
  that can contribute both effectiveness and flexibility to users in
  such environments.
  To address this gap, 
  we describe a new design approach that enables users to compose decision
  behaviors from
  separate, configurable components, and allows dynamic construction
  of analysis and modeling tools from small, single-purpose
  evaluator services. The result is what we call an ``evaluator
  service network'' that can easily be
  configured to test hypotheses and analyze the impact of various
  choices for elements of decision processes.
  We have implemented and tested this design in
  an interactive version of the MinneTAC trading agent, an agent
  designed for the Trading Agent Competition for Supply
  Chain Management.},
  pdf = {papers/ejis09.pdf}
}
@techreport{Holmstrom10,
  author = {Garrett Holmstrom},
  title = {Reducing Redundancy and Cognitive Burdens of Complex, 
                 Configurable Systems with Semantic Annotations},
  institution = {University of Minnesota},
  year = {2010},
  type = {Honors thesis},
  address = {Minneapolis, MN},
  month = {May},
  abstract = {Configurable, metadata-driven systems provide an attractive design
philosophy for large, modular applications due to their scalable, easy-to-
test natures. However, their size still presents challenges to developers
in that it is difficult to avoid duplicating code, and to users in that one
must be familiar with a very large number of components to be able to
configure such a system effectively. In this paper I present a method of si-
multaneously reducing the sizes of these lists and the amount of redundant
component code using source code annotations. I use these annotations as
a basis for an ontology that in turn helps reduce the number of components
one must be familiar with to create a system configuration. This helps
reduce the cognitive burden of using the system to a reasonable level.
I additionally employ these annotations to eliminate a large amount of
redundant, uncoordinated code and enforce a link between components’
behavior and semantic metadata.},
  pdf = {papers/holmstrom10_thesis.pdf}
}
@techreport{Kang10,
  author = {Puthyrak Kang},
  title = {Software Architecture of the TAC Energy Trading Broker},
  institution = {University of Minnesota},
  year = {2010},
  type = {Masters project},
  address = {Minneapolis, MN},
  month = {July},
  abstract = {The TAC Energy Trading Broker is a design framework on which to build
working software agents. The framework will include sufficient
capability to act as a broker in the TAC Energy scenario with minimal
capabilities. The broker framework is designed to lower the barrier to
entry that, in other TAC scenarios, imposes huge challenges for agent
developers who are not strong software developers. The framework
leverages the Repast Simphony toolkit so that the developers can build
their brokers totally using the Repast's graphical interface. The
framework also provides APIs for those developers who want to build
their brokers from scratch. Additionally, the framework design is
flexible, extensible, and its components will be independently
testable. This makes the 
framework easy for framework developers to understand and
maintain.},
  pdf = {papers/Agent_Architecture.pdf}
}
@inproceedings{Ketter04,
  author = {Wolfgang Ketter and Elena Kryzhnyaya and Steven Damer
                  and Colin McMillen and Amrudin Agovic and John Collins
                  and Maria Gini},
  title = {{MinneTAC} Sales Strategies for Supply Chain {TAC}},
  booktitle = {Int'l Conf. on Autonomous Agents
                 and Multi-Agent Systems},
  pages = {1372--1373},
  year = {2004},
  address = {New York},
  month = {July},
  pdf = {http://www.cs.umn.edu/magnet/papers/common/ketter04.pdf}
}
@inproceedings{Ketter05,
  author = {Wolfgang Ketter},
  title = {Dynamic Regime Identification and Prediction
                  Based on Observed Behavior in Electronic Marketplaces},
  booktitle = {Proc. of the Twentieth National Conference on
Artificial Intelligence},
  pages = {1646--1647},
  year = {2005},
  address = {Pittsburgh},
  month = {July},
  pdf = {papers/aaai05dc.pdf}
}
@inproceedings{Ketter05wits,
  author = {Wolfgang Ketter and John Collins and Maria Gini and Alok Gupta and Paul Schrater},
  title = {A Computational Approach to Predicting  Economic Regimes in Automated Exchanges},
  booktitle = {Proc. of the Fifteenth Annual Workshop on
                  Information Technologies and Systems},
  pages = {147--152},
  year = {2005},
  address = {Las Vegas, Nevada, USA},
  month = {December},
  pdf = {papers/wits05.pdf}
}
@inproceedings{Ketter06AAAI-ISD,
  author = {Wolfgang Ketter and Eric Sodomka and Amrudin Agovic and John Collins and Maria Gini},
  title = {Strategic Sales Management in an Autonomous Trading Agent for
  {TAC SCM}},
  booktitle = {Proc. of the Twenty-First Nat'l Conf. on Artificial
                 Intelligence},
  pages = {1943--1944},
  year = {2006},
  abstract = {We present methods for an autonomous agent to predict price
distributions and price trends in the customer market of the
Trading Agent Competition for Supply Chain Management.
