Interactive task control method and device and storage medium

Abstract

The invention relates to the technical field of man-machine interaction, and provides an interactive task control method and device and a storage medium, an operation process of an interactive task comprises an execution process of a first sub-task and a second sub-task, the first sub-task can be controlled to be executed by operation of a user, and the second sub-task is controlled to be executed by an intelligent agent; the method comprises the steps of obtaining first subtask evaluation data of a first subtask in task execution time when an interactive task is in a running state; determining a preset deviation corresponding to the task execution time according to reference evaluation data corresponding to the task execution time and the first sub-task evaluation data; and according to the preset deviation, adjusting the preset interaction parameter of the intelligent agent so as to improve the difficulty adjustment accuracy of the interactive task.

Description

Technical Field

[0001] The present application relates to the field of human-computer interaction technology, and in particular to a control method, device and storage medium for an interactive task. Background Art

[0002] Currently, when users experience interactive tasks, such as games, different users have different levels of task operation. In order to improve the user's task interaction experience as much as possible, the difficulty of the interactive task can be adjusted to adapt to the interactive needs of different users for the interactive task.

[0003] For example, the difficulty of the interactive task can be adjusted by adjusting the interactive parameters of the agent set in the interactive task. In the related art, conditional configuration is generally used to trigger the adjustment of the interactive parameters of the agent. However, since in the process of adjusting the interactive parameters of the agent by conditional configuration, it is generally based on fixed conditions to judge whether to adjust the interactive parameters of the agent, it is easy to cause the adjusted interactive parameters of the agent to not match the actual task operation ability of the user, resulting in poor accuracy in adjusting the difficulty of the interactive task. Summary of the invention

[0004] The main purpose of the present application is to provide a control method, device and storage medium for an interactive task, aiming to improve the control accuracy of the interactive task.

[0005] In a first aspect, the present application provides a control method for an interactive task, wherein the operation process of the interactive task includes the execution process of a first subtask and a second subtask, wherein the first subtask can be executed by a user's operation control, and the second subtask is executed by an agent control; the control method comprises:

[0006] When the interactive task is in a running state, obtaining first subtask evaluation data of the first subtask within the task execution time;

[0007] Determining a preset deviation corresponding to the task execution time according to the reference evaluation data corresponding to the task execution time and the first subtask evaluation data;

[0008] According to the preset deviation, the preset interaction parameters of the agent are adjusted.

[0009] In a second aspect, the present application further provides a computer device, the computer device comprising a memory and a processor;

[0010] The memory is used to store computer programs;

[0011] The processor is used to execute the computer program and implement the steps of the control method of the interactive task as described above when executing the computer program.

[0012] In a third aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the control method of the interactive task as described above are implemented.

[0013] The present application provides a control method, apparatus, device and storage medium for an interactive task. The running process of the interactive task includes the execution process of a first subtask and a second subtask. The first subtask can be executed by user operation control, and the second subtask is executed by an intelligent agent. The control method includes: when the interactive task is in a running state, obtaining first subtask evaluation data of the first subtask within the task execution time; determining a preset deviation corresponding to the task execution time based on reference evaluation data corresponding to the task execution time and the first subtask evaluation data; and adjusting the preset interaction parameters of the intelligent agent based on the preset deviation.

[0014] For example, in the process of controlling the execution of the first subtask according to the user's operation, the user's operation may change. For example, the user's operation may be different at different task execution times. Accordingly, the first subtask evaluation data of the first subtask within the task execution time may also change. The first subtask evaluation data may be used to indicate the user's task operation level. Furthermore, since the reference evaluation data corresponds to the task execution time, the reference evaluation data may also change with the change of the task execution time. Accordingly, in the case where both the reference evaluation data and the first subtask evaluation data can change with the change of the task execution time, the preset deviation corresponding to different task execution times may also change. Based on this, in the case where the preset interaction parameters of the agent are adjusted according to the preset deviation, the preset interaction parameters of the agent can be adapted to the first subtask evaluation data within the task execution time. The preset interaction parameters of the agent can be used to determine the task interaction level of the agent. Accordingly, in the case where the preset interaction parameters of the agent are adapted to the first subtask evaluation data, the difficulty of the interactive task can be adapted to the user's task operation level, which is conducive to improving the accuracy of difficulty adjustment of the interactive task. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 It is a flowchart of a control method for an interactive task provided in an embodiment of the present application;

