JP2007014680A - Character growing method in simulation of game - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To allow a character to attain an intended level by a necessary minimum training by eliminating the waste of the training. <P>SOLUTION: When a terminal PC 11a is going to start a battle game, at first, a preliminary simulation is executed by a virtual character on an infinite (namely, invincible) capacity level, and a target capacity level B where a character starting the battle from now should be attained for clearing (winning) is set. The terminal PC 11a extracts the present capacity level A of the character going to start the battle from the database 1a via a central control server 1, and plans and executes a minimum training for clearing the target capacity level B based on a difference (B-A) between the target capacity level B and the present capacity level A of the character. When the difference (B-A) becomes zero, the terminal PC 11a stops the execution of the training. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a character training method in a game simulation for executing a computer game, and more particularly to a character training method in a game simulation when a computer game is played separately for a teammate character and an enemy character.

  Examples of the computer game include a role-playing game in which an ally character and an enemy character grow while adventuring (game) (see, for example, Patent Document 1). In games that include elements that allow the character to grow, such as role-playing games, etc., each character has its own personality, nature, or psychology, and the internal personality of each character. Can be clearly communicated to the player. Therefore, each character can be grown according to the individuality as the player intends. As the character grows in this way, the character's ability increases and the player can further challenge the enemy character or challenge difficult events, making the game more fun. can do.

  FIG. 6 is a flowchart showing the flow of a conventional battle simulation game. In FIG. 6, when the initial level (initial ability) X of the handheld character is A (that is, X = A) (step S51), a battle simulation is executed with the enemy character by the user's operation, and training ( (Training) is performed (step S52). Then, the result of training is analyzed (step S53), and the level X ′ of the character after training is analyzed to determine whether or not the ability is improved from the initial level (initial ability) X (that is, X ′ = X ±). It is determined whether or not α (step S54).

  Here, it is determined whether or not the level X ′ after the improvement of the skill has reached the pass level of combat training (that is, whether X ′ ≧ pass level) (step S55), and X ′ ≧ pass level. If there is (Yes in Step S55), it is assumed that the character on hand has cleared the mission for performing the battle training (Step S56), and the process is terminated. On the other hand, if X ′ ≧ pass level is not satisfied in step S55 (No in step S55), it is assumed that the character on hand has not cleared the mission for performing the battle training (step S57), and the process returns to step S52 and again. Run a battle simulation to train and repeat the same steps as above.

That is, as shown in FIG. 6, in the conventional battle simulation game, the battle simulation is training itself, in which a battle simulation is performed with a hand-held character and the characteristics of the character are improved based on the result (that is, , Battle simulation, equal training).
JP 2001-157778 A

  However, in the simulation flow of the battle simulation / equal training as shown in FIG. 6, training beyond the level necessary to achieve the target is performed, and unnecessary training occurs. End up. FIG. 7 is a characteristic diagram showing waste generated during simulation when the flow of the conventional battle simulation game shown in FIG. 6 is executed. In this characteristic diagram, the horizontal axis represents the training level, and the vertical axis represents the achievement level of the ability (that is, the level of execution ability of battle training).

  As shown in FIG. 7, the achievement level increases as the training progresses, such as the first training, the second training, and the third training, but has not yet reached the passing level in the third training. . Therefore, when the fourth training is performed, the achievement level of the ability completely clears the passing level.

  However, as shown by the vertical axis in FIG. 7, the passing level is far exceeded between the third training and the fourth training, and thus “wasted attainment level” occurs. In addition, as shown on the horizontal axis in FIG. 7, training after reaching the pass level is performed between the third training and the fourth training, so that “training wasted” occurs. This means that the character's level has reached an excessive level higher than the mission he wants to achieve this time by performing the fourth training, even though the character only needs to reach the passing level. To do. This is a good result for a comprehensive battle simulation, but reaching more levels than necessary requires more training time and effort than that. That is, an excessive level above the pass level is a waste of the achievement level, and training beyond the pass level is a waste of training.