We describe how these predictions can then be used by the agent
to make strategic and tactical sales decisions},
  address = {Boston, Massachusetts, USA},
  month = {July},
  pdf = {http://www-users.cs.umn.edu/~ketter/research/publications/Ketter06AAAI-ISD.pdf}
}
@incollection{KetterTADA05LNAI,
  author = {Wolfgang Ketter and John Collins and Maria Gini and Alok Gupta and Paul Schrater},
  title = {Identifying and Forecasting Economic Regimes in {TAC SCM}},
  editor = {Han La Poutr\'{e} and Norman Sadeh and Sverker Janson},
  booktitle = {AMEC and TADA 2005, LNAI 3937},
  pages = {113--125},
  abstract = {We present methods for an autonomous agent to identify
dominant market conditions, such as over-supply or scarcity, and to
forecast market changes.  We show that market conditions can be
characterized by distinguishable statistical patterns that can be
learned from historic data and used, together with real-time
observable information, to identify the current market regime and to
forecast market changes.  We use a Gaussian Mixture Model to represent
the probabilities of market prices and, by clustering these
probabilities, we identify different economic regimes. We show that
the regimes so identified have properties that correlate with market
factors that are not directly observable.  We then present methods to
predict regime changes.  We validate our methods by presenting
experimental results obtained with data from the Trading Agent
Competition for Supply Chain Management.},
  publisher = {Springer Verlag Berlin Heidelberg},
  year = {2006},
  pdf = {http://www-users.cs.umn.edu/~ketter/research/publications/KetterTADA05LNAI.pdf}
}
@phdthesis{KetterPhDThesis,
  author = {Wolfgang Ketter},
  title = {Identification and Prediction of Economic Regimes to
                  Guide Decision Making in Multi-Agent Marketplaces},
  pages = {1--138},
  year = {2007},
  school = {University of Minnesota, Twin-Cities, USA},
  month = {January},
  abstract = {Supply chain management is commonly employed by businesses to
improve organizational processes by optimizing the transfer of
goods, information, and services between buyers and suppliers.
Traditionally, supply chains have been created and maintained
through the interactions of human representatives of the various
companies involved.  However, the recent advent of autonomous
software agents opens new possibilities for automating and
coordinating the decision making processes between the various
parties involved.
Autonomous agents participating in supply chain management must
typically make their decisions in environments of high complexity,
high variability, and high uncertainty since only limited information
is visible.
We present an approach whereby an autonomous agent is able to make
tactical decisions, such as product pricing, as well as strategic
decisions, such as product mix and production planning, in order
to maximize its profit despite the uncertainties in the market.
The agent predicts future market conditions and adapts its decisions
on procurement, production, and sales accordingly.
Using a combination of machine learning and optimization techniques,
the agent first characterizes the microeconomic conditions, such
as over-supply or scarcity, of the market. These conditions are
distinguishable statistical patterns that we call \emph{economic
regimes}.  They are learned from historical data by using a Gaussian
Mixture Model to model the price density of the different products
and by clustering price distributions that recur across days.
In real-time the agent identifies the current dominant market
condition and forecasts market changes over a planning horizon.
Methods for the identification of regimes are explored in detail,
and three different algorithms are presented. One is based on
exponential smoothing, the second on a Markov prediction process,
and the third on a Markov correction-prediction process. We examine
a wide range of tuning options for these algorithms, and show how
they can be used to predict prices, price trends, and the probability
of receiving a customer order.
