Browsing by Author "Collins, John"
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Item A Market Architecture for Multi-Agent Contracting(1997) Collins, John; Jamison, Scott; Mobasher, Bamshad; Gini, MariaWe present a generalized market architecture that provides support for a variety of types of transactions, from simple buying and selling of goods and services to complex multi-agent contract negotiations. This architecture is organized around three basic components: the exchange, the market, and the session. We also present a negotiation protocol for planning by contracting that takes advantage of the services of the market. We show how the existence of an appropriate market infrastructure can add value to a multi-agent contracting protocol by controlling fraud and discouraging counterspeculation.Item Asking the Right Question: Risk and Expectation in Multi-Agent Contracting(2002-11-05) Babanov, Alexander; Collins, John; Gini, MariaThis paper investigates methods of reducing risk in market-based auctions of tasks with complex time constraints and interdependencies. The research addresses problems in a contracting setting in which a buyer has a set of tasks to be performed. Because of the complex dependencies among the tasks, a task not completed on time might have devastating effect on other tasks. Therefore, the problem is to sequence tasks and allocate time windows to maximize the expected utility of the agent. Because there is a probability of loss as well as a probability of gain, the decision process must deal with the risk posture of the person or organization on whose behalf the decision maker is acting.Item Component-based design for a trading agent(2004-05-04) Collins, John; Agovic, Amrudin; Damer, Steven; Gini, MariaMinneTAC is an agent 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. Dependencies between components are almost non-existent. The agent itself 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 user to focus on a single problem and work independently, and it allows multiple user 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. This paper describes the design of MinneTAC and evaluates its effectiveness in support of our research agenda, and in its competitiveness in the TAC-SCM game environment.Item Cooperative, Project-Based Learning Reinforced by Reflection and Presentation in a Graduate-Level Software Engineering Course(2007-09-14) Moore, Tamara; Kern, Anne; Collins, JohnThis project studied the effectiveness of a comprehensive learning project that incorporates cooperative learning, project-based learning, and reflective practices in a graduate software engineering course. This paper will report on the instructor's objectives for the project, the students' achievement, and the team reflections of their progress through the project. The results of this study (1) show the effectiveness of project-based learning in a software engineering course, (2) serve as a model for software engineering instructors to use for the creation and implementation of similar course projects, and (3) show the impact of reflective practices in team settings on student learning.Item Design and Analysis of the MinneTAC-03 Supply-Chain Trading Agent(2004-04-26) Ketter, Wolfgang; Kryzhnyaya, Elena; Damer, Steven; McMillen, Colin; Agovic, Amrudin; Collins, John; Gini, MariaMinneTAC is an agent 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 outeach basic behavior into a separate, configurable component. Dependencies between components are almost non-existent. This design allows each user to focus on a single problem and work independently, and it allows multiple user to tackle the same problem in different ways. This paper describes the design of MinneTAC and evaluates its effectiveness in support of our research agenda, and in its competitiveness in the TAC-SCM game environment. We also describe two sales strategies used by MinneTAC. Both strategies estimate, as the game progresses, the probability of receiving a customer order for different prices and compute the expected profit. Offers are made to maximize the expected profit on each order. The maindifference between the two strategies is in how the probability of receiving an order and the offer prices are computed. The first strategy works well in high-demand games, the second was developed to improve performance in low-demand games. We empirically analyze the effect of the discount given by suppliers on orders received the firstday of the game, and we show that in high-demand games there is a strong correlation between the offers an agent receives from suppliers on the first day of the game and the agent's performance in the game.Item Detecting and Forecasting Economic Regimes in Automated Exchanges(2007-03-05) Ketter, Wolfgang; Collins, John; Gini, Maria; Gupta, Alok; Schrater, PaulWe present basic building blocks of an agent that can use observable market conditions to characterize the microeconomic conditions 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 that can learn dominant market conditions, such as over-supply or scarcity, from historical data using computational methods to construct price density functions. We discuss how this knowledge can be used, together with real-time observable information, to identify the current dominant market condition and to forecast market changes over a planning horizon. We validate our methods by presenting experimental results in a case study, the Trading Agent Competition for Supply Chain Management.Item Efficient Statistical Methods for Evaluating Trading Agent Performance(2007-02-13) Sodomka, Eric; Collins, John; Gini, MariaMarket 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.Item Evaluating Risk: Flexibility and Feasibility in Multi-Agent Contracting(1999-01-09) Sundareswara, Rashmi; Tsvetovat, Maksim; Collins, John; Gini, Maria; van Tonder, Joshua; Mobasher, BamshadIn an automated contracting environment, where a "customer" agent must negotiate with other self-interested "supplier" agents in order to execute its plans, there is a trade-off between giving the suppliers sufficient flexibility to incorporate the requirements of the customer's call-for-bids into their own resource schedules, and ensuring the customer that any bids received can be composed into a feasible plan. In this paper, we introduce a bid evaluation process that incorporates cost, task coverage, temporal feasibility, and risk estimation. Using this evaluation process, we provide an empirical study of the trade-offs between flexibility, plan feasibility, and cost in the context of our MAGNET multi-agent contracting market infrastructure. Our experimental results demonstrate that the advantage of increasing supplier flexibilty is dependent on the number of available suppliers. In other words, if the number of suppliers is small, the risk of plan nfeasibility outweighs the advantage of added flexibility. On the other hand, if the numer of suppliers is large, the more flexibile plan specifications result in lower-risk plans.Item Exploring Decision Processes in Multi-Agent Automated Contracting(2000-10-16) Collins, John; Gini, MariaWe