  For example, when a sudden incident occurs in an urban area or the like, the trouble must be solved in a short period of time. In that case, once the passing level is reached with the minimum necessary training, it is necessary to respond to a sudden incident immediately, so excessive level of training or excessive training is simply wasted. In other words, performing training beyond the level necessary to achieve the goal will waste the extra level and the training itself.

  The present invention has been made in view of the above problems, and eliminates the waste of training caused by the approach of simulation and equal training in a battle simulation game. An object of the present invention is to provide a character training method in a game simulation that can achieve the desired level.

  In order to solve the above-described problem, a character training method in a game simulation according to the present invention is a character training method in a game simulation for improving a character processing ability level by a learning effect by training, and a preliminary simulation by a virtual character Based on the step of setting the target processing ability level B to be reached by the character and the difference (BA) between the target processing ability level B and the current processing ability level A of the character, It includes a step of executing a minimum training for clearing, and a step of stopping the execution of training when (B-A) becomes zero.

  According to the character training method in the game simulation according to the present invention, for example, by performing a preliminary simulation with a virtual character having an infinite processing capability (that is, an invincible virtual character), the ally character battles with the enemy character. When performing, it is possible to grasp the processing capability level (target processing capability level) necessary for the teammate character to win (clear) the battle. Also, the teammate character can check the characteristics and abilities that are lacking in the handheld character. Therefore, it is possible to determine the minimum training content necessary to make up for the shortage and perform the minimum training to achieve the goal, so it is possible to eliminate unnecessary training and perform efficient training it can.

  According to the character nurturing method in the game simulation according to the present invention, the level required to clear the predetermined target can be confirmed in advance by performing the preliminary simulation with the virtual character. Therefore, the target can be cleared if training is performed to replenish the currently insufficient level (insufficient level), so that efficient and effective training can be performed.

<Outline of the invention>
The character training method in the game simulation according to the present invention is a battle simulation game in which a battle simulation training is performed by a hand-held character and the characteristics of the character are improved based on a learning result of the training. By performing the simulation, the level necessary for clearing the desired battle content is estimated, and the characteristics that are lacking in the handheld character are analyzed. Then, in order to make up for a portion that is insufficient to clear the desired battle content, the minimum training content is determined and training is performed again. As a result, it is possible to effectively clear desired battle contents while performing efficient training by eliminating useless training.

  In other words, the present invention analyzes characteristics required by performing a preliminary simulation with an ideal virtual character (that is, a virtual character with an infinite level) in a battle simulation game, and performs the necessary minimum training of the character. The mission is successful. Thereby, the efficiency of character training in the battle simulation game can be effectively improved.

  Hereinafter, an embodiment of a character training method in a game simulation according to the present invention will be described in detail with reference to the drawings, taking a battle simulation as an example. First, what kind of game is the battle simulation performed by the game device applied to the present invention will be specifically described. First, the most efficient tactics necessary to complete a battle mission are simulated, and a training menu for characters (characters) is determined to realize the tactics determined by the simulation. Then, assuming the system as a game, the simulation is expressed according to the following system operation flow.

That is, the flow of system operation is a repetition of the following five procedures.
(1) Perform a battle simulation for the target mission to determine the most efficient tactic.
(2) Based on the past use record data stored in the database, the optimum team training menu is selected from past similar patterns such as the battle environment, the firearms, and the number of constituent members.
(3) Select a personal training menu for each member that is configured as a team training menu.
(4) Conduct training in the order of individual training menu ⇒ team training menu and confirm the results.
(5) Update the usage record data stored in the database according to the confirmed result.
Then, the completion of the training menu pattern search is improved by repeating the procedures (1) to (5).

  The battle results data stored in the database is as follows. The first accumulated data is data relating to the results of the tactics implemented this time and the results after the training as the usage record data. The second accumulated data is preset data such as various known patterns / results, battle environment data in simulation, firearm data such as ammunition number and flight distance, and information on characters (characters) and arrival levels. It is data.

