JP7231997B2 - Game machine evaluation device and game machine evaluation program - Google Patents

Description

The present invention relates to a game machine evaluation device and a game machine evaluation program for evaluating the fun of a game machine.

Players of pachinko and other gaming machines are given a sense of satisfaction by playing balls and winning prizes, and it is believed that repeated satisfaction leads to an improvement in the operation rate of the gaming machine. .

However, in recent years, it has become difficult to attract customers with so-called high gambling characteristics, such as the implementation of measures against gambling addiction. In such a situation, gaming machines are required to give pleasure to players even when gambling is low. Therefore, in the development of amusement machines, what kind of amusement machines can be expected to bring pleasure to players and be highly utilized. rate is difficult to predict.

JP 2005-168605 A

Current game machine evaluation criteria include the number of units sold and operating rate, but it is difficult to make highly accurate predictions of these factors. For example, there are cases in which operation predictions are made based on questionnaires of players who have actually played the game. Items such as good ball output, good content, brand evaluation, ratio to appropriate sales volume for that machine, how to use the hall (how to turn, how to put in settings) are evaluated, and these are evaluated by multivariate analysis. Although it is structured and scaled, such evaluation cannot escape the realm of subjective evaluation.

The present invention has been made in consideration of the above problems, and its object is to break away from the subjective evaluation of gaming machines and evaluate the gaming machines through objective analysis, thereby providing a player with a sense of pleasure. It is to enable the development of a game machine that can bring

In order to solve the above-mentioned problems, the gaming machine evaluation device according to the present invention stores an effect flow, which is a flow of effects actually appearing in the gaming machine, from the hold change to the pre-reach notice to the reach-in notice. an appearance storage unit for storing, as appearance rate information, the probability of appearance of each production flow stored in the production storage unit; and a case where each production flow stored in the production storage unit appears. a reliability storage unit that stores the probability of reaching the point as reliability information ; a dopamine value calculation unit that calculates the value of dopamine that is estimated to be secreted by the player according to the performance flow; and the appearance rate information and the reliability information. and an evaluation unit that evaluates the gaming machine based on the dopamine value calculated by the dopamine value calculation unit .

The game machine evaluation device according to the present invention evaluates a game machine using appearance rate information, which is the probability of each effect appearing, and reliability information, which is the probability of winning from each effect. Therefore, the evaluation is not subjective as in the past, but is based on information obtained by objectively quantifying the actual behavior of the game machine. becomes.

1 is a hardware block diagram showing a configuration example of a gaming machine evaluation device according to a first embodiment; FIG. 1 is a functional block diagram showing a configuration example of a gaming machine evaluation device according to a first embodiment; FIG. FIG. 10 is a comparison diagram showing an example of effect distribution for multiple models; FIG. 11 is a functional block diagram showing a configuration example of a gaming machine evaluation device according to a second embodiment; It is a graph which shows the transition example of dopamine. FIG. 11 is a functional block diagram showing a configuration example of a gaming machine evaluation device according to a third embodiment;

1. First Embodiment A game machine evaluation device 1 shown in FIG. 1 is a device for evaluating game machines by model, and includes a CPU 11 for performing various calculations and judgments, a ROM 12 for storing control programs executed by the CPU 11, A RAM 13 for storing application programs for evaluating the game machine and data as necessary in processing by the CPU 11, a recording unit 14 including a hard disk drive or the like for storing various data, and a processing result by the CPU 11 and a recording unit 14. and an input section 16 for inputting data to be stored in the recording section 14. These are connected to each other by an internal bus. In the game machine evaluation device 1, the control program stored in the ROM 12 and the application program stored in the RAM 13 are executed by the CPU 11, thereby realizing the functions of the parts shown in FIG.

As shown in FIG. 2, the game machine evaluation device 1 stores an effect storage unit 21 that stores effects that actually appear in the game machine, and stores the probability of appearance of each effect stored in the effect storage unit 21 as an appearance rate. an appearance rate storage unit 22, a reliability storage unit 23 for storing, as reliability, the probability of winning when each effect stored in the effect storage unit 21 appears, and an appearance rate and reliability for each effect. and an evaluation unit 25 for evaluating the gaming machine based on the effect distribution representing the relationship between the appearance rate and the reliability.

All effects are stored in the effect storage unit 21.例文帳に追加The production includes a production of suspension, a production of advance notice before reach, a production of notice during reach, and the like, and each production belongs to one of these stages. For example, the effect of hold includes the color when the hold lamp lights up, the sound of the hold, etc., and the effect of notice before reach includes change in display color, change in color of dialogue, cut-in, etc., and notice during reach. Production includes pseudo-ren, cut-in, reach development, etc.

A game machine, for example, a pachinko machine is played, and when a pachinko ball wins a start chucker, a lottery is started with a predetermined probability. Therefore, the subsequent prize winning is stored for a fixed number of times, and the subsequent lottery operation is performed after the previous lottery operation is completed. This is called hold.

As a result of the lottery, a performance action leading up to winning occurs with a predetermined probability. In many gaming machines, a slot is displayed on the main digital display, and if the pattern of the slot is aligned in any direction, you win. In such a game machine, a state in which two symbols are aligned in either direction is defined as a ready-to-win state, and by performing a ready-to-win effect display on the main digital, the expectation for winning is increased. Before the ready-to-win effect is started, an effect to foretell that the ready-to-win effect will be performed may be performed. This production is referred to as pre-reach notice. In addition, the degree of anticipation for the hit is heightened by performing a performance from entering the ready-to-reach state to finally performing the hit determination. This advance notice effect operation is referred to as advance notice during reach.

