Data processing: artificial intelligence – Machine learning – Genetic algorithm and genetic programming system
Reexamination Certificate
1998-09-24
2001-11-06
Chaki, Kakali (Department: 2122)
Data processing: artificial intelligence
Machine learning
Genetic algorithm and genetic programming system
C706S014000, C706S025000
Reexamination Certificate
active
06314412
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates to an evolutionary controlling system, and particularly to that for controlling characteristics of a subject in an evolutionary manner.
2. Description of Related Art
Heretofore, when a control system or control characteristics of a subject, such as vehicles and electrical appliances, is designed, imaginary users are selected, and the users' preferences and their using conditions are taken into consideration. The characteristics of the subject are determined in such a way as to adapt the subject to users in as broad a range as possible.
However, each individual user has a particular and unique personality, and thus, their preferences are diverse. Thus, there is a problem in that even if imaginary users are selected to develop and design a product for users by presuming the users' preference, it is impossible to satisfy all of the users of the product.
In order to solve the above problem, prior to purchase of a product, a prospective user is requested to determine whether or not the product is satisfactory to the user after checking the characteristics of the product in light of the user's preferences. However, it is troublesome for the user to check the characteristics of the product before purchase. Further, because a series of products are often operated or controlled by characteristics common in the products, although the design of the product is changed depending on the user's preferences, the user may not like other operational characteristics. Thus, although the design is appealing to some prospective users, the users may not purchase the product since the operational characteristics do not appeal to them. In other words, there is another problem in that the range of users is limited and depends on the operational characteristics.
An objective of the present invention is to provide an evolutionary control system to construct characteristics which can satisfy plural users.
SUMMARY OF THE INVENTION
One important aspect of the present invention attaining the above objective is a method for evolutionally controlling a subject based on a user's preference by using a control system having control characteristics, comprising the steps of: selecting coefficients, as genes, affecting the control characteristics of the control system; creating plural control units as chromosomes, each being constituted by plural genes; expressing the genes of each chromosome as control characteristics by controlling the subject using the control system having the control characteristics; selecting at least one chromosome from the created chromosomes based on the control characteristics expressed by the genes in view of the user's preference; and causing said at least one chromosome to evolve by repeating the creating step, the expressing step, and the selecting step, while causing crossover and mutation of the genes, until the control system obtains control characteristics suitable to the user.
In the above, since plural chromosomes are created using coefficients affecting the control characteristics of the control system controlling the characteristics of a product to be controlled, and the chromosomes are subjected to selection based on the user's intention, thereby causing the chromosomes to undergo evolution using a genetic algorithm, the characteristics of the product can be changed after shipment in accordance with the user's intention, i.e., a customized or user-friendly product most suitable to each user can be supplied. Further, since the characteristics of a product to be controlled can be changed in accordance with the user's intention after its purchase, the user can give less weight to the initial characteristics of the product itself, and can select a product from a wide range at purchase. Further, the user can “train” a product to ease control of the subject particularly for the user and enjoy training and adapting the product to the user's preference. In addition, since a genetic algorithm is used as means for effecting evolution, evolution of the subject can efficiently be achieved.
In the above, when the user's intention is determined based on the user's command and/or is presumed based on operation characteristics of the user when controlling the object, it is not necessary to determine the user's direct preference regarding the characteristics of the object to be controlled, such as quick start, smooth acceleration at high speeds, stability, etc. The determination of the user's direct preference is relatively difficult and requires time-consuming processing. By the above simplified processing, the characteristics which are close to the user's direct preference can finally be obtained after repeating the simplified processing. If the user's intention is determined solely by the user's command, the determination of the user's intention can easily be achieved. If the user's intention is determined solely by the user's presumed intention, the user's direct role required for determining the user's intention can be minimized. If any combination of the above two determination methods is adapted, the benefit rendered by each method can be realized at the same time.
When the primary framework of selection is automatically conducted but the final selection is assigned to the user, the user's role of selection can be reduced while maintaining enjoyment of “training”, i.e., adapting the subject to the user's preference. In the above, automatic selection can be conducted using an internal model formed by modeling behavior of the subject.
When a display indicating the characteristics of the subject is used, the user's role in the selection process can be reduced.
When smoothness of operation by the user which is likely to represent the user's intention is evaluated as an operation characteristic, and the smoothness of operation is computed based on the changes in degree of an operational action by the user and/or the frequency of the operational action by the user, the user's intention can easily be presumed by relatively simple processing.
When the range of changing in behavioral characteristics of the subject is limited, in the case of adopting the control system to an engine for a vehicle, for example, the engine can effectively be prevented from performing beyond the environmental standard for exhaust gas even if the engine characteristics are changed, i.e., it is possible to prevent an change in behavioral characteristics beyond the necessary.
In the foregoing, the user can retrieve any preferable characteristics stored in a memory at any time, thereby quickly changing the behavioral characteristics of the subject depending on the feeling of the user or the environment.
When the subject to be controlled is an engine for a vehicle, the characteristics of the engine can be changed to suit the user's preferences, thereby obtaining characteristics of the engine most suitable to each individual user.
When the subject to be controlled is a suspension system or seat, the characteristics of the damper of the suspension system or seat can be changed to suit the user's preferences, thereby obtaining characteristics of the damper most suitable to each individual user.
When the subject to be controlled is an auxiliary power unit installed in a bicycle or a wheelchair, the characteristics of the auxiliary power unit (motor) can be changed to suit the user's preferences, thereby effecting assistance most customized to each individual user.
When the subject to be controlled is a personal robot, the behavioral characteristics of the robot can be changed to suit the user's preferences, thereby obtaining behavioral characteristics of the robot most suitable to each individual user.
In addition, when the control system comprises neural networks learning relationships among the control characteristics using coupling coefficients, and the coefficients used as the genes are the co
Kamihira Ichikai
Yamaguchi Masashi
Chaki Kakali
Khatri Anil
Knobb Martens Olson & Bear LLP
Yamaha Hatsudoki Kabushiki Kaisha
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