We validate our methods by presenting experimental results from the
Trading Agent Competition for Supply Chain Management, an international
competition of software agents that has provided inspiration for this
work.  We also show how the same approach can be applied to the stock
market.},
  pdf = {http://www-users.cs.umn.edu/~ketter/research/publications/KetterPhDThesis.pdf}
}
@inproceedings{Ketter07ICEC,
  author = {Wolfgang Ketter and John Collins and Maria Gini and
                  Alok Gupta and Paul Schrater},
  title = {A Predictive Empirical Model for Pricing and Resource Allocation Decisions},
  booktitle = {Proc. of 9th Int'l Conf. on Electronic Commerce},
  pages = {449--458},
  year = {2007},
  abstract = {We present a semi-parametric model that describes pricing behaviors in
a market environment, and we show how that model can be used to guide
resource allocation and pricing decisions in an autonomous trading agent.
We validate our model by presenting experimental results obtained in the
Trading Agent Competition for Supply Chain Management.},
  address = {Minneapolis, Minnesota, USA},
  month = {August},
  pdf = {http://www-users.cs.umn.edu/~ketter/research/publications/Ketter07ICEC.pdf}
}
@inproceedings{Ketter07ISIS,
  author = {Wolfgang Ketter and John Collins and Maria Gini and
                  Alok Gupta and Paul Schrater},
  title = {Pricing and Resource Allocation for Intelligent Trading Agents using Economic Regimes},
  booktitle = {International Symposium of Information Systems},
  pages = {--},
  year = {2007},
  abstract = {We present a semi-parametric model that describes and predicts
pricing behaviors in a market environment using a Gaussian mixture
model and a Markov process. We show how the model can be used to
guide resource allocation and pricing decisions in an autonomous
trading agent. We validate our model by presenting results obtained
in the Trading Agent Competition for Supply Chain Management.},
  address = {Hyderabad, India},
  month = {December},
  pdf = {http://www-users.cs.umn.edu/~ketter/research/publications/Ketter07ISIS.pdf}
}
@inproceedings{Ketter08tada,
  author = {Wolfgang Ketter and John Collins and Maria Gini},
  title = {A Survey of Agent Designs for {TAC SCM}},
  booktitle = {Workshop on Trading Agent Design and Analysis (TADA)},
  pages = {--},
  year = {2008},
  abstract = {An autonomous trading agent is a complex piece of software that must
operate in a competitive economic environment. We report results of an informal survey
of agent design approaches among the competitors in the Trading Agent Competition for Supply
Chain Management (TAC SCM). We identify the problem of decision
coordination as a crucial element in the design of an agent for TAC
SCM, and we review the published literature on agent design to
discover a wide variety of approaches to this problem. We believe that
the existence of such variety is an indication that much is yet to be
learned about designing such agents.},
  address = {Chicago, USA},
  month = {July},
  pdf = {http://www-users.cs.umn.edu/~ketter/research/publications/Ketter08TADA.pdf}
}
@article{Ketter09DSS,
  author = {Wolfgang Ketter and John Collins and Maria Gini and
                  Alok Gupta and Paul Schrater},
  title = {Detecting and Forecasting Economic Regimes
                  in Multi-Agent Automated Exchanges},
  journal = {Decision Support Systems},
  year = {2009},
  pages = {307--318},
  volume = {47},
  number = {4},
  abstract = {We show how an autonomous agent can use
observable market conditions to characterize the microeconomic
situation of the market and predict future market trends.  The
agent can use this information to make both tactical decisions, such
as pricing, and strategic decisions, such as product mix and
production planning.  We develop methods to learn dominant
market conditions, such as over-supply or scarcity, from historical
data using Gaussian mixture models to construct price density
functions. We discuss how this model can be combined with
real-time observable information to identify the current dominant
market condition and to forecast market changes over a planning
horizon. We forecast market changes via both
a Markov correction-prediction process and
an exponential smoother.
Empirical analysis shows that the exponential smoother yields more accurate
predictions for the current and the next day
(supporting tactical decisions),
while the Markov correction-prediction process is
better for longer term predictions (supporting strategic decisions).