In addition, cut-in is a production in which a cut that has nothing to do with the flow up to that point is inserted in the middle of the notice, and pseudo-ren is actually only one rotation of the digital, but it looks like it. By devising the production, it is a production that makes it seem as if the notice is continuous over several rotations.

The appearance rate storage unit 22 stores appearance rates of individual effects stored in the effect storage unit 21 . The appearance rate is obtained by dividing the number of appearances during multiple (for example, several thousand) games by the number of games played for each effect.

A reliability storage unit 23 calculates and stores a probability of winning for each performance when each performance stored in the performance storage unit 21 appears as a reliability. The reliability is obtained by dividing the number of hits for each individual effect by the total number of appearances of that effect during a plurality of games.

The effect distribution generation unit 24 sets the appearance rate on the horizontal axis and the reliability on the vertical axis, for example, as in the effect distribution comparison diagram shown in FIG. The rate and the reliability stored in the reliability storage unit 23 are plotted in association with each other. Effect Distribution Comparison FIG. 3 shows effect distributions for a plurality of models. The effect distribution comparison diagram 3 is generated for each of the effect of suspension, the effect of advance notice before reach, the effect of advance notice during reach, and the like. Effect distribution comparison FIG. 3 plots the distribution of individual effects in a plurality of game machine models, so that it is possible to grasp how the distribution of appearance rate and reliability of effects differs for each model.

The evaluation unit 25 evaluates the game machines for each model based on the effect distribution comparison diagram 3 . The evaluation unit 25 evaluates based on the areas where the plots are concentrated in the effect distribution comparison diagram 3, how the plots are distributed as a whole, the relative relationship with the distribution of other models, and the like.

When evaluating a gaming machine using the gaming machine evaluation device 1, a plurality of gaming machines to be evaluated are actually played the same number of times (for example, 3000 times), and an effect that appears is produced for each gaming machine. It is stored in the storage unit 21 . Here, the models to be evaluated are assumed to be three models. All renditions belong to the pending, pre-reach, or in-reach stage. In addition, a plurality of gaming machines have the same winning probability (for example, 1/320).

When all effects are stored in the effect storage unit 21, the appearance rate storage unit 22 calculates and stores the appearance rate of each effect for all effects. Further, the reliability storage unit 23 calculates and stores the reliability for all effects.

Next, the effect distribution generation unit 24 plots individual effects in the effect distribution comparison diagram 3 of FIG. The production distribution comparison diagram 3 is generated for each stage of hold, advance notice before reach, and advance notice during reach. Note that the effect distribution comparison diagram 3 shown in FIG. 3 shows the relationship between the appearance rate of the effects appearing in the advance notice during reach and the reliability for the model 1-3.

Next, the evaluation unit 25 evaluates each gaming machine based on the effect distribution diagram in each stage of hold, advance notice before reach, and notice during reach. For example, the evaluation unit 25 divides the effect distribution comparison diagram 3 into several areas, and grasps which area has a large number of plots.

Indicators for evaluating gaming machines include whether or not the reliability is dispersed, whether or not the appearance rate is too low, and the like. For example, model 1 in the effect distribution comparison diagram 3 of FIG. It is thought that there is a low possibility of bringing pleasure to people.

Moreover, in model 1, there are many effects whose reliability has reached 100%.

Also, for model 3, for example, since the reliability is too dispersed, even if the performance appears, it is difficult to expect a win, and the distribution is such that it is difficult to obtain a pleasant feeling.

Various determinations such as those described above are made by setting, for example, the input from the input unit 16 shown in FIG. The evaluation unit 25 preliminarily recognizes areas where it is easy to get pleasure when the degree of concentration is high, areas where it is difficult to get pleasure when the degree of plot concentration is low, and areas where pleasure is easy to get when the degree of plot concentration is low. This can be done automatically. For example, if a region with a reliability of 20% or less and a region with a reliability of 70% or more are set as regions where it is easy to get pleasure when the degree of concentration is high, plots are actually concentrated in these regions. As for amusement machines, it is highly likely that the utilization rate will increase. With this setting, among models 1-3, model 2 has the most concentrated plots in areas where it is easy to get pleasure, and among models 1-3, model 2 has the highest operating rate. can be judged to be promising.

It should be noted that the evaluation of the gaming machine can also be made by the evaluator actually seeing the effect distribution comparison diagram 3 with his own eyes.

Whether or not the presentation distribution matches the developer's intention is also an index for evaluating the game machine. For example, if the developer wanted to set the reach warning confidence to be low, but the plot is actually concentrated in areas where the reach warning confidence is high, it may not be what the developer intended. It can be evaluated that the possibility of high utilization is low. Such an evaluation is performed by using, for example, the input unit 16 shown in FIG. The evaluation unit 25 judges whether or not the area where the concentration of the plots is high matches. Alternatively, the ideal degree of concentration of the designated area may be input from the input unit 16, and the evaluation unit 25 may compare and evaluate the degree of concentration with the actual degree of concentration.