[0017] Figure 2 is a flowchart of a control method for an interactive task according to an embodiment of the present application;

[0018] Figure 3 is a schematic block diagram of a control device for an interactive task provided by an embodiment of the present application;

[0019] Figure 4 It is a schematic block diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0022] The embodiments of the present application provide a control method, device and storage medium for an interactive task. The control method for the interactive task can be applied to a computer device, which can be a mobile phone, a tablet computer, a laptop computer, a desktop computer and the like. The method can also be applied to a server, which can be a separate server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0023] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0024] See also Figure 1 , Figure 1It is a flow chart of a control method for an interactive task provided by an embodiment of the present application. It should be noted that the control method for an interactive task provided by an embodiment of the present application can be used in a computer device, and of course can also be used in a server. For example, the computer device can obtain the first subtask evaluation data of the first subtask within the task execution time from the server, and perform corresponding processing on the first subtask evaluation data according to the control method of the interactive task, and obtain a preset deviation between the first subtask evaluation data and the reference evaluation data corresponding to the task execution time. Exemplarily, the server can, for example, send the preset deviation to the computer device, so that the computer device adjusts the preset interaction parameters of the intelligent body according to the preset deviation; of course, it is not limited to this, and is not limited here.

[0025] In specific implementation, computer devices include but are not limited to: any one of mobile phones, tablet computers, laptop computers, and desktop computers; the server can be a single server or a server cluster, or a cloud server that provides cloud computing services.

[0026] like Figure 1 As shown, the control method of the interactive task includes steps S101 to S103.

[0027] Exemplarily, the running process of the interactive task includes the execution process of a first subtask and a second subtask, the first subtask can be executed by user operation control, and the second subtask is executed by an agent control. The agent is also called artificial intelligence (AI).

[0028] For example, the interactive task may include a game. Games include, for example, real-time battle games, role-playing games, interactive games, etc., which are not limited here. The game may be provided with user-controlled characters and non-user-controlled characters. The user may control the user-controlled character through corresponding operations to perform the first subtask. The agent may control the non-user-controlled character through corresponding preset interaction parameters to perform the second subtask.

[0029] Step S101: when an interactive task is in a running state, obtaining first subtask evaluation data of a first subtask within the task execution time.

[0030] For example, when an interactive task is in operation, as the task execution time passes, the user's operation may change with the operation of the interactive task or the task execution time, thereby causing the user's task operation level to change. Based on this, the computer device can obtain the first subtask evaluation data of the first subtask within the task execution time. The first subtask evaluation data can be used to indicate the user's task operation level. The first subtask evaluation data of the first subtask at different task execution times can be different, and the first subtask evaluation data can be associated with the task execution time, that is, the first subtask evaluation data can correspond to the task execution time.

[0031] In some implementations, the computer device may acquire the first subtask evaluation data in real time to acquire the first subtask evaluation data at different task execution times. Alternatively, the computer device may acquire the first subtask evaluation data at different task execution times at preset time intervals or random time intervals, and so on, which is not limited here.

[0032] The first subtask evaluation data may be determined based on at least one task evaluation index. Taking the interactive task including a real-time battle game as an example, the task evaluation index corresponding to the first subtask evaluation data may include the user's battle economic index, battle performance index, battle damage index, etc., which are not limited here. During different task execution times, the data corresponding to the user's battle economic index, battle performance index, and battle damage index are prone to change. Then, the first subtask evaluation data determined by the computer device according to the data corresponding to the user's battle economic index, battle performance index, and battle damage index during different task execution times will also change accordingly.

[0033] When obtaining the first subtask evaluation data within the task execution time, the computer device can set the user's task operation level indicated by the first subtask evaluation data as the basis for adjusting the difficulty of the interactive task, so as to subsequently improve the accuracy of the difficulty adjustment of the interactive task.

[0034] Step S102: determining a preset deviation corresponding to the task execution time according to the reference evaluation data corresponding to the task execution time and the first subtask evaluation data.