  The data stored in the database when developed as an actual battle training system includes battle training result data, personal information, and superior evaluation data in the battle training apparatus.

  FIG. 1 is a system configuration diagram of hardware for constructing a battle training simulator implemented in a game device that realizes a character training method in battle simulation according to the present invention. That is, the hardware configuration and system configuration of the training simulator implemented in the present invention include a centralized management server 1 having a database (not shown), a simulation execution PC (for examining tactics and training menus) as shown in FIG. A personal computer 2, a plurality of preset data input PCs 3 a to 3 n, and a battle training apparatus 4.

  Each of the plurality of preset data input PCs 3a to 3n inputs preset data such as known battle result data, battle environment data in simulation, firearm data used, and character arrival level. The simulation execution PC 2 performs a battle simulation for the target mission based on the preset data input by each of the preset data input PCs 3a to 3n and the training result data stored in the central management server 1 database. Implement to determine the most efficient tactic. The battle training device 4 is necessary only when actually developing a shooting training system, and is not required at the stage of executing a battle simulation and determining a tactic.

  FIG. 2 is a system management diagram of a battle training simulator implemented by a game device that implements the character training method in the battle simulation according to the present invention. The difference between FIG. 1 and FIG. 2 is that FIG. 1 is a diagram showing a system configuration of hardware for constructing a battle training simulator, while FIG. 2 is a diagram showing system management of hardware for constructing a battle training simulator. It is a point. Therefore, in FIG. 2, the simulation execution PC and the preset data input PC of FIG. 1 are configured as one terminal PC (for example, the terminal PC 11a).

  That is, the system management configuration of the battle training simulator shown in FIG. 2 includes a central management server 1 having a database 1a, a simulation execution function (corresponding to the simulation execution PC 2 in FIG. 1), and a preset data input function (preset in FIG. 1). And a plurality of terminal PCs 11a to 11g having data input PCs 3a to 3n). The plurality of terminal PCs 11a to 11g are PCs for battle training, respectively. In FIG. 2, the battle training apparatus shown in FIG. 1 is omitted.

  First, the operation of the battle game performed by the battle training simulator of FIG. 2 will be schematically described. When the terminal PC 11a tries to start a battle game, first, a preliminary simulation is performed with an infinite (that is, invincible) virtual character with an infinite processing capability level, and the character starting the battle from now on clears the battle (wins) A target processing capacity level B to be reached is set. Furthermore, the terminal PC 11a extracts the current processing ability level A of the character that is about to start a battle from the database 1a via the central management server 1, and the target processing ability level B and the current processing ability level A of the character Based on the difference (B−A), the minimum training for clearing the target processing power level B is planned and executed. And terminal PC11a stops execution of training, when (BA) becomes zero. This eliminates training waste and allows the character to be achieved to a desired level with the minimum necessary training.

  Next, the operation of the battle training simulator shown in FIG. 2 will be described in more detail. The terminal PC 11a (PC-A), which is a PC for battle training, acquires past training record information of characters from the database 1a of the central management server 1 (step S1), and executes a battle simulation for the target mission. The most efficient tactic is determined (step S2), and at that time, the optimum training is performed to train the character (step S3). Then, the terminal PC 11a (PC-A) transmits the latest training result as character information of the character to the central management server 1 and registers it in the database 1a (step S4). Hereinafter, the battle training PC 11b (PC-B) to the battle training PC 11g (PC-G) perform the same operations as the battle training PC 11a (PC-A), respectively.

  Next, in order to facilitate understanding, the flow of operations in which the game device applied to the present invention plays an actual game will be described in comparison with the flow of operations by the conventional game device. In the conventional game device, as shown in FIG. 6, when the test of the result of training based on the past experience value fails, the training is performed again based on the experience value based on the training result. Do. If the test of the training result again fails, the training is repeated several times based on the experience value based on the training result again. When the training is repeated in this way, even if the passing level is reached during the training, the training of that time is continued. Therefore, the last training becomes a useless training state from the middle.

  On the other hand, in the game device according to the present invention, an optimal training menu is determined based on accumulated data based on past training results and the result of the current battle simulation. If the result of the training performed according to the optimal training menu fails, the training menu is determined again based on the accumulated data and the training is performed. In this way, the test passes as the training is repeated.

  Here, in the game device according to the present invention, the accumulated data increases each time the training is repeated, so that the probability of passing the test with a smaller number of trainings than before becomes high. Further, according to the optimal training menu at that time, training that is slightly above the passing line is performed, so training is less wasteful than in the past.

  Next, a flow of battle simulation processing in the game device according to the present invention will be described with reference to a flowchart. FIG. 3 is a flowchart showing a flow of pre-battle simulation processing by an invincible virtual character in the game device according to the present invention.

  In FIG. 3, when a user (player) starts a pre-battle simulation before playing a battle game on a game device, a virtual character with an infinite level is placed (step S11). Then, an enemy character determined by automatic analysis by the simulator appears (step S12). As a result, the user (player) sets the opposing action of the virtual character by his operation (step S13). Then, by the automatic analysis by the simulator, the execution of the battle simulation by the ally virtual character and the enemy character is put on standby (step S14).

  As a result, the user determines whether or not the battle simulation is OK even after starting the battle simulation (step S15). If the battle simulation cannot be started yet (NG in step S15), the battle simulation is performed. The action is re-examined (step S16), the process returns to step S13, the counter action of the virtual character is reset, and the processes after step S14 are repeated.

  On the other hand, when the start of the battle simulation is OK in step S15 (in the case of OK in step S15), the request level for one operation is analyzed by automatic analysis by the simulator (step S17). Further, the total required level in the battle simulation results so far is analyzed by automatic analysis by the simulator (step S18).

  Further, it is determined by the user's judgment whether or not there is a next battle simulation (step S19). If there is a next battle simulation (if step S19 is present), the process returns to step S12 and the simulator automatically The next enemy character determined by the analysis appears, and the processing from step S13 onward is repeated.

  In step S19, if there is no next battle simulation (if not in step S19), an analysis is performed to determine whether or not the character has reached the required level of level B according to the result of the battle simulation by automatic analysis by the simulator. It performs (step S20). Further, the automatic analysis by the simulator confirms that the hand-held character has an initial level X after the battle simulation of A (step S21).

  Then, the difference (B−A) between the required level B and the initial level A after the battle simulation is obtained by automatic analysis by the simulator, and whether the level is insufficient is analyzed (step S22). Further, if the level is insufficient by the automatic analysis by the simulator, the training content for shortage supplementation is determined according to the level shortage (step S23), and the process is terminated.

  That is, as shown in the flowchart of FIG. 3, by adding a battle simulation function with an invincible virtual character, it is possible to determine the minimum training content for passing the required level in the next training, There can be no training.

  At this time, in the step execution image, a battle simulation is performed for each battle training, and the best battle pattern is found. Then, based on the result of each training, the required level, the characteristics that are lacking in the handheld character, and the like are analyzed, and the minimum training content necessary to make up for the lack of level is determined.

  FIG. 4 is a flowchart showing the flow of processing for executing the actual battle simulation in response to the result of the pre-battle simulation processing of FIG. As shown in FIG. 4, when the minimum training content (that is, training content not performed as training) for reaching the required level B is determined in the process of the pre-battle simulation of FIG. 3 (step S31). The necessary minimum training is performed, and α is added to the initial level X in the level X ′ after the training is performed. That is, X ′ = X + α (step S32).