It is also possible to estimate whether or not there is a high possibility that the evaluation target model will become popular in comparison with the effect distribution of models that were popular in the past. For example, the effect distribution of the past popular models is superimposed on the effect distribution comparison diagram 3, and the evaluation unit 25 determines whether the past popular models and the areas with high degree of concentration coincide with the past popular models among the models to be evaluated. It can be judged that the model with a high probability of becoming popular also has a high possibility of becoming popular.

As described above, the effect distribution generation unit 24 generates the effect distribution comparison diagram 3, and the evaluation unit 25 can judge the gaming machine for each model based on the effect distribution. Machine evaluation becomes possible. By changing the degree of appearance and the degree of reliability of each effect based on this evaluation, it is possible to develop a gaming machine that makes it easier for the player to feel pleasure. In addition, since the effect distribution comparison diagrams 3 are generated for a plurality of models, it is possible to grasp the characteristics in comparison with other models. moreover. By generating a production distribution comparison diagram 3 for each of hold change, pre-reach notice, and reach-in notice, the difference in production distribution from other models is grasped for hold change, pre-reach notice, and reach-in notice, respectively. Therefore, it is possible to grasp the characteristics of the model more clearly.

2. Second Embodiment A gaming machine evaluation device 4 shown in FIG. 4 has the same hardware configuration as the gaming machine evaluation device 1 shown in FIG. By executing the application program in the CPU 11, the function of each part of the gaming machine evaluation device 4 shown in FIG. 4 is realized.

The game machine evaluation device 4 evaluates the game machine based on brain science. In brain science, dopamine is produced in the human brain when a desire is satisfied, or when a desire is found to be satisfied. It is believed to cause pleasure in humans. The reward system is activated not only when a desire is satisfied, but also during behavior in anticipation of receiving a reward. It has been demonstrated that the total amount of dopamine is greater in the case of giving a reward than in the case of giving a reward without prior notice of the reward. Also, the winning of a pachinko ball in the start chucker, and the shift to winning through suspension, advance notice before reach, and notice during reach can also be considered as a combination of notice and reward in the above brain science. Animal experiments have also revealed the ratio of the dopamine at the time of advance notice and the dopamine at the time of the actual reward, when the reward is given after the advance notice of the reward.

The game machine evaluation device 4 includes a performance storage unit 41 that stores a performance flow that is a flow of a plurality of performances or performances that have actually appeared in the gaming machine, and a probability that the performance or performance flow stored in the performance storage unit 41 will appear. as an appearance rate, a reliability storage unit 43 for storing, as a reliability, the probability of winning when the performance or performance flow stored in the performance storage 41 appears, and A pattern storage unit 44 that stores transition patterns of dopamine levels in advance, and a search unit 45 that searches the pattern storage unit 44 and determines which transition pattern the effect or effect flow stored in the effect storage unit 41 corresponds to. , a dopamine value calculation unit 46 for calculating a dopamine value that is likely to be secreted by the player by each effect flow based on the judgment in the search unit 45 and the appearance rate information of each effect stored in the appearance rate storage unit 42; A total dopamine amount calculation unit 47 that calculates the total dopamine amount of the game machine using all values of the dopamine value calculated by the value calculation unit 46; and an evaluation unit 48 that evaluates the gaming machine based on the value.

All effects are stored in the effect storage unit 41 as in the first embodiment. The effects include a pending effect, a pre-reach effect, a pre-notice effect during reach, and the like, and each effect belongs to one of these stages. In addition, the performance storage unit 41 also stores the flow of performance when the player actually plays the game machine as a performance flow. The effect flow is stored, for example, in the form of the effect flow table shown in Table 1 below. The effect flow table stores all the effects that appear when the player has played the game multiple times (for example, several thousand times).

(Table 1) Production flow table

The leftmost column of the effect flow table shown in Table 1 is the serial number assigned to each effect flow. In this example, it is shown whether the appearing effect appears at any stage of pending change, advance notice before reach, and notice during reach.

The effect of the hold change in the example of Table 1 is represented by color display and presence/absence of hold tone. In the example of Table 1, the effect items of the pre-reach announcement include lines, characters, background color, and cut-in. In the example of Table 1, the effect items of advance notice during reach include pseudo-relationship and reach development. Note that the effects shown in Table 1 are examples, and other effects can be added. As a result of the performance flow of each number, there are cases in which a win is achieved and cases in which a win is not achieved.

The appearance rate storage unit 42 shown in FIG. 4 stores the appearance rate of each effect or effect flow stored in the effect storage unit 41 . The appearance rate is obtained by dividing the number of appearances during a plurality of games (for example, several thousand times) by the number of games played.

The reliability storage unit 43 calculates and stores the probability of winning for each performance or performance flow when the performance or performance flow stored in the performance storage unit 41 appears. do. The reliability is obtained by dividing the number of hits by the total number of appearances of the effect or effect flow during a plurality of games.

The pattern storage unit 44 stores, for example, transition patterns of dopamine values shown in FIGS. 5(a) to 5(g) for each reliability. This transition pattern is based on neuroscience findings obtained from animal experiments.

A pattern P1 shown in FIG. 5(a) indicates the dopamine level when a win is won without advance notice, and the total amount of dopamine value that is considered to be secreted when the win is 1 is set. Here, "without advance notice" means that the win is achieved without any effect such as reach.

