Abstract:
A computing device stores a Bayesian network ( 20 ) which includes nodes ( 22 ) representing random variables and conditional probability indicating a dependence between the nodes, and at least one learned data table ( 30 ) in which a value of a random variable represented by the node included in the Bayesian network ( 20 ) is associated with a value of learned data inputted to the Bayesian network ( 20 ) concerning at least one of the nodes included in the Bayesian network; updates the learned data table ( 30 ); acquires the learned data inputted to the Bayesian network ( 20 ); and calculates a certainty factor of a value of a random variable represented by a node having a dependence with the node representing the random variable associated with the value of the learned data acquired by an acquisition section, at least based on the value of the random variable associated with the value of the learned data.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application is based on and claims priority under 35 USC 119 from Japanese Patent Application No. 2007-066458 filed Mar. 15, 2007. 
       BACKGROUND 
       [0002]    1. Technical Field 
         [0003]    The present invention relates to a computing device, a method of controlling the computing device, and a computer readable medium for recording a program. 
         [0004]    2. Related Art 
         [0005]    In general, in computing devices in which a model of a Bayesian network is implemented, each time learned data inputted to the Bayesian network is learned, conditional probability and the like included in the Bayesian network are updated to further improve the precision of the model of the Bayesian network. In general, since the conditional probability and the like included in the Bayesian network are updated based on all the learned data that has been accumulated so far, as more learned data is accumulated, the learned data which is newly learned has a relatively small effect on the model. For this reason, when a change occurs in the tendency of the values of the learned data, it may be difficult to adjust the model to a changed situation. 
       SUMMARY 
       [0006]    According to an aspect of the present invention, there is provided a computing device including: a storage section that stores a Bayesian network which includes nodes representing random variables and conditional probability indicating a dependence between the nodes, and at least one learned data table in which a value of a random variable represented by the node included in the Bayesian network is associated with a value of learned data inputted to the Bayesian network concerning at least one of the nodes included in the Bayesian network; an acquisition section that acquires the learned data inputted to the Bayesian network; and a certainty factor calculation section that calculates a certainty factor of a value of a random variable represented by a node having a dependence with the node representing the random variable associated with the value of the learned data acquired by the acquisition section, at least based on the value of the random variable associated with the value of the learned data. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0007]    An exemplary embodiment of the present invention will be described in detail based on the following figures, wherein: 
           [0008]      FIG. 1  is a functional block diagram of a computing device according to the exemplary embodiment of the present invention; 
           [0009]      FIG. 2  is a schematic diagram showing an example of a Bayesian network according to the exemplary embodiment of the present invention; 
           [0010]      FIG. 3  is a schematic diagram showing an example of the Bayesian network according to the exemplary embodiment of the present invention; 
           [0011]      FIGS. 4A and 4B  show example conditional probability tables that express the Bayesian network according to the exemplary embodiment of the present invention; 
           [0012]      FIG. 5  is a diagram showing a specific example of the Bayesian network according to the exemplary embodiment of the present invention; 
           [0013]      FIG. 6  is a flowchart of processing performed by a control device; 
           [0014]      FIG. 7  is a diagram showing a specific example of the Bayesian network according to the exemplary embodiment of the present invention; and 
           [0015]      FIG. 8  is a diagram showing a specific example of the Bayesian network according to the exemplary embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0016]    A computing device according to an exemplary embodiment of the present invention is configured, for example, by a known personal computer that includes a control device such as a CPU, a storage device such as a RAM and a hard disk, an output device such as a display, an input device such as a keyboard, and a communication device such as a network board. 
         [0017]      FIG. 1  is a functional block diagram showing a relationship among respective functions realized by the computing device  10 . As shown in  FIG. 1 , the computing device  10  includes, as functional components, a data input section  12 , a data acquisition section  14 , a calculation section  16 , and a storage section  18 . 