[0035] Exemplarily, when the interactive task is in operation, the computer device may be provided with reference evaluation data for the interactive task. The reference evaluation data may be used to indicate the target task operation level. The reference evaluation data may be different in different task execution times of the interactive task. For example, the reference evaluation data may be dynamically adjusted according to the task execution time, the first subtask evaluation data and other factors. Based on this, the reference evaluation data and the task execution time may have a corresponding relationship. Take the interactive task including a real-time battle game as an example. The computer device may obtain the second subtask evaluation data of the second subtask within the task execution time. The second subtask evaluation data may be used to indicate the task interaction level of the agent. In the real-time battle game, the non-role control role controlled by the agent in different task execution times may be different, or the same non-role control role controlled by the agent may cause different damage or obstacles to the user control role in different task execution times, then the second subtask evaluation data may also be different in different task execution times. In different task execution times, the user's task operation level needs to be higher than the task interaction level of the agent until it meets the target operation level, so as to achieve the clearance condition of the real-time battle game. Based on this, when both the first subtask evaluation data and the second subtask evaluation data change with the task execution time, the difficulty of the interactive task may change easily in different task execution times. Accordingly, the reference evaluation data may also change in response to the change in the difficulty of the interactive task in different task execution times, so that the reference evaluation data for different task execution times may be different, and the reference evaluation data may correspond to the task execution time.

[0036] In some embodiments, the computer device may combine the reference evaluation data corresponding to the task execution time and the first subtask evaluation data to determine the preset deviation corresponding to the task execution time. The preset deviation corresponding to the task execution time can be used to evaluate whether the user's task operation level within the task execution time is adapted to the target task operation level, and then serve as a basis for adjusting the difficulty of the interactive task.

[0037] For example, when the user's task operation level during the task execution time is determined to be adapted to the target task operation level based on a preset deviation, the difference between the user's task operation level and the agent's task interaction level is consistent with the target task operation level, and the computer device can determine that there is no need to adjust the difficulty of the interactive task.

[0038] For example, when the user's task operation level fails to adapt to the target task operation level within the task execution time determined according to the preset deviation, the difference between the user's task operation level and the agent's task interaction level is lower than the target task operation level or higher than the target task operation level, which may easily lead to a poor user experience in the interactive task. The computer device needs to adjust the difficulty of the interactive task to improve the user's experience in the interactive task.

[0039] Based on this, the computer device can determine the preset deviation corresponding to the task execution time according to the reference evaluation data corresponding to the task execution time and the first subtask evaluation data, so as to evaluate whether the user's task operation level is suitable for the target task operation level, thereby adjusting the difficulty of the interactive task.

[0040] In some embodiments, a task evaluation difference between the first subtask evaluation data and the second subtask evaluation data within the task execution time is determined; and a preset deviation corresponding to the task execution time is obtained by subtracting the task evaluation difference from the reference evaluation data.

[0041] For example, the first subtask evaluation data can be expressed as G self .

[0042] The task execution time can be expressed as t.

[0043] The second subtask evaluation data can be expressed as G AI (t). The second subtask evaluation data may be determined according to the preset interaction parameters of the agent. The second subtask evaluation data may be used to indicate the task interaction level of the agent within the task execution time. The preset interaction parameters of the agent within different task execution times may be different, and the second subtask evaluation data within different task execution times may also be different. The second subtask evaluation data may be associated with the task execution time, that is, the second subtask evaluation data may correspond to the task execution time. In the case where the first subtask evaluation data may be determined according to at least one task evaluation indicator, the second subtask evaluation data may be determined according to the agent evaluation indicator corresponding to the at least one task evaluation indicator. For example, the second subtask evaluation data may be determined according to the statistical value of the agent evaluation indicator corresponding to the at least one task evaluation indicator. Accordingly, when the computer device acquires the second subtask evaluation data within different task execution times, it may adopt different statistical calibers to determine the second subtask evaluation data, which is not limited here.

[0044] When the computer device obtains the first subtask evaluation data and the second subtask evaluation data, it can determine the task evaluation difference between the first subtask evaluation data and the second subtask evaluation data. When both the first subtask evaluation data and the second subtask evaluation data correspond to the task execution time, the task evaluation difference also corresponds to the task execution time. The task evaluation difference can be used to evaluate the difference between the user's task operation level and the agent's task interaction level during the task execution time, so as to subsequently evaluate whether the user's task operation level is adapted to the target task operation level. The task evaluation difference can be expressed as G now Since the task evaluation difference can be different in different task execution times and can be associated with the task execution time, the task evaluation difference can also be expressed as G now (t).