FIGS. 5(b) to 5(g) show transition patterns of the dopamine value as relative values based on the dopamine value 1 in FIG. 5(a). It shows the dopamine value due to each performance at the time of winning and the dopamine value at the time of winning through the performance. A plurality of effects in the models shown in FIGS. 5(b) to (g) may belong to any of pending change, advance notice before reach, and notice during reach.

Pattern P2 shown in FIG. 5(b) shows the transition of dopamine value in the case where there are two types of effects, and the reliability of winning from each effect is 25%. It is shown as a relative value as a reference. The dopamine value in pattern P2 is 0.1 for the first effect, 0.2 for the second effect, and 0.4 for winning. Therefore, the natural dopamine value is 0.1+0.2=0.3, and the dopamine value after winning is 0.1+0.2+0.4=0.7.

The pattern P3 shown in FIG. 5(c) has two types of effects, and the transition of the dopamine value when the reliability from each effect to winning is 50%. It is shown as a relative value as a reference. The dopamine value in pattern P3 is 2.0 for the first effect, 0.4 for the second effect, and 0.4 for winning. Therefore, the natural dopamine value is 2.0+0.4=2.4, and the dopamine value after the win is 2.0+0.4+0.4=2.8. Since the performance that constitutes the pattern P3 has a higher degree of reliability than the performance that constitutes the pattern P2, the dopamine level increases as the expectation of winning in the first performance increases, but after that, the dopamine value decreases. , the dopamine level is not so high even when it hits.

The pattern P4 shown in FIG. 5(d) has two types of effects, and the transition of the dopamine value when the reliability from each effect to winning is 75%. It is shown as a relative value as a reference. The dopamine value in pattern P4 is 2.5 for the first effect, 0.4 for the second effect, and 0.4 for winning. Therefore, the natural dopamine value is 2.5+0.4=2.9 and the dopamine value after the win is 2.5+0.4+0.4=3.3. Since the effects forming the pattern P4 have a higher degree of reliability than the effects forming the pattern P3, the expectation of winning in the first effect increases and the dopamine level increases, and after that, the dopamine level decreases. , the dopamine level is not so high even when it hits.

The pattern P5 shown in FIG. 5(e) has two types of effects, and the transition of the dopamine value when the reliability from each effect to winning is 100%. It is shown as a relative value as a reference. The dopamine value in the pattern P5 is 2.5 for the first effect, 0.2 for the second effect, and 0.1 for winning. Therefore, the natural dopamine value is 2.5+0.2=2.7 and the dopamine value after the win is 2.5+0.2+0.1=2.8. Since the reliability of the effects that make up the pattern P5 is 100%, it is known that the first effect of the notice during the reach is certain to be a win, and the dopamine level rises sharply, and after that, it is possible to win. Because you know, your dopamine levels drop sharply, and your dopamine levels drop when you hit.

Pattern P6 shown in FIG. 5(f) shows the transition of the dopamine value in the case of winning after two stages of the effect flow consisting of two types of effects, based on the dopamine value 1 in FIG. 5(a). It is shown as a relative value, the reliability of the two effects in the first stage is 25%, and the reliability of the two effects in the second stage is 50%. The dopamine value of each effect is 0.1 in the first effect of the first stage effect flow, 0.2 in the second effect, and 2.0 in the first effect of the second stage effect flow. , 0.4 in the second effect, and 0.4 in the win. Therefore, the dopamine value for granted is 0.1+0.2+2.0+0.4=2.7 and the dopamine value after the win is 2.7+0.4=3.1.

Pattern P7 shown in FIG. 5(g) shows the transition of the dopamine value when the hit is reached after the effect flow consisting of two types of effects continues for three stages, with the dopamine value 1 in FIG. 5(a) as the reference. It is shown as a relative value, the reliability of each of the two effects in the first stage and the second stage is 25%, and the reliability of the two effects in the third stage is 50%. The dopamine value of each effect is 0.1 in the first effect of the first stage effect flow, 0.2 in the second effect, and 0.1 in the first effect of the second stage effect flow. , 0.2 for the second effect, 2.0 for the first effect in the third stage effect flow, 0.4 for the second effect, and 0.4 for winning. Therefore, the dopamine value for granted is 0.1+0.2+0.1+0.2+2.0+0.4=3.0 and the dopamine value after the hit is 3.0+0.4=3.4.

In the example shown in FIG. 5, since the reliability is in increments of 25%, the dopamine value of the effect having the reliability in between is obtained using an interpolation method such as linear interpolation. Note that transition patterns other than the examples shown in FIGS. 5A to 5G may be stored in the pattern storage unit 44.

The search unit 45 reads reliability information about the effect or effect flow stored in the effect storage unit 41 from the reliability storage unit 43 . Then, the transition pattern corresponding to the reliability is read out from the pattern storage unit 44, and the dopamine value generated from the transition pattern is recognized.

The dopamine value calculation unit 46 adds the appearance rate of each effect to the dopamine value recognized by the search unit 45 to obtain the dopamine value of the effect. The dopamine value obtained here is a value that means how much dopamine the player secretes in the performance flow stored in the performance storage unit 41 . The determined dopamine values are stored, for example, in a dopamine value table shown in Table 2 below. This dopamine value table is obtained for each of pending change, pre-reach notice, and reach notice. If a win is achieved, "win" is displayed in the result column, and if a win is not achieved, the result column is blank.