  Then, it is determined whether or not the level X ′ after the training has reached the required level B or more (X ′ ≧ B?) (Step S33). If X ′ ≧ B has not been reached (No in step S33), the process returns to step S32 to repeat the above-described processing to obtain X ′ = X + α, and it is determined whether X ′ ≧ B. .

  On the other hand, if the level X ′ after execution of training is equal to or higher than the required level B (X ′ ≧ B) in step S33 (Yes in step S33), a battle simulation after the character reaches the required level B is performed (step S34). ). The battle simulation at this time is not training. And the battle result by battle simulation is analyzed (step S35), and a predetermined battle mission is cleared (step S36). In other words, as can be seen from FIG. 3 and FIG. 4, by performing the preliminary battle simulation with virtual characters, it becomes possible to recognize the level required to complete the battle mission, so the minimum training is necessary. It will be good.

  FIG. 5 is a characteristic diagram showing how waste during simulation execution is reduced by performing a preliminary battle simulation. In FIG. 5, the horizontal axis represents the training level, and the vertical axis represents the arrival level. As shown on the vertical axis in FIG. 5, the necessary level for reaching the pass level B is analyzed by performing the pre-battle simulation (step S <b> 41).

  Furthermore, if the current level of the character is A, the deficiency level (BA) until reaching the pass level B is analyzed (step S42). Then, as shown on the horizontal axis of FIG. 5, the training content for compensating for the deficiency level (BA) is determined (step S43). Furthermore, when the battle simulation is performed according to the determined training content, the deficiency level (B-A) decreases and the training level increases as the training progresses, as shown by the rising characteristics of the diagonal lines in FIG. Step S44), eventually, the first pass level is reached (Step S45).

  In this way, a battle simulation is performed only for the shortage level (B-A) for all of the characters on hand. In other words, after analyzing the characteristics that hand-held characters lack in order to complete similar battles, the minimum training necessary to compensate for the deficiency level is performed to efficiently achieve the goal of the passing line. Can be realized.

  Further, by constructing a system that realizes a characteristic diagram as shown in FIG. 5, the centralized management server 1 collects profile data of a plurality of characters from a plurality of battle game terminals PCs 11a to 11g as shown in FIG. Is registered in the database 1a and can be referred from each of the terminal PCs 11a to 11g, so that “character: user who develops characters = 1: N” can be realized. That is, character training can be performed for a large number of users (players) using one character. Furthermore, by applying the system according to the present invention to an actual battle training system, it can be expanded to a member training system.

It is a system configuration | structure figure of the hardware which builds the battle training simulator implemented with the game device which implement | achieves the character training method in the battle simulation which concerns on this invention. It is a system management figure of the battle training simulator implemented with the game device which implement | achieves the character training method in the battle simulation which concerns on this invention. It is a flowchart which shows the flow of the process of the prior battle simulation by an invincible virtual character in the game device which concerns on this invention. It is a flowchart which shows the flow of the process which receives the result of the process of the prior battle simulation of FIG. 3, and performs a real battle simulation. It is a characteristic view which shows a mode that the waste at the time of simulation execution is reduced by performing a prior battle simulation. It is a flowchart which shows the flow of the conventional battle simulation game. It is a characteristic view which shows the waste which generate | occur | produces at the time of the simulation at the time of performing the flow of the conventional battle simulation game shown in FIG.

Explanation of symbols

1 Centralized management server 1a Database 2 PC for simulation
3a, 3b ... 3n Preset data input PC
4 battle training apparatus 11a, 11b, 11c, 11d, 11e, 11f, 11g Terminal PC

Claims (1)

A character nurturing method in a game simulation for improving the processing ability level of a character by a learning effect by training,
Setting a target processing ability level B to be reached by the character by preliminary simulation with a virtual character;
Performing minimal training to clear the target processing power level B based on the difference (BA) between the target processing power level B and the current processing power level A of the character;
Stopping the execution of the training when (B-A) becomes zero;
A character nurturing method in a game simulation characterized by comprising:

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