[0045] The calculation expression of the task evaluation difference can be expressed as:

[0046] G now (t) = G self -G AI (t)

[0047] When determining the task evaluation difference, the computer device can obtain the preset deviation by subtracting the task evaluation difference from the reference evaluation data to evaluate whether the difference between the user's task operation level indicated by the task evaluation difference and the agent's task interaction level is suitable for the target task operation level indicated by the reference evaluation data. The preset deviation can be expressed as E t .

[0048] The reference evaluation data can be expressed as G target The reference evaluation data may be different in different task execution times, and the reference evaluation data may be associated with the task execution time. Based on this, the reference evaluation data may also be expressed as G(t).

[0049] The calculation expression of the preset deviation can be expressed as:

[0050] E t =G target -G now =G(t)-G now (t) = G(t) - [G self -G AI (t)]

[0051] The computer device can determine the preset deviation in combination with the task execution time corresponding to the interactive task. The preset deviation can be used by the computer device to subsequently adjust the difficulty of the interactive task. In the case where the first subtask evaluation data and the reference evaluation data both correspond to the task execution time, the preset deviation will also correspond to the task execution time accordingly. Based on this, the computer device can adjust the difficulty of the interactive task according to the preset deviations corresponding to the different task execution times during different task execution times, and the difficulty of the interactive task can be adjusted in a more delicate manner, making the change in the difficulty of the interactive task smoother and more natural, thereby making it less likely for the user to notice the adjustment of the difficulty of the interactive task, which is conducive to the subsequent improvement of the difficulty adjustment effect of the interactive task.

[0052] Step S103: adjusting the preset interaction parameters of the agent according to the preset deviation.

[0053] Exemplarily, during the task execution time, the agent can execute the second subtask according to the preset interaction parameters to obtain the second subtask evaluation data corresponding to the second subtask at the task execution time. Since the second subtask evaluation data corresponding to the task execution time can be used in combination with the first subtask evaluation data corresponding to the task execution time to evaluate the difference between the user's task operation level and the agent's task interaction level, so as to provide for subsequent evaluation of whether the first subtask evaluation data corresponding to the task execution time is adapted to the reference evaluation data corresponding to the task execution time, the second subtask evaluation data can be associated with the difficulty of the interactive task. Accordingly, when the difficulty of the interactive task needs to be adjusted, the second subtask evaluation data corresponding to each of the subsequent task execution times can be adjusted by adjusting the preset interaction parameters of the agent.

[0054] In some embodiments, when the preset deviation is determined, the computer device can adjust the preset interaction parameters of the agent according to the preset deviation. The computer device can control the operation of the agent in the interactive task according to the preset interaction parameters of the agent, such as controlling the non-user-controlled character to perform the second subtask, thereby determining the second subtask evaluation data of the second subtask. Accordingly, in response to the adjustment of the preset interaction parameters, the second subtask evaluation data of the second subtask in the subsequent task execution time involved in the interactive task can be adjusted, thereby adjusting the difficulty of the interactive task to reduce the difference between the task operation level of the user and the task operation level of the agent, so that the task operation level of the user can be adapted to the target task operation level indicated by the reference evaluation data. Accordingly, in different task execution times, the preset deviations corresponding to different task execution times can be different, and the computer device can adjust the preset interaction parameters of the agent in different task execution times in response to the preset deviations corresponding to different task execution times to adjust the difficulty of the interactive task in different task execution times.

[0055] In this way, by adjusting the preset interaction parameters of the agent according to the preset deviation, the computer device can adjust the preset interaction parameters of the agent according to the preset deviations corresponding to the different task execution times during different task execution times, thereby adjusting the difficulty of the interactive task during different task execution times. Based on this, the computer device can adjust the difficulty of the interactive task in a more delicate way, making the change of the difficulty of the interactive task smoother and more natural, thereby making it less likely for the user to notice the adjustment of the difficulty of the interactive task, which is conducive to the subsequent improvement of the difficulty adjustment effect of the interactive task.