Our approach offers more flexibility than
traditional regression based approaches,
since it does not assume a fixed functional
relationship between dependent and independent variables. We
validate our methods by presenting experimental results in a case
study, the Trading Agent Competition for Supply Chain Management.},
  pdf = {http://www-users.cs.umn.edu/~ketter/research/publications/Ketter09DSS.pdf}
}
@incollection{Ketter08TADAbook,
  author = {Wolfgang Ketter and John Collins and Maria Gini},
  title = {Coordinating Decisions in a Supply-Chain Trading Agent},
  booktitle = {Agent-Mediated Electronic Commerce X and
                  Trading Agent Design and Analysis VI},
  pages = {},
  publisher = {Springer},
  year = {2010},
  editor = {Wolfgang Ketter and Han La Poutre and Norman M. Sadeh and Onn Shehory and William Walsh},
  series = {Lecture Notes in Business Information Processing},
  abstract = {An autonomous trading agent is a complex piece of software
   that must
   operate in a competitive economic environment.
   We identify the problem of decision
   coordination as a crucial element in the design of an agent for TAC
   SCM, and we review the published literature on agent design to
   discover a wide variety of approaches to this problem. We believe that
   the existence of such variety is an indication that much is yet to be
   learned about designing such agents.},
  pdf = {papers/tada08.pdf}
}
@inproceedings{Nelson09CEC,
  author = {Andrew Nelson and Dickens Nyabuti and John Collins and Wolfgang Ketter and Maria Gini},
  title = {Ontology-driven decision support in dynamic supply-chains},
  booktitle = {Proceedings of the 11th IEEE Conference on Enterprise Computing (CEC-09)},
  pages = {},
  year = {2009},
  address = {Vienna, Austria},
  month = {July},
  abstract = {We describe an approach to building a highly configurable,
semi-autonomous agent that can support users playing a variety of
roles in a supply-chain trading environment. The agent's decision
processes are composed of networks of simple services that are
described using an OWL ontology. The ontology describes both the
abstract data structures that are produced and consumed by individual
services, as well as the business meaning of these data elements.
This approach supports
goal-directed composition of services in order to generate performance
dashboards, as well as direct injection of user input at arbitrary
points in the network.}
}
@inproceedings{Nelson09TADA,
  title = {Toward Human-Agent Competition in {TAC SCM}},
  author = {Nelson, A. and Nyabuti, D. and Collins, J. and Ketter, W. and Gini, M.},
  booktitle = {Workshop on Trading Agent Design and Analysis (TADA)},
  pages = {},
  year = {2009},
  address = {Pasadena, USA},
  month = {July},
  publisher = {AAAI Press},
  abstract = {We propose a variation of the TAC SCM supply-chain trading
competition, in which human decision-makers compete with
fully-autonomous agents.
Because of the complexity and time pressures of the competition
environment, humans may be assisted by semi-autonomous agents, which
could be modifications of existing agents. The research goal
is to discover what kinds of decision support will make a human
decision-maker most effective in this environment.
We show how an existing agent might be modified to
operate in this new competition by updating our MinneTAC agent
into a highly configurable,
semi-autonomous agent that can support human users playing a variety of
roles in the modified competition environment. The agent's decision
processes are composed of networks of simple services that are
described using an OWL ontology. The ontology describes the structure
of the service network, along with the structure and semantics of the
data elements that are produced and consumed by individual
services.},
  pdf = {http://www-users.cs.umn.edu/~ketter/research/publications/Nelson09TADA.pdf}
}
@inproceedings{Sodomka07,
  author = {Eric Sodomka and John Collins and Maria Gini},
  title = {Efficient Statistical Methods for Evaluating
        Trading Agent Performance},
  year = {2007},
  booktitle = {Proc. of the Twenty-Second National Conference on
        Artificial Intelligence},
  pages = {770--775},
  abstract = {Market simulations, like their real-world counterparts,
are typically
domains of high complexity, high variability, and incomplete
information. 
The performance of autonomous agents in these markets depends both upon the
strategies of their opponents
and on various market conditions, such as supply and
demand.
Because the space for possible strategies and market conditions is
very large, empirical analysis in these domains becomes
exceedingly difficult. Researchers who wish to evaluate their agents must
run many test games across multiple opponent sets and market conditions
to verify that agent performance has actually improved. 
Our approach is to improve the statistical power of market simulation
experiments by controlling their complexity, thereby creating
an environment more conducive to structured agent testing and
analysis. We develop a tool that controls variability across games
in one such
market environment, the Trading Agent Competition for Supply Chain
Management (TAC SCM),
and demonstrate how it provides an efficient, systematic method 
for TAC SCM researchers to analyze agent performance.},
  pdf = {papers/aaai07.pdf}
}

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