Gerald J Tesauro

US Patent:

20170032277, Feb 2, 2017

Filed:

Jul 29, 2015

Appl. No.:

14/812344

Inventors:

- Armonk NY, US
CHANDRASEKHARA K. REDDY - KINNELON NJ, US
ASHISH SABHARWAL - WHITE PLAINS NY, US
HORST C. SAMULOWITZ - WHITE PLAINS NY, US
GERALD J. TESAURO - CROTON-ON-HUDSON NY, US
DEEPAK S. TURAGA - ELMSFORD NY, US

International Classification:

G06N 99/00

Abstract:

A system, method, and computer program product for automatically selecting from a plurality of analytic algorithms a best performing analytic algorithm to apply to a dataset is provided. The automatically selecting from the plurality of analytic algorithms the best performing analytic algorithm to apply to the dataset enables a training a plurality of analytic algorithms on a plurality of subsets of the dataset. Then, a corresponding prediction accuracy trend is estimated across the subsets for each of the plurality of analytic algorithms to produce a plurality of accuracy trends. Next, the best performing analytic algorithm is selected and outputted from the plurality of analytic algorithms based on the corresponding prediction accuracy trend with a highest value from the plurality of accuracy trends.

US Patent:

20170017732, Jan 19, 2017

Filed:

Jul 13, 2015

Appl. No.:

14/797777

Inventors:

- Armonk NY, US
Youngdeok Hwang - White Plains NY, US
Levente Klein - Tuckahoe NY, US
Jonathan Lenchner - North Salem NY, US
Siyuan Lu - Yorktown Heights NY, US
Fernando J. Marianno - New York NY, US
Gerald J. Tesauro - Croton-on-Hudson NY, US
Theodore G. van Kessel - Millbrook NY, US

International Classification:

G06F 17/50
G06F 17/10
G06N 99/00

Abstract:

A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.

US Patent:

20170017895, Jan 19, 2017

Filed:

Jul 14, 2015

Appl. No.:

14/798824

Inventors:

- Armonk NY, US
Youngdeok Hwang - White Plains NY, US
Levente Klein - Tuckahoe NY, US
Jonathan Lenchner - North Salem NY, US
Siyuan Lu - Yorktown Heights NY, US
Fernando J. Marianno - New York NY, US
Gerald J. Tesauro - Croton-on-Hudson NY, US
Theodore G. van Kessel - Millbrook NY, US

International Classification:

G06N 99/00
G06F 17/50
G06F 17/30

Abstract:

A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.

US Patent:

20170017896, Jan 19, 2017

Filed:

Jul 14, 2015

Appl. No.:

14/798844

Inventors:

- Armonk NY, US
Youngdeok Hwang - White Plains NY, US
Levente Klein - Tuckahoe NY, US
Jonathan Lenchner - North Salem NY, US
Siyuan Lu - Yorktown Heights NY, US
Fernando J. Marianno - New York NY, US
Gerald J. Tesauro - Croton-on-Hudson NY, US
Theodore G. van Kessel - Millbrook NY, US

International Classification:

G06N 99/00

Abstract:

A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.

US Patent:

20130204412, Aug 8, 2013

Filed:

Feb 2, 2012

Appl. No.:

13/364843

Inventors:

Janusz Marecki - New York NY, US
Richard B. Segal - Chappaqua NY, US
Gerald J. Tesauro - Croton-on-Hudson NY, US

Assignee:

INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY

International Classification:

G06F 19/00

US Classification:

700 93

Abstract:

A system, method and computer program product for planning actions in a repeated Stackelberg Game, played for a fixed number of rounds, where the payoffs or preferences of the follower are initially unknown to the leader, and a prior probability distribution over follower types is available. In repeated Bayesian Stackelberg games, the objective is to maximize the leader's cumulative expected payoff over the rounds of the game. The optimal plans in such games make intelligent tradeoffs between actions that reveal information regarding the unknown follower preferences, and actions that aim for high immediate payoff. The method solves for such optimal plans according to a Monte Carlo Tree Search method wherein simulation trials draw instances of followers from said prior probability distribution. Some embodiments additionally implement a method for pruning dominated leader strategies.

US Patent:

20130185039, Jul 18, 2013

Filed:

Jan 12, 2012

Appl. No.:

13/348993

Inventors:

Gerald J. Tesauro - Croton-on-Hudson NY, US
Alina Beygelzimer - White Plains NY, US
Richard B. Segal - Chappaqua NY, US
Mark N. Wegman - New Castle NY, US

Assignee:

INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY

International Classification:

G06G 7/48

US Classification:

703 6

Abstract:

A method, system and computer program product for choosing actions in a state of a planning problem. The system simulates one or more sequences of actions, state transitions and rewards starting from the current state of the planning problem. During the simulation of performing a given action in a given state, a data record is maintained of observed contextual state information, and observed cumulative reward resulting from the action. The system performs a regression fit on the data records, enabling estimation of expected reward as a function of contextual state. The estimations of expected rewards are used to guide the choice of actions during the simulations. Upon completion of all simulations, the top-level action which obtained highest mean reward during the simulations is recommended to be executed in the current state of the planning problem.

US Patent:

20120203912, Aug 9, 2012

Filed:

Apr 19, 2012

Appl. No.:

13/450789

Inventors:

David Michael Chess - Mohegan Lake NY, US
Rajashi Das - New Rochelle NY, US
James Edwin Hanson - Yorktown Heights NY, US
Alla Segal - Mount Kisco NY, US
Gerald James Tesauro - Croton-On-Hudson NY, US
Ian Nicholas Whalley - Pawling NY, US

Assignee:

INTERNATIONAL BUSINESS MACHINES - Armonk NY

International Classification:

G06G 7/48
G06F 15/173

US Classification:

709226, 709223

Abstract:

Methods and systems are provided for autonomic control and optimization of computing systems. A plurality of component models for one or more components in an autonomic computing system are maintained in a system level database. These component models are obtained from a source external to the management server including the components associated with the models. Component models are added or removed from the database or updated as need. A system level management server in communication with the database utilizes the component models maintained in the system level database and generic component models as needed to compute an optimum state of the autonomic computing system. The autonomic computing system is managed in accordance with the computed optimum state.

US Patent:

20090098515, Apr 16, 2009

Filed:

Oct 11, 2007

Appl. No.:

11/870698

Inventors:

Rajarshi Das - Armonk NY, US
Gerald J. Tesauro - Croton-on-Hudson NY, US
Kilian Q. Weinberger - Mountain View CA, US

International Classification:

G09B 19/18

US Classification:

434107

Abstract:

The present invention is a method and an apparatus for reward-based learning of policies for managing or controlling a system or plant. In one embodiment, a method for reward-based learning includes receiving a set of one or more exemplars, where at least two of the exemplars comprise a (state, action) pair for a system, and at least one of the exemplars includes an immediate reward responsive to a (state, action) pair. A distance measure between pairs of exemplars is used to compute a Non-Linear Dimensionality Reduction (NLDR) mapping of (state, action) pairs into a lower-dimensional representation. The mapping is then applied to the set of exemplars, and reward-based learning is applied to the transformed exemplars to obtain a management policy.