[0056] In some implementations, parameter adjustment data corresponding to the task execution time is determined based on a preset deviation; and preset interaction parameters of the agent are adjusted based on the parameter adjustment data.

[0057] For example, in the process of adjusting the preset interaction parameters of the agent, the computer device can determine the parameter adjustment data corresponding to the task execution time according to the preset deviation. The parameter adjustment data can be used as a basis for adjusting the preset interaction parameters of the agent.

[0058] The computer device can use a proportional-integral-derivative (PID) controller to determine the parameter adjustment data corresponding to the task execution time, so as to adjust the preset interaction parameters of the intelligent agent and dynamically adjust the difficulty of the interactive task.

[0059] Exemplarily, the parameter adjustment data includes first adjustment data. The first adjustment data is also called proportional control data, and the first adjustment data can be expressed as P out .

[0060] In some implementations, the first adjustment data corresponding to the task execution time is determined according to the product of the preset deviation and the first gain parameter.

[0061] The first adjustment data P out The calculation formula can be expressed as:

[0062] P out =K p ×E t

[0063] Among them, K p It is used to indicate the first gain parameter, which is also called the proportional gain coefficient, E t Used to indicate a preset deviation.

[0064] In an exemplary embodiment, in order to improve the difficulty adjustment stability, difficulty adjustment accuracy and difficulty balance of the interactive task, the computer device may monitor and adjust the setting of the first gain parameter. For example, the computer device may be provided with a first preset parameter range, and the first preset parameter range includes at least one first preset parameter. The computer device may select the first gain parameter from at least one first preset parameter within the first preset parameter range. The first preset parameter range may be pre-set or may be set by the user, which is not limited here.

[0065] Since the preset deviation is determined based on the reference evaluation data corresponding to the first subtask evaluation data and the task execution time, the first subtask evaluation data can be used to indicate the user's task operation level, and the reference evaluation data can be used to indicate the target task operation level, then the preset deviation can be used to evaluate whether the user's task operation level is adapted to the target task operation level. For example, the preset deviation can be used to indicate the difference between the user's task operation level and the agent's task interaction level, whether it is consistent with the target task operation level. Among them, the first subtask evaluation data can be determined based on different task evaluation indicators, and the reference evaluation data can be determined based on the reference evaluation indicators corresponding to different task evaluation indicators, then the preset deviation can also evaluate whether the task evaluation indicator is adapted to the corresponding reference evaluation indicator. In different task execution times, the task evaluation indicators required to determine the preset deviation and the reference evaluation indicators corresponding to the task evaluation indicators can be different, which is not limited here. Accordingly, in the case where the preset deviation corresponds to the task execution time, the first adjustment data can also correspond to the task execution time.

[0066] In some implementations, preset interaction parameters of the agent are adjusted according to the first adjustment data.

[0067] When the first adjustment data corresponding to the task execution time is determined based on the preset deviation combined with the first gain parameter, the computer device can adjust the preset interaction parameters of the agent based on the first adjustment data.

[0068] In the process of adjusting the preset interaction parameters of the agent through the first adjustment data, the first adjustment data can be used to reduce the task evaluation difference between the first subtask evaluation data and the second subtask evaluation data, thereby reducing the difference between the user's task operation level and the agent's task interaction level, so as to reduce the preset deviation corresponding to the subsequent task interaction time involved in the interactive task in the running state. Accordingly, in the case where the computer device reduces the preset deviation corresponding to the subsequent task interaction time involved in the interactive task in the running state, the user's task operation level indicated by the first subtask evaluation data during the subsequent task interaction time can be adapted to the target task operation level indicated by the reference evaluation data corresponding to the subsequent task execution time, that is, the preset interaction parameters of the agent can be adapted to the first subtask evaluation data, which is conducive to improving the accuracy of the difficulty adjustment of the interactive task.

[0069] Exemplarily, the parameter adjustment data includes second adjustment data. The second adjustment data is also called integral control data, and the second adjustment data can be expressed as I out .

[0070] In some embodiments, a preset deviation cumulative value within a preset time period corresponding to the task execution time is determined based on the task execution time, the preset deviation, and the deviation weight coefficient corresponding to the task execution time; and the second adjustment data corresponding to the task execution time is determined based on the product between the preset deviation cumulative value and the second gain parameter.