         [0018]    The data input section  12  is realized by the input device such as a keyboard. The data acquisition section  14  and the calculation section  16  are realized when the control device such as a CPU included in the computing device  10  executes a calculation program installed in the computing device  10 . The calculation program is supplied to the computing device  10  by an information transfer medium such as a CD-ROM and a DVD-ROM or via a communication network such as the Internet. The storage section  18  is realized by the storage device such as a RAM and a hard disk. 
         [0019]    The storage section  18  stores a Bayesian network. The Bayesian network includes nodes that represent random variables indicating uncertain events, a link that indicates a qualitative dependence between the nodes, and conditional probability that indicates a quantitative relationship between the nodes. 
         [0020]      FIG. 2  is a schematic diagram showing an example of a Bayesian network  20 . The Bayesian network  20  of  FIG. 2  includes five nodes  22 . The nodes  22  are linked by directed links  24 . 
         [0021]    This exemplary embodiment shows an example case where the level of email importance is calculated while focusing on a cause node  22   a  (representing a random variable X1) and a result node  22   b  (representing a random variable X2) shown in  FIG. 3  of the Bayesian network  20 . 
         [0022]    The cause node  22   a  represents the random variable X1 indicating an event that an email sender has a close relationship with the user. The random variable X1 can hold any of three values of 0, 1, and 2. For example, the value 2 indicates a person who has a close relationship with the user (Rank  1 ), the value 1 indicates a person who has a relationship with the user (Rank  2 ), and the value 0 indicates a person who has no relationship with the user (Rank  3 ). The result node  22   b  represents the random variable X2 indicating an event that the email is important. The random variable X2 can hold any of two values of 0 and 1. The value 1 indicates that the email is important, and the value 0 indicates that the email is not important. 
         [0023]    The directed link  24  links the cause node  22   a  to the result node  22   b  in the direction from the cause node  22   a  to the result node  22   b.  It is sufficient that the event indicated by the random variable X1 and the event indicated by the random variable X2 have a qualitative dependence, but there is no need to have a causal relationship. 
         [0024]    The cause node  22   a  of the Bayesian network  20  shown in  FIG. 3  is associated with a learned data table  30 . The learned data table  30  associates each of the values which can be held as the random variable X1 represented by the cause node  22   a  with a value (individual data) which can be held as learned data. In the example of the Bayesian network  20  shown in  FIG. 3 , individual data of people (Mr. A to Mr. E and other people) who can be senders of emails is associated with each rank of senders. The learned data table  30  shown in  FIG. 3  is stored in the storage section  18 . 
         [0025]    The Bayesian network  20  shown in  FIG. 3  may express a quantitative relationship between the respective nodes  22  by using conditional probability tables  40   a  and  40   b  shown in  FIGS. 4A and 4B . The conditional probability tables  40   a  and  40   b  are stored in the storage section  18 . 
         [0026]    An example case is shown in which a certainty factor of a value of the random variable X2 is calculated while the conditional probability tables  40   a  and  40   b  shown in  FIGS. 4A and 4B  are applied to the specific example of  FIG. 3 . Note that the certainty factor is a numeric value indicating the probability of judgment, and is expressed by a real number which falls in the range from 0 to 1 inclusive. 
         [0027]    For example, when an email sender is Mr. A, it is found from the learned data table  30  shown in  FIG. 3  that this email sender is associated with Rank  1 , so the value of the random variable X1 is 2. In this case, from the conditional probability table  40   b  shown in  FIG. 4B , the certainty factor in which the value of the random variable X2 is 1, in other words, the certainty factor in which the email is important, is calculated to be 0.8. Further, the certainty factor in which the value of the random variable X2 is 0, in other words, the certainty factor in which the email is not important, is calculated to be 0.2. 
         [0028]    On the other hand, for example, when an email sender is a person other than Mr. A to Mr. E, it is found from the learned data table  30  shown in  FIG. 3  that this email sender is associated with Rank  3 , so the value of the random variable X1 is 0. In this case, from the conditional probability table  40   b  shown in  FIG. 4B , the certainty factor in which the value of the random variable X2 is 1, in other words, the certainty factor in which the email is important, is calculated to be 0.1. Further, the certainty factor in which the value of the random variable X2 is 0, in other words, the certainty factor in which the email is not important, is calculated to be 0.9. 
         [0029]    It is assumed that individual data indicating Mr. A is updated from Rank  1  to Rank  2  in the learned data table  30  as shown in  FIG. 5 . When email is sent from Mr. A after the learned data table  30  has been updated, the value of the random variable X1 is 1. In this case, from the conditional probability tables  40   b  shown in  FIG. 4B , the certainty factor in which the value of the random variable X2 is 1, in other words, the certainty factor in which the email is important, is calculated to be 0.6. Further, the certainty factor in which the value of the random variable X2 is 0, in other words, the certainty factor in which the email is not important, is calculated to be 0.4. 
         [0030]    Next, a description is given of processing performed in the computing device  10  according to the exemplary embodiment of the present invention, with reference to the functional block diagram shown in  FIG. 1  and a flowchart shown in  FIG. 6 . 
         [0031]    The storage section  18  stores in advance the conditional probability tables  40   a  and  40   b  shown in  FIGS. 4A and 4B . The storage section  18  also stores the learned data table  30  shown in  FIG. 3 . 
         [0032]    The data acquisition section  14  acquires learned data through the data input section  12  (S 101 ) In this exemplary embodiment, the learned data includes the name of an email sender. The calculation section  16  retrieves, from the learned data table  30 , individual data corresponding to the name of an email sender included in the learned data (S 102 ). The calculation section  16  obtains, from the learned data table  30 , a value of the random variable X1 corresponding to the retrieved individual data (S 103 ) The calculation section  16  learns the learned data as needed (S 104 ). Specifically, the values of the conditional probability tables  40   a  and  40   b,  stored in the storage section  18 , are updated. A series of these steps are repeated until no learned data is left (S 105 ). 
         [0033]    Hereinafter, modifications of this exemplary embodiment will be described. 
         [0034]    For example, the following application examples are conceivable. As shown in  FIG. 7 , the Bayesian network  20  includes the result node  22   b  (representing the random variable X2) indicating an event that a scientific paper is important, the cause node  22   a  (representing the random variable X1) indicating an event that the author has a relation with the area of study of the user, and the directed link  24  connecting the cause node  22   a  to the result node  22   b.  The cause node  22   a  is associated with the learned data table  30  in which a person who can be an author is associated with a rank indicating the degree of relation with the area of study of the user. 
         [0035]    Further, as shown in  FIG. 8 , the Bayesian network  20  includes the result node  22   b  (representing the random variable X2) indicating an event that a document is important, the cause node  22   a  (representing the random variable X1) indicating an event that an important keyword is included in the document, and the directed link  24  connecting the cause node  22   a  to the result node  22   b.  The cause node  22   a  is associated with a first learned data table  30 - 1 . In the first learned data table  30 - 1 , each value which can be held as the random variable X1, represented by the cause node  22   a,  is associated with a keyword rank. Further, the keyword rank is associated with a second learned data table  30 - 2 . The keyword rank is assigned to each keyword co-occurrence relation. 
         [0036]    Note that the learned data table  30 , shown in  FIG. 3  and the like, does not necessarily indicate ranks. 
         [0037]    Further, the single Bayesian network  20  may include multiple learned data tables  30  such as that shown in  FIG. 3 . 
         [0038]    The data acquisition section  14  may acquire learned data stored in the storage section  18 , instead of acquiring learned data through the data input section  12 . 
         [0039]    The present invention is not limited to the above-described exemplary embodiment. It is needless to say that the present invention can be widely applied to a system in which electronic documents are accumulated in a server, and when user authentication is performed in an information processor connected to the server via a network, and when an authenticated user is the user who owns the electronic documents or their agent, the electronic documents are sent to the information processor.