Article Bid determination in simultaneous actions an agent architecture Share on Authors: Justin Boyan ITA Software, Cambridge, MA ITA Software, Cambridge, MAView Profile , Amy Greenwald Brown University, Providence, RI Brown University, Providence, RIView Profile Authors Info & Claims EC '01: Proceedings of the 3rd ACM conference on Electronic CommerceOctober 2001 Pages 210–212https://doi.org/10.1145/501158.501184Online:14 October 2001Publication History 12citation256DownloadsMetricsTotal Citations12Total Downloads256Last 12 Months3Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

This paper is concerned with computational problems that arise in the design of bidding agents for simultaneous auctions. Three natural bid determination (BD) problems are identi ed| allocation, acquisition, and completion. The rst part of the paper contains theoretical results. It is argued (i) BD in double auctions, where goods can be sold as well as bought, can be formally reduced to the problem of BD in single-sided auctions; and (ii) BD problems in simultaneous auctions are isomorphic to common variants of the winner determination problem.

This paper overviews Q2, an algorithm for optimizing the expected output of a multi-input noisy continuous function. Q2 is designed to need only a few experiments, it avoids strong assumptions on the form of the function, and it is autonomous in that it requires little problem-specific tweaking. These capabilities are directly applicable to industrial processes, and may become increasingly valuable elsewhere as the machine learning field expands beyond prediction and function identification, and into embedded active learning subsystems in robots, vehicles and consumer products. Four existing approaches to this problem (response surface methods, numerical optimization, supervised learning, and evolutionary methods) all have inadequacies when the requirement of "black box" behavior is combined with the need for few experiments. Q2 uses instance-based determination of a convex region of interest for performing experiments. In conventional instance-based approaches to learning, a neighborhood was defined by proximity to a query point. In contrast, Q2 defines the neighborhood by a new geometric procedure that captures the size and shape of the zone of possible optimum locations. Q2 also optimizes weighted combinations of outputs, and finds inputs to produce target outputs. We compare Q2 with other optimizers of noisy functions on several problems, including a simulated noisy process with both nonlinear continuous dynamics and discrete-event queueing components. Results are encouraging in terms of both speed and autonomy.

We describe an extension of the Markov decision process model in which a continuous time dimension is included in the state space. This allows for the representation and exact solution of a wide range of problems in which transitions or rewards vary over time. We examine problems based on route planning with public trans(cid:173) portation and telescope observation scheduling.

A straightforward approach to the curse of dimensionality in re(cid:173) inforcement learning and dynamic programming is to replace the lookup table with a generalizing function approximator such as a neu(cid:173) ral net. Although this has been successful in the domain of backgam(cid:173) mon, there is no guarantee of convergence. In this paper, we show that the combination of dynamic programming and function approx(cid:173) imation is not robust, and in even very benign cases, may produce an entirely wrong policy. We then introduce Grow-Support, a new algorithm which is safe from divergence yet can still reap the benefits of successful generalization .