[0071] Since the preset deviations corresponding to different task execution times may be different, different preset deviations may have different deviation weight coefficients. The deviation weight coefficient may be used to indicate the importance of the preset deviation corresponding to the task execution time when calculating the preset deviation cumulative value. For example, the deviation weight coefficient may be in direct proportion to the size of the preset deviation, that is, the larger the preset deviation corresponding to the task execution time, the larger the deviation weight coefficient corresponding to the preset deviation. Of course, it is not limited to this, and the deviation weight coefficient may be pre-set or set by the user, which is not limited here. Based on this, in the case where the preset deviation corresponds to the task execution time, the computer device may also set the deviation weight coefficient to correspond to the task execution time. For example, the deviation weight coefficient may be expressed as W(t).

[0072] The preset deviation cumulative value can be expressed as S(t), and the calculation formula of the preset deviation cumulative value can be expressed as:

[0073] S(t)=∫0 t W(τ)E τ dτ

[0074] Among them, t is used to indicate the task execution time, and 0-t is used to indicate the preset time period corresponding to the task execution time. It is used to indicate that the product between the deviation weight coefficient corresponding to each task execution time within the preset time period and the preset deviation is integrated to obtain the preset deviation cumulative value.

[0075] Second adjustment data I out The calculation formula can be expressed as:

[0076] I out =K i ×S(t)=K i ×∫0 t W(τ)E τ dτ

[0077] Among them, K i Used to indicate a second gain parameter, which is also called an integral gain parameter, for example.

[0078] In an exemplary embodiment, in order to improve the difficulty adjustment stability, difficulty adjustment accuracy and difficulty balance of the interactive task, the computer device can monitor and adjust the setting of the second gain parameter. For example, the computer device can be provided with a second preset parameter range, and the second preset parameter range includes at least one second preset parameter. The computer device can select the second gain parameter from at least one second preset parameter within the second preset parameter range. The second preset parameter range can be pre-set or set by the user, which is not limited here.

[0079] According to the second adjustment data, it can be determined that the second adjustment data is in direct proportion to the preset deviation cumulative value. The preset deviation cumulative values ​​in the preset time periods corresponding to different task execution times may be different, and the preset deviation cumulative value may correspond to the task execution time. Accordingly, in the case where the preset deviation cumulative value is used to determine the second adjustment data, the second adjustment data may also correspond to the task execution time.

[0080] In some implementations, preset interaction parameters of the agent are adjusted according to the sum of the first adjustment data and the second adjustment data corresponding to the task execution time.

[0081] When the second adjustment data corresponding to the task execution time is determined based on the preset deviation cumulative value combined with the second gain parameter, the computer device can adjust the preset interaction parameters of the intelligent agent by combining the first adjustment data and the second adjustment data corresponding to the task execution time.

[0082] In the process of adjusting the preset interaction parameters of the intelligent agent through the second adjustment data, the second adjustment data can be used to accumulate the preset deviations within the preset time period corresponding to the task execution time. Since the preset deviations can be used to evaluate whether the first subtask evaluation data corresponding to each task execution time within the preset time period is adapted to the target task operation level indicated by the reference evaluation data under the corresponding task execution time, the second adjustment data can make the first subtask evaluation data corresponding to the subsequent task execution time involved in the interactive task in the running state gradually approach the reference evaluation data corresponding to the subsequent task execution time, thereby eliminating the steady-state deviation, so that the task operation level of the user indicated by the first subtask evaluation data within the subsequent task interaction time is adapted to the target task operation level indicated by the reference evaluation data corresponding to the subsequent task execution time, that is, the preset interaction parameters of the intelligent agent can be adapted to the first subtask evaluation data, which is conducive to improving the accuracy of difficulty adjustment of the interactive task.

[0083] In some embodiments, the data involved in the interactive task is discrete data obtained by marking according to the time schedule, such as different task execution times. When the computer device determines the preset deviation cumulative value, the determination process of the preset deviation cumulative value can be discretized to reduce the amount and difficulty of calculating the preset deviation cumulative value, thereby facilitating the subsequent improvement of the efficiency of difficulty adjustment of the interactive task.