are interested in the problem of multi-agent contracting, in which customers must solicit the resources and capabilities of other, self-interested agents in order to accomplish their goals. Goals may involve the execution of multi-step plans, in which different steps are contracted out to different suppliers. We have focused on decision criteria for composing requests for quotations, managing the bidding process, evaluating bids, and monitoring plan execution. We have developed a testbed that allows us to study these decision behaviors. It can generate sets of plans with known statistical attributes, formulate and submit requests for quotations, generate bids with well-defined statistics, and evaluate those bids according to a number of criteria. Each of these processes is supported by an abstract interface and a series of pluggable modules with a large number of configuration parameters. Data collection and analysis tools round out the package. We will demonstrate how to take statistics from a real application domain, apply them to the simulation, and test a variety of bid-management and bid-evaluation procedures against them.Item Flexible decision support in dynamic interorganizational networks(2008-11-10) Collins, John; Ketter, Wolfgang; Gini, MariaThis Technical Report has been completely re-written and re-released as #09-028. The updated version is available for download at its new location: http://www.cs.umn.edu/research/technical_reports.php?page=report&report_id=09-028Item Flexible decision support in dynamic interorganizational networks(2009-11-23) Collins, John; Ketter, Wolfgang; Gini, MariaAn effective Decision Support System (DSS) should help its users improve decision-making in complex, information-rich, dynamic environments. We present a feature gap analysis of current decision support technologies, and we identify a set of DSS Desiderata, properties that can contribute both effectiveness and flexibility to users in such environments. We show that there is a gap between the features provided by current DSS technologies and the DSS Desiderata we aim for. We present a design-science approach that extends the boundaries of human decision-makers by creating a new and innovative artifact called "evaluator service networks" at the confluence of people, organizations, and technology. Our artifact 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 a 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. We present an example of an evaluator service network that determines sales prices in a rich, dynamic trading environment. Additionally we describe visual interface elements that allow users 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.Item Multi-Agent Contracting for Supply-Chain Management(2000-02-10) Collins, John; Sundareswara, Rashmi; Tsvetovat, Maksim; Gini, Maria; Mobasher, BamshadWe present a system for multi-agent contract negotiation, implemented as a generalized market architecture called MAGNET. MAGNET provides support for a variety of types of transactions, from simple buying and selling of goods and services to complex multi-agent contract negotiations. In the latter case, MAGNET is designed to negotiate contracts based on temporal and precedence constraints, and includes facilities for dealing with time-based contingencies. The market operates as an explicit intermediary in the negotiation process, which helps in controlling fraud and discouraging counterspeculation.We introduce a multi-criterion, anytime bid evaluation strategy that incorporates cost, task coverage, temporal feasibility, and risk estimation into a simulated annealing framework. We report on an experimental evaluation using a set of increasingly informed search heuristics within simulated annealing. The results show that excess focus on improvement leads to faster improvement early on, at the cost of a lower likelihood of finding a solution that satisfies all the constraints.Item Multi-Agent Negotiation using Combinatorial Auctions with Precedence Constraints(2002-02-18) Collins, John; Gini, Maria; Mobasher, BamshadWe present a system for multi-agent contract negotiation, implemented as an auction-based market architecture called MAGNET. A principal feature of MAGNET is support for negotiation of contracts based on temporal and precedence constraints. We propose using an extended combinatorial auction paradigm to support these negotiations. A critical component of the agent negotiation process in a MAGNETsystem is the ability of a customer to evaluate the bids of competing suppliers. Winner determination in standard combinatorial auctions is known to be difficult, and the problem is further complicated by the addition of temporal constraints and a requirement to complete the winner-determination process within a hard deadline. We introduce two approaches to the extended winner determination problem. One is based on Integer Programming, and the other is a flexible, multi-criterion, anytime bid evaluator based on a simulated annealing framework. We evaluatethe performance of both approaches and show how performance data can be used in the agent's deliberation-scheduling process. The results show that coarse problem-size metrics can be effectively used to predict winner-determination processing time.Item Software architecture of the MinneTAC supply-chain trading agent(2008-10-20) Collins, John; Ketter, Wolfgang; Gini, Maria; Agovic, AmrudinThe 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.Item Tactical and Strategic Sales Management for Intelligent Agents Guided By Economic Regimes(2008-10-20) Ketter, Wolfgang; Collins, John; Gini, Maria; Gupta, Alok; Schrater, PaulWe present a computational approach that autonomous software agents can adopt to make tactical decisions, such as product pricing, and strategic decisions, such as product mix and production planning, to maximize profit in markets with supply and demand uncertainties. Using a combination of machine learning and optimization techniques, the agent is able to characterize economic regimes, which are historical microeconomic conditions reflecting situations such as over-supply and scarcity. We assume an agent is capable of using real-time observable information to identify the current dominant market condition and we show how it can forecast regime changes over a planning horizon. We demonstrate how the agent can then use regime characterization to predict prices, price trends, and the probability of receiving a customer order in a dynamic supply chain environment. We validate our methods by presenting experimental results from a testbed derived from the Trading Agent Competition for Supply Chain Management (TAC SCM). The results show that our agent outperforms traditional short- and long-term predictive methodologies (such as exponential smoothing) significantly, resulting in accurate prediction of customer order probabilities, and competitive market prices. This, in turn, has the potential to produce higher profits. We also demonstrate the versatility of our computational approach by applying the methodology to prediction of stock price trends.