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Justin A. Boyan

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9 papers
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9

UAI Conference 2004 Conference Paper

Bidding under Uncertainty: Theory and Experiments

  • Amy Greenwald
  • Justin A. Boyan

This paper describes a study of agent bidding strategies, assuming combinatorial valuations for complementary and substitutable goods, in three auction environments: sequential auctions, simultaneous auctions, and the Trading Agent Competition (TAC) Classic hotel auction design, a hybrid of sequential and simultaneous auctions. The problem of bidding in sequential auctions is formulated as an MDP, and it is argued that expected marginal utility bidding is the optimal bidding policy. The problem of bidding in simultaneous auctions is formulated as a stochastic program, and it is shown by example that marginal utility bidding is not an optimal bidding policy, even in deterministic settings. Two alternative methods of approximating a solution to this stochastic program are presented: the first method, which relies on expected values, is optimal in deterministic environments; the second method, which samples the nondeterministic environment, is asymptotically optimal as the number of samples tends to infinity. Finally, experiments with these various bidding policies are described in the TAC Classic setting.

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ICRA Conference 2000 Conference Paper

Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions

  • Andrew W. Moore 0001
  • Jeff G. Schneider
  • Justin A. Boyan
  • Mary S. Lee

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.

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AAAI Conference 1998 Conference Paper

Learning Evaluation Functions for Global Optimization and Boolean Satisfiability

  • Justin A. Boyan

This paper describes STAGE, a learning approach to automatically improving search performance on optimization problems. STAGE learns an evaluation function which predicts the outcomeof a local search algorithm, such as hillclimbing or WALKSAT, as a function of state features along its search trajectories. Thelearned evaluation function is used to bias future search trajectories toward better optima. Wepresent positive results on six large-scale optimizationdomains.

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AAAI Conference 1996 Short Paper

A Reinforcement Learning Framework for Combinatorial Optimization

  • Justin A. Boyan

The combination of reinforcement learning methods with neural networks has found success on a growing number of large-scale applications, including backgammon move selection, elevator control, and job-shop scheduling. In this work, we modify and generalize the scheduling paradigm used by Zhang and Dietterich to produce a general reinforcement-learning-based framework for combinatorial optimization.

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