[0084] For example, the historical deviation cumulative value in the historical time period corresponding to the historical execution time closest to the task execution time is obtained; and the preset deviation cumulative value is determined according to the task execution time, the preset deviation, the historical execution time and the historical deviation cumulative value.

[0085] The calculation formula of the preset deviation cumulative value S(t) can be expressed as:

[0086]

[0087] Among them, t pre Used to indicate the historical execution time, S(t pre ) is used to indicate the accumulated value of historical deviation, t is used to indicate the task execution time, and E t Used to indicate the preset deviation corresponding to the task execution time.

[0088] like Figure 2As shown, each time the preset deviation corresponding to the task execution time is obtained, the preset deviation corresponding to the task execution time can be recorded. Accordingly, the computer device can calculate the preset deviation cumulative value according to the preset deviation.

[0089] For example, when the preset deviation obtained for the first time is deviation 1 corresponding to time 1, the computer device may determine the preset deviation cumulative value corresponding to time 1 as deviation 1 itself. The computer device may record deviation 1 and the preset deviation cumulative value corresponding to time 1 for use at the next task execution time.

[0090] like Figure 2 As shown, when the next task execution time, i.e., the latest task execution time, is obtained, the task execution time closest to the latest task execution time is the historical execution time. The computer device can determine the historical deviation accumulation value within the historical time period corresponding to the historical execution time according to the historical execution time to determine the preset deviation accumulation value.

[0091] For example, when the preset deviation obtained by the computer device next time is deviation 2 corresponding to time 2, and the historical execution time closest to time 2 is time 1, the computer device can determine that the historical deviation cumulative value in the historical time period corresponding to the historical execution time closest to time 2 is the preset deviation cumulative value corresponding to time 1. Based on this, the computer device can determine the preset deviation cumulative value corresponding to time 2 according to time 2, deviation 2 corresponding to time 2, time 1, and the preset deviation cumulative value corresponding to time 1. And so on. In this way, by obtaining the historical deviation cumulative value and determining the preset deviation cumulative value according to the task execution time, the preset deviation, the historical execution time, and the historical deviation cumulative value, the determination process of the preset deviation cumulative value can be discretized to reduce the amount of calculation and the difficulty of calculation of the preset deviation cumulative value, which is conducive to the subsequent improvement of the efficiency of difficulty adjustment of interactive tasks.

[0092] Exemplarily, the parameter adjustment data includes third adjustment data. The third adjustment data is also called differential control data, and the third adjustment data can be expressed as D out .

[0093] In some embodiments, the preset deviation is differentiated according to the task execution time to obtain the deviation change rate corresponding to the task execution time; the response speed is adjusted according to the difficulty corresponding to the deviation change rate to determine the third gain parameter corresponding to the task execution time; and the third adjustment data corresponding to the task execution time is determined based on the product of the deviation change rate and the third gain parameter.

[0094] The third adjustment data D out The calculation formula can be expressed as:

[0095]

[0096] Among them, K d (t) is used to indicate a third gain parameter, Used to indicate the rate of change of deviation. It is based on the task execution time t, the preset deviation E t Obtained by differential processing.

[0097] In an exemplary embodiment, in order to improve the difficulty adjustment stability, difficulty adjustment accuracy and difficulty balance of the interactive task, the computer device may monitor and adjust the setting of the third gain parameter. For example, the computer device may be provided with a third preset parameter range, and the third preset parameter range includes at least one third preset parameter. The computer device may select the third gain parameter from at least one third preset parameter within the third preset parameter range. The third preset parameter range may be pre-set or may be set by the user, which is not limited here.

[0098] Since the deviation change rate corresponding to the task execution time is obtained by differentiating the preset deviation according to the task execution time, the computer device can determine that the deviation change rate corresponds to the task execution time. The deviation change rates corresponding to different task execution times may be different. The deviation change rate can be used by the computer device to determine the change trend of the preset deviation.