(Table 2) Dopamine level table

The total dopamine amount calculation unit 47 calculates the sum of all the dopamine values calculated by the dopamine value calculation unit 46 for one model, and further multiplies the sum value by the expected number of balls for the model to be evaluated. Calculate the total dopamine amount for that model. The expected number of balls to be paid out is an expected value of the number of balls to be paid out at the time of winning.

The evaluation unit 48 evaluates the model to be evaluated based on the dopamine value calculated by the dopamine value calculation unit 46 or the total dopamine amount calculated by the total dopamine amount calculation unit 47 . The evaluation unit 48 can evaluate the gaming machine based on the individual dopamine values of each performance flow, or evaluate the gaming machine based on the total of all dopamine values calculated by the total dopamine amount calculation unit 47. can also

Below, the effect flow table shown in Table 1 is stored in the effect storage unit 41, and the case of obtaining the dopamine value for the effect stored in the effect flow table will be described.

First, the retrieval unit 45 sequentially reads the effect flow line by line from the line numbered 1 of the effect flow table. Then, the search unit 45 reads the appearance rate from the appearance rate storage unit 42 and reads the reliability from the reliability storage unit 43 for each effect present in each row. The appearance rate and reliability may be stored in the appearance rate storage unit 42 and the reliability storage unit 43 for each production flow. In this case, the appearance rate and reliability of the production flow are read.

The effect of the hold change includes display in various colors and sounding of a hold tone. The reliability information of the pending change “blue” is read out from the degree storage unit 43 . Here, when it is found that the effect of "blue" does not lead to a hit because the reliability of "blue" is 0, the search unit 45 sets the dopamine value of the pending change to 0 in this case. This value is stored in the dopamine value column of the dopamine value table of Table 2, number 1 pending change dopamine value.

Next, the search unit 45 reads out the appearance rate and reliability of the line "warning blue" that constitutes the pre-reach announcement in the row of number 1 from the appearance rate storage unit 42 and the reliability storage unit 43, respectively. Assuming that the reliability of the dialogue "warning blue" is 25%, the search unit 45 reads the transition pattern P2 in FIG. 5(b). Since the number 1 row does not reach the final hit, the natural dopamine value of 0.3 in this transition pattern P2 is read. On the other hand, assuming that the appearance rate of the line "warning blue" is 0.3, the dopamine value 1 is obtained by dividing the natural dopamine value of 0.3 in the transition pattern P2 by the appearance rate of 0.3.

In the row of number 1, since the background color "red" is produced as a pre-reach notice, the appearance rate and reliability are similarly read out from the appearance rate storage unit 42 and the reliability storage unit 43, respectively. . Assuming that the reliability of the background color “red” is 25%, the search unit 45 reads the transition pattern P2 in FIG. 5(b). The natural dopamine value for this transition pattern is 0.3, since we have not reached the final hit in row number 1. On the other hand, assuming that the appearance rate of the background color "red" is 0.5, the dopamine value of the background color "red" is 0.6.

When the dopamine values of the individual effects that constitute the pre-reach notice are obtained in this way, the dopamine value calculation unit 46 takes the maximum value of the dopamine values of the effects that constitute the pre-reach notice. Since the maximum value of the advance notice before reach in the row of number 1 is the dopamine value "1" of the line "warning blue", the dopamine value of the notice before reach is 1, and this value is the predetermined position of the dopamine value table in Table 2. stored in

Next, the search unit 45 stores the appearance rate and reliability of the effect flow composed of the pseudo-continuation "2" and the reach development "all rotation reach" that constitute the advance notice during the reach in the row of number 1, and the appearance rate storage unit 42 and the Each is read out from the reliability storage unit 43 . Assuming that the reliability of the flow from the pseudo run "2" to the reach development "all rotation reach" is 50%, the search unit 45 reads the transition pattern P3 of FIG. 5(c). The natural dopamine value for this transition pattern is 2.4, as row number 1 has not reached the final hit. On the other hand, if the occurrence rate of this flow is 0.2, the dopamine value of this flow is 12 by dividing the natural dopamine value of 2.4 in the transition pattern P3 by the occurrence rate of 2. This value is stored in the corresponding position of the dopamine value table in Table 2.

Next, for the effect flow of number 2, the dopamine values of pending change, advance notice before reach, and notice during reach are obtained in the same manner. In the row of number 2, since the win is finally reached, the dopamine value at the time of winning in the transition pattern of FIG. 5 is used to obtain the dopamine value of each effect or effect flow.

In the effect flow of the number 2 line, the effect of “on hold sound” is performed as the hold change, so the retrieval unit 45 reads the reliability information of the hold change “on hold sound” from the reliability storage unit 43 . Here, assuming that the reliability of "on-hold music" is 0.3, there is no transition pattern with a reliability of 0.3 in the pattern storage unit 44. Dopamine values per 0.7 per transition pattern with a confidence level of 0.25 and 2.8 per transition pattern with a confidence level of 0.5 are read out, respectively, and a linear interpolation method is used. Then, the dopamine value corresponding to the reliability of 0.3 is obtained using the following formula (1).
0.7+(2.8-0.7)×(0.3-0.25)/(0.5-0.25) (Formula 1)
This calculation yields a dopamine value of 1.12, corresponding to a confidence level of 0.3. Assuming that the appearance rate of the hold change “hold sound” is 0.5, the dopamine value of this effect is calculated as 2.24 by dividing 1.12 by 0.5. This value is stored in place in the dopamine value table of Table 2.

Next, the search unit 45 reads out the appearance rate and reliability of the line "green pattern" that constitutes the pre-reach announcement in the row of number 2 from the appearance rate storage unit 42 and the reliability storage unit 43, respectively. Assuming that the reliability of the dialogue "green pattern" is 25%, the search unit 45 reads out the dopamine value of 0.7 at the time of transition pattern P2 in FIG. 5(b). On the other hand, assuming that the appearance rate of the dialogue "green pattern" is 0.2, the dopamine value of 3.5 can be obtained by dividing 0.7 by 0.2.

In the row of number 1, since the cut-in "dialogue red" is produced as a pre-reach announcement, the appearance rate and reliability are similarly stored from the appearance rate storage unit 42 and the reliability storage unit 43, respectively. read out. Assuming that the reliability of the cut-in "serif red" is 50%, the search unit 45 reads out the dopamine value of 2.8 at the time of transition pattern P3 in FIG. 5(b). On the other hand, if the appearance rate of the cut-in "serif red" is 0.4, the dopamine value of the cut-in "serif red" is 7 by dividing 2.8 by the rate of 0.4.

When the dopamine values of the individual effects that constitute the pre-reach notice are obtained in this way, the dopamine value calculation unit 46 takes the maximum value of the dopamine values of the effects that constitute the pre-reach notice. The maximum value of the pre-reach notice in the row of number 2 is the dopamine value “7” of the cut-in “serif red”, so the dopamine value of the pre-reach notice is 7, and this value is the predetermined value in the dopamine value table in Table 2. stored in position.

Next, the search unit 45 stores the appearance rate and reliability of the effect flow composed of the pseudo-continuation "3" and the reach development "awakening" that constitute the advance notice during reach in the row of number 2, and the appearance rate storage unit 42 and the reliability. Each is read out from the storage unit 43 . Assuming that the reliability of the flow from the pseudo-continuation "3" to the reach development "awakening" is 75%, the search unit 45 reads out the dopamine value of 3.3 at the time of transition pattern P4 in FIG. 5(d). . Assuming that the appearance rate of this flow is 0.1, the dopamine value of this flow becomes 33 by dividing the dopamine value of 3.3 at the time of hit in the transition pattern P4 by the appearance rate of 0.1. This value is stored in the corresponding position of the dopamine value table in Table 2.

As described above, by obtaining the dopamine values of the pending change, pre-reach notice, and reach-in notice for all rows of the effect flow table shown in Table 1, the dopamine value in all positions of the dopamine value table of Table 2 is obtained. is stored, the total dopamine amount calculation unit 47 shown in FIG. 4 obtains the sum of the dopamine values of the hold change, the advance notice before reach, and the notice during reach. Then, by summing them up and multiplying the sum by the expected number of balls, the total dopamine amount of the model is obtained.

The evaluation unit 48 compares the total dopamine amount calculated by the total dopamine amount calculation unit 47 with the total dopamine amount obtained by the same method for other models, and evaluates the gaming machine based on the magnitude relationship. Since the total dopamine amount is an evaluation that comprehensively considers all effects, a game machine having an effect configuration with a large total dopamine amount is likely to lead to replays and a high operating rate.

In addition, the evaluation unit 48 evaluates the gaming machine based on individual dopamine values of each effect or each effect flow obtained by the dopamine value calculation unit 46 instead of the total dopamine amount calculated by the total dopamine amount calculation unit 47. can also For example, even if the dopamine value of each line is small, if the number of effects is large and the number of lines in the effect flow table of Table 1 is large, or if there are few effect flows with a low dopamine value, the player will not get bored. It can be determined that it is a model that does not allow

As described above, in the gaming machine evaluation devices 1 and 4 of the first embodiment and the second embodiment, appearance rate information, which is the probability that each effect appears, and reliability information, which is the probability of winning from each effect, are used. to evaluate the gaming machine. Therefore, the evaluation is not subjective as in the past, but is based on information obtained by objectively quantifying the actual behavior of the game machine. becomes.

In addition, how to change the configuration, appearance rate, and reliability of the presentation of the game machine to make the machine more attractive to players, and how to change the configuration, appearance rate, and reliability of the presentation will cause a difference in the operating rate. It is possible to specifically examine whether the

In the gaming machine evaluation device 4 of the second embodiment, the gaming machine is evaluated based on the dopamine value calculated by the dopamine value calculation unit 46, so evaluation using dopamine theory in neuroscience is possible. Also, the dopamine value of each transition pattern stored in the pattern storage unit 44 is represented by a relative value based on the dopamine value at the time of winning without prior notice. This means that the dopamine level when giving a reward after giving a reward notice is calculated in comparison with the dopamine value when giving a reward without giving a reward notice. Evaluation that is faithful to the theory becomes possible.

The gaming machine evaluation device 4 of the second embodiment includes a pattern storage unit 44 that stores transition patterns of dopamine values for each reliability, and a search unit 45 searches the pattern storage unit 44 for a transition pattern corresponding to reliability information. Then, since the dopamine value of the effect is obtained based on the dopamine value of the searched transition pattern, the obtained dopamine value corresponds to the degree of reliability. Therefore, it is possible to make an evaluation corresponding to the characteristics of the gaming machine in which the quality of the result is determined by the probability (reliability) rather than the skill of the player.

In the gaming machine evaluation device 4 of the second embodiment, the dopamine value calculation unit 46 calculates the dopamine value at each stage of hold change, advance notice before reach, and notice during reach. You can adjust the composition of the production to change it to an attractive model.

The appearance rate and reliability information used in the first and second embodiments may be provided by game machine manufacturers or may be collected through actual test games.

Further, in the gaming machine evaluation device 1 of the first embodiment, the effect distribution comparison chart 3 is generated for each individual effect, but the distribution comparison chart may be generated for each effect flow consisting of a combination of a plurality of effects. .

Furthermore, although the transition patterns shown in FIG. 5 are based on animal experiments, each transition pattern may be determined by a method other than animal experiments as long as it is based on medical knowledge.

3. Third Embodiment A gaming machine evaluation device 5 shown in FIG. 6 has the same hardware configuration as the gaming machine evaluation device 1 shown in FIG. When the application program is executed by the CPU 11, the function of each part of the gaming machine evaluation device 5 shown in FIG. 6 is realized.

The gaming machine evaluation device 5 shown in FIG. 6 is a device that evaluates the gaming machine by loading a program that operates on the actual gaming machine into the RAM 13 shown in FIG. 1 and executing it in the CPU 11 . As actual gaming machines, there are pachinko machines, pachislot machines, and the like. In the following, pachinko machines are assumed as actual gaming machines.

The gaming machine evaluation device 5 absorbs the difference in the hardware environment from the actual gaming machine, and performs a simulation for enabling the gaming machine evaluation device 5 to implement functions equivalent to those realized on the actual gaming machine. a main processing unit 52 that performs the same processing as the main board provided in the actual gaming machine, and a sub-processing unit that performs the same processing as the sub-board provided in the actual gaming machine and selects an effect. 53 , an effect analysis unit 54 that analyzes the effect selected by the sub-processing unit 53 , and an evaluation unit 55 that evaluates the gaming machine based on the analysis by the effect analysis unit 54 .

The simulation unit 51 includes a conversion unit 511 that converts the binary code of the program to be executed on the gaming machine to be evaluated into a binary code that can be executed by the gaming machine evaluation device 5, and a pachinko ball on the start chucker of the actual pachinko machine. A prize winning signal generating section 512 for generating a winning presence/absence signal indicating whether or not a prize has been won, and a synchronization control section 513 having a timer for synchronizing the main processing section 52 and the sub processing section 53 are provided.

The main processing unit 52 receives the notification from the prize winning signal generation unit 512, performs a lottery, and determines whether the prize is a win or a loss. This lottery result is notified to the sub-processing unit 53 as lottery result information.

The sub-processing unit 53 receives lottery result information from the main processing unit 52 and performs a lottery for the effect. The content of the performance selected by lottery is notified to the performance analysis unit 54 as performance information. The performance information is composed of, for example, a performance code of several bytes. The lottery result information notified from the main processing unit 52 is also notified to the effect analysis unit 54 together with the effect information.

The effect analysis unit 54 aggregates the effect information notified from the sub-processing unit 53 . For example, by dividing the number of times specific effect information is notified (the number of appearances) by the total number of notifications, the appearance rate is obtained for each effect. Also, the ratio of the lottery result information being "win" out of the number of times each effect information is notified is calculated, and the result is obtained as the reliability. Information on the appearance rate and reliability obtained in this way is recorded, for example, in the recording unit 14 shown in FIG. 1 as analysis table information compiled for each effect information.

The evaluation unit 55 evaluates the gaming machine by calculating a specific index based on the analysis result of the performance analysis unit 54, such as the analysis table information. As an evaluation method, there is the method shown in the first embodiment or the second embodiment. That is, for example, an effect distribution comparison diagram similar to the effect distribution comparison diagram 3 shown in FIG. 3 is created and analyzed, or the total dopamine amount is calculated using the dopamine transition example shown in FIG.

The game machine evaluation device 5 can be connected to a terminal 7 of a game machine maker via the Internet 6 . From the terminal 7 , for example, a binary code of a program to be executed in the gaming machine to be evaluated is sent to the gaming machine evaluation device 5 via the Internet 6 . The binary code transmitted from the terminal 7 is stored in the storage section 56 .

In the gaming machine evaluation device 5 configured as described above, when a certain gaming machine is to be evaluated, the program and the sub-board that operate on the main board of the gaming machine to be evaluated are operated from the manufacturer's terminal 7. A program for playing is transmitted to the gaming machine evaluation device 5 via the Internet 6 .

Then, in the gaming machine evaluation device 5, both received programs are loaded into the RAM 13 shown in FIG. 1 and executed. At this time, the main processing section 52 is configured by loading the program that operates on the main substrate into the RAM 13, and the sub processing section 53 is configured by loading the program that operates on the sub substrate into the RAM 13, respectively.

The conversion unit 511 of the simulation unit 51 shown in FIG. 6 converts the binary code of each program into binary code that can be executed by the CPU 11 shown in FIG. Then, the converted binary code is executed by the CPU 11 .

The synchronization control unit 513 calls the winning signal generation unit 512 . The winning signal generation unit 512 generates a winning signal indicating that the start checker has won or a winning signal indicating that the prize has not been won by random number processing, and notifies the main processing unit 52 of the signal. . The main processing unit 52 reads whether the winning signal is the winning signal or the non-winning signal.

Here, the main processing unit 52 and the sub processing unit 53 are called and executed by the synchronization control unit 513 at regular time intervals. whereas the processing on the sub-board is invoked and executed every 16 mSec or 32 mSec. Therefore, the sub-processing unit 53 is called once every four or eight times the main processing unit 52 is called.

When the signal generated by the winning signal generation unit 512 is a winning signal, the signal is notified to the main processing unit 52 . In response to this, the main processing unit 52 performs a lottery. On the other hand, if the signal generated by the prize winning signal generating section 512 is a non-winning signal, the signal is notified to the main processing section 52, but the main processing section 52 does not perform the lottery.

When the lottery is performed in the main processing unit 52, the result is notified to the sub processing unit 53 as lottery result information. In the sub-processing section 53, regardless of whether the result of the lottery in the main processing section 52 is a hit or a loss, a lottery for the effect is performed. The sub-processing unit 53 does not need to perform actual effects, but at least the effect code of the selected effect is notified to the effect analysis unit 54 together with the lottery result in the main processing unit 52 .

The above processing is performed one after another with timing controlled by the synchronization control unit 513 . Then, the effect analysis unit 54 stores the effect code and the lottery result information notified from the sub-processing unit 53 and tallies them. The effect belongs to, for example, one of pending change, advance notice before reach, and notice during reach, and is aggregated in the same manner as in Table 1 above. As a result, the reliability, appearance rate, etc. are calculated for each effect.

The evaluation unit 55 is notified of the result calculated by the effect analysis unit 54 . The evaluation unit 55 evaluates the gaming machine by creating an effect distribution comparison diagram as shown in FIG. 3 and calculating the dopamine value. This evaluation result is then sent to the maker side terminal 7 via the Internet 6 .

In this way, by causing the game machine evaluation device 5 to operate the program that actually runs in the game machine, the effect code and the lottery result in the main processing unit 52 are automatically output, so that the effect analysis unit 54 can also count. can be done automatically. Therefore, there is no need for a person to check the behavior of the gaming machine, and rapid evaluation is possible. In addition, since it is possible to operate the game machine faster than the actual game machine, quick evaluation is possible in this respect as well.

In this embodiment, the transfer of the program that operates on the main board and the program that operates on the sub-board to the game machine evaluation device 5 and the transfer of the evaluation result to the manufacturer-side terminal 7 are performed via the Internet 6. However, the exchange of programs and evaluation results can also be carried out through some medium.

Further, in the present embodiment, the binary code of the program to be executed in the gaming machine is transmitted to the gaming machine evaluation device 5 and converted by the conversion unit 511 for execution. 5, and the gaming machine evaluation device 5 compiles the source program and then executes it.

21, 41: Effect storage units 22, 42: Appearance rate storage units 23, 43: Reliability storage units 25, 48: Evaluation unit

Claims (5)

an effect storage unit that stores an effect flow that actually appears in the gaming machine, which is the flow of effects from the hold change to the advance notice before reach to the notice during reach;
an appearance rate storage unit that stores, as appearance rate information, the probability of appearance of each effect flow stored in the effect storage unit;
a reliability storage unit that stores, as reliability information, a probability that each effect flow stored in the effect storage unit appears and results in winning;
a dopamine value calculation unit that calculates the value of dopamine that is estimated to be secreted by the player through the effect flow;
A game machine evaluation device comprising: an evaluation unit that evaluates the game machine based on the dopamine value calculated by the dopamine value calculation unit based on the appearance rate information and the reliability information. a pattern storage unit that stores a dopamine value transition pattern for each reliability level of advance notice;
a search unit that searches the pattern storage unit for a transition pattern corresponding to the reliability information stored in the reliability storage unit;
The dopamine value calculation unit obtains the dopamine value of the effect stored in the effect storage unit based on the dopamine value of the corresponding transition pattern.
The gaming machine evaluation device according to claim 1 . The evaluation unit evaluates the gaming machine based on the total dopamine value calculated by the dopamine value calculation unit.
The gaming machine evaluation device according to claim 1 or 2 . The dopamine value of each transition pattern stored in the pattern storage unit is represented by a relative value based on the dopamine value at the time of winning without prior notice.
4. The gaming machine evaluation device according to claim 2 or 3 . the computer,
an effect storage unit that stores an effect flow that actually appears in the gaming machine, which is the flow of effects from the hold change to the advance notice before reach to the notice during reach;
an appearance rate storage unit that stores, as appearance rate information, the probability of appearance of each effect flow stored in the effect storage unit;
a reliability storage unit that stores, as reliability information, a probability that each effect flow stored in the effect storage unit appears and results in winning;
a dopamine value calculation unit that calculates the value of dopamine that is estimated to be secreted by the player through the effect flow;
A game machine evaluation program for functioning as an evaluation unit that evaluates a game machine based on the dopamine value calculated by the dopamine value calculation unit based on the appearance rate information and the reliability information.

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