The Conditional Density

This paper addresses the problem of learning dynamic models of hybrid systems from demonstrations and then the problem of imitation of those demonstrations by using Bayesian filtering. A linear programming-based approach is used to develop nonparametric kernel-based conditional density estimation technique to infer accurate and concise dynamic models of system evolution from data. The training data for these models have been acquired from demonstrations by teleoperation. The trained data-driven models for mode-dependent state evolution and state-dependent mode evolution are then used online for imitation of demonstrated tasks via particle filtering. The results of simulation and experimental validation with a hexapod robot are reported to establish generalization of the proposed learning and control algorithms.

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Conditional Density Operators and the Subjectivity of Quantum Operations

10.1063/1.2713456 ◽

2007 ◽

Cited By ~ 4

Author(s):

M. S. Leifer

Keyword(s):

Conditional Density ◽

Quantum Operations ◽

Density Operators

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A characterization of ε-optimal controls for stochastic systems with partial observations via the unnormalized conditional density

International Journal of Control ◽

10.1080/00207178408933217 ◽

1984 ◽

Vol 39 (5) ◽

pp. 891-899 ◽

Cited By ~ 1

Author(s):

T. YONEYAMA

Keyword(s):

Stochastic Systems ◽

Conditional Density ◽

Optimal Controls ◽

Partial Observations

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Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions

Journal of Artificial Intelligence Research ◽

10.1613/jair.1200 ◽

2003 ◽

Vol 19 ◽

pp. 209-242 ◽

Cited By ~ 27

Author(s):

P. Stone ◽

R. E. Schapire ◽

M. L. Littman ◽

J. A. Csirik ◽

D. McAllester

Keyword(s):

Conditional Distribution ◽

Learning Problems ◽

Conditional Density ◽

The Internet ◽

Core Component ◽

Popular Method ◽

Estimation Problems ◽

Bidding Agents ◽

Trading Agent ◽

Past Data

Auctions are becoming an increasingly popular method for transacting business, especially over the Internet. This article presents a general approach to building autonomous bidding agents to bid in multiple simultaneous auctions for interacting goods. A core component of our approach learns a model of the empirical price dynamics based on past data and uses the model to analytically calculate, to the greatest extent possible, optimal bids. We introduce a new and general boosting-based algorithm for conditional density estimation problems of this kind, i.e., supervised learning problems in which the goal is to estimate the entire conditional distribution of the real-valued label. This approach is fully implemented as ATTac-2001, a top-scoring agent in the second Trading Agent Competition (TAC-01). We present experiments demonstrating the effectiveness of our boosting-based price predictor relative to several reasonable alternatives.

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