2112426
Evaluating risk: flexibility and feasibility in multi-agent contracting
In an automated contracting environment, where a contractor agent must negotiate with other self-interested supplier agents in order to execute its plans, there is a tradeoff between giving the suppliers sufficient flexibility to incorporate the requirements of the contractor’s call-for-bids into their own resource schedules, and ensuring the contractor that any bids received can be composed into a feasible plan. We introduce a bid evaluation process that incorporates cost, task coverage, temporal feasibility, and risk estimation. Using this evaluation process, we describe an empirical study of the tradeoffs between flexibility, plan feasibility, and cost in the context of our MAGNET multi-agent contracting market infrastructure.
2312899
Risk and expectations in a-priori time allocation in multi-agent contracting
In related research we have proposed a market architecture for multi-agent contracting and we have implemented prototypes of both the market architecture and the agents in a system called MAGNET. A customer agent in MAGNET solicits bids for the execution of multi-step plans, in which tasks have precedence and time constraints, by posting a Request for Quotes to the market. The Request for Quotes needs to include for each task its precedence constraints and a time window. In this paper, we study the problem of optimizing the time windows in the Requests for Quotes. Our approach is to use the Expected Utility Theory to reduce the likelihood of receiving unattractive bids, while maximizing the number of bids that are likely to be included in the winning bundle. We describe the model, illustrate its operation and properties, and discuss what assumptions are required for its successful integration into MAGNET or other multi-agent contracting systems.
9691353
Research applications of the MAGNET multi-agent contracting testbed
MAGNET is a testbed for exploring decision processes andagent interactions in the domain of multi-agent contracting.We are i nterested in learning how a community of hetero-geneous, self-interested agents, can operate to discover re-sources, make commitments, and carry out plans that in-volvemultipletasksandrequirecoordinationamongagents.We assume that this community of agents contains someagents who have goals that they themselves cannot fully sat-isfy. They may lack the abilities, or they may lack the re-sources, to carry out at least some of the operations in theirplans. Therearealsootherself-interestedagentsinthecom-munity who have resources to offer, and who are willing tomake those resources available to other agents in a way thatmaximizes their value to the agents that control them.The MAGNET system (Collins
35532626
Bid Selection Strategies for Multi-agent Contracting in the Presence of Scheduling Constraints
Bid evaluation in a multi-agent automated contracting environment presents a challenging search problem. We introduce a multicriterion, 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. The most successful approach used a combination of random and focused bid selection methods, along with pruning and repeated restarts.
15718873
A predictive empirical model for pricing and resource allocation decisions
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.
21639
Strategic Sales Management in an Autonomous Trading Agent for TAC SCM
To operate successfully in a competitive trading environment such as the Trading Agent Competition for Supply Chain Management (Collins et al. 2004) (TAC SCM), an agent has to allocate resources and set prices in a way that maximizes its expected profit. This requires the ability to detect changing market conditions and act accordingly. In TAC SCM six agents buy parts, assemble personal computers, and sell them in daily auctions to customers. Sales decisions in our agent, MinneTAC, are driven by three different models: an automated characterization and prediction of market conditions, which we call economic regimes (Ketter 2005), a linear program that optimizes daily sales quotas, and a model of order acceptance probability. While economic regime models are commonly used at the macro economic level (Osborn & Sensier 2002), such predictions are rarely done for a micro economic environment. We focus on the sales decisions the agent has to make, where predicting prices and customer demand play an essential role. The strategies we present have been inspired, among others, by the work of Kephart et al. (Kephart, Hanson, & Greenwald 2000). MinneTAC makes sales decisions in two steps. The first step is a strategic decision, where resources are allocated over a planning horizon in a way that maximizes expected profit over the horizon. The second step is a tactical decision, which determines the offer prices that are expected to sell the quantities determined by the strategic decision, given the current demand and the pricing model.
34266504
The BBC Television Management Information System
173562095
The ABC's of digital type
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Outline or bitmap type