[0099] For example, different deviation change rates may correspond to different difficulty adjustment response speeds. For example, when the change trend of the preset deviation indicated by the deviation change rate is large, the speed at which the difficulty of the interactive task changes is correspondingly fast, and the computer device can adjust the difficulty of the interactive task through a faster difficulty adjustment response speed. For another example, when the change trend of the preset deviation indicated by the deviation change rate is small, the speed at which the difficulty of the interactive task changes is correspondingly slow, and the computer device can adjust the difficulty of the interactive task through a slower difficulty adjustment response speed. Based on this, the computer device may be provided with a corresponding relationship between the deviation change rate and the difficulty adjustment response speed of the interactive task. Among them, different deviation change rates may correspond to the same difficulty adjustment response speed, and different deviation change rates may also correspond to different difficulty adjustment response speeds one by one, which is not limited here. In the case of determining the deviation change rate corresponding to the task execution time, the computer device may determine the difficulty adjustment response speed corresponding to the deviation change rate based on the corresponding relationship between the deviation change rate and the difficulty adjustment response speed of the interactive task.

[0100] When determining the difficulty adjustment response speed corresponding to the deviation change rate corresponding to the task execution time, the computer device may determine the third gain parameter corresponding to the task execution time according to the difficulty adjustment response speed corresponding to the deviation change rate.

[0101] For example, the application scenarios involved in the interactive task, such as the game scenes of the game, can change with the passage of the task execution time. Compared with the unchanging gain parameter, the third gain parameter that can be controlled with the task execution time can be more suitable for improving the difficulty adjustment response speed of the interactive task during different task execution times. Based on this, the computer device can determine the third gain parameter corresponding to the task execution time according to the difficulty adjustment response speed corresponding to the deviation change rate. For example, the computer device can be provided with a corresponding relationship between the difficulty adjustment response speed and the third gain parameter. Among them, different difficulty adjustment response speeds can correspond to the same third gain parameter, and different difficulty adjustment speeds can also correspond to different third gain parameters one by one, which is not limited here. In the case of determining the difficulty adjustment response speed corresponding to the deviation change rate, the computer device can determine the third gain parameter corresponding to the difficulty adjustment response speed based on the corresponding relationship between the difficulty adjustment response speed and the third gain parameter, and then determine the third gain parameter as the third gain parameter corresponding to the task execution time.

[0102] Accordingly, the computer device may determine the third adjustment data corresponding to the task execution time in combination with the deviation change rate and the third gain parameter corresponding to the task execution time.

[0103] In some embodiments, preset interaction parameters of the intelligent agent are adjusted based on the sum of at least one of the first adjustment data corresponding to the task execution time and the second adjustment data corresponding to the task execution time and the third adjustment data.

[0104] When the third adjustment data corresponding to the task execution time is determined based on the deviation change rate and the third gain parameter corresponding to the task execution time, the computer device can adjust the preset interaction parameters of the intelligent agent by combining the first adjustment data corresponding to the task execution time, at least one of the second adjustment data corresponding to the task execution time, and the third adjustment data.

[0105] In the process of adjusting the preset interaction parameters of the intelligent body through the third adjustment data, the third adjustment data can be used by the computer device to judge the changing trend of the preset deviation in advance, and then the computer device can adjust the preset interaction parameters of the intelligent body in response to the predicted result of the changing trend of the preset deviation. For example, the computer device can adjust the preset interaction parameters of the intelligent body according to the difficulty adjustment response speed adapted to the changing trend in response to the predicted result of the changing trend of the preset deviation. Based on this, the computer device can make the task operation level of the user indicated by the first subtask evaluation data during the subsequent task execution time adapt to the target task operation level indicated by the reference evaluation data corresponding to the subsequent task execution time, and then make the preset interaction parameters of the intelligent body adapt to the first subtask evaluation data, which is conducive to improving the difficulty adjustment response speed of the interactive task while improving the difficulty adjustment accuracy of the interactive task.

[0106] In some embodiments, when determining the deviation change rate, the computer device may also discretize the process of determining the deviation change rate to reduce the amount and difficulty of calculating the deviation change rate, thereby facilitating the subsequent improvement of the efficiency of difficulty adjustment for interactive tasks.

[0107] For example, the historical deviation corresponding to the historical execution time closest to the task execution time is obtained; and the deviation change rate is determined according to the task execution time, the preset deviation, the historical execution time and the historical deviation.

[0108] Deviation change rate The calculation formