US2012330884A1PendingUtilityA1
Deciding an optimal action in consideration of risk
Individually held — no corporate assignee on recordPriority: Feb 15, 2011Filed: Sep 5, 2012Published: Dec 27, 2012
Est. expiryFeb 15, 2031(~4.5 yrs left)· nominal 20-yr term from priority
G06N 7/01G06Q 40/00
38
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Claims
Abstract
A method and system for deciding an optimal action in consideration of risk. The method includes the steps of: generating sequentially, by way of a Markov decision process based on a Monte Carlo method, a series of data having states on a memory of a computer; computing a risk measure of a present data by tracking generated data from opposite order to generation order, where the risk measure is calculated from a value at risk or an exceedance probability that is derived from risk measures of a plurality of states transitionable from a state of the present data; and executing the step of computing the risk measure while tracking back to starting data, where at least one of the steps is carried out using a computer device.
Claims
exact text as granted — not AI-modified1 . A system for computing an iterated risk measure, the system comprising:
a generating module for generating sequentially, by way of a Markov decision process based on a Monte Carlo method, a series of data having states on a memory of a computer; a risk measure module for computing a risk measure of a present object by tracking generated data from opposite order to generation order, wherein said risk measure is calculated from a value at risk or an exceedance probability that is derived from risk measures of a plurality of states transitionable from a state of said present object; and an executing module for executing said risk measure module while tracking back to starting object.
2 . The system according to claim 1 , wherein said risk measure module computes said risk measure using the following expression:
VaR
α
%
[
X
]
=
inf
x
∈
R
{
∑
i
:
v
i
>
x
p
i
≤
1
-
α
100
}
wherein:
X is a random variable;
v i (i=1, . . . , n) is a value of each of said plurality of states transitionable from said state of a present object; and
p i (i=1, . . . , n) is a transition probability of each of said plurality of states transitionable from said state of said present object.
3 . The system according to claim 1 , wherein said risk measure module computes said risk measure using the following expression:
Pr
(
X
>
x
)
=
∑
i
:
v
i
>
x
p
i
wherein:
Pr is a probability;
X is a random variable;
v i (i=1, . . . , n) is a value of each of said plurality of states transitionable from said state of a present object; and
p i (i=1, . . . , n) is a transition probability of each of said plurality of states transitionable from said state of said present object.
4 . A system for computing an action that minimizes an iterated risk measure, the system comprising:
a postdecision module for generating, during postdecision, data comprising combinations of a predetermined state and a possible action on a memory of the computer; a selecting module for selecting a state-action combination data from generated data of said combinations of said state and said action, based on a value associated with each of said combinations; a predecision module for generating, during predecision, a state from selected state-action combination data, by way of a Markov decision process based on a Monte Carlo method; a state data sequence module for generating a state data sequence by iterating said step of generating a state and said step of generating data comprising combinations; a risk measure module for computing, based on risk measures of a plurality of states transitionable from a present predecision state, a risk measure of an immediately preceding postdecision state by tracking generated states in opposite order to order of the generation, wherein said risk measure is calculated from a value at risk or an exceedance probability; and a value module for setting a value of a state having a minimum value in a present postdecision state to an immediately preceding predecision state, by tracking said generated states in the opposite order to the order of the generation.
5 . The system according to claim 4 , wherein said risk measure module computes said risk measure using the following expression:
VaR
α
%
[
X
]
=
inf
x
∈
R
{
∑
i
:
v
i
>
x
p
i
≤
1
-
α
100
}
wherein:
X is a random variable;
v i (i=1, . . . , n) is a value of each of said plurality of states transitionable from said state of a present object; and
p i (i=1, . . . , n) is a transition probability of each of said plurality of states transitionable from said state of said present object.
6 . The system according to claim 4 , wherein the said risk measure module computes said risk measure using the following expression:
Pr
(
X
>
x
)
=
∑
i
:
v
i
>
x
p
i
wherein:
Pr is a probability;
X is a random variable;
v i (i=1, . . . , n) is a value of each of said plurality of states transitionable from said state of a present object; and
p i (i=1, . . . , n) is a transition probability of each of said plurality of states transitionable from said state of said present object.
7 . The system according to claim 4 , wherein said selecting module uses an evaluation function which is a monotonically decreasing function with respect to a frequency of visiting said state.Join the waitlist — get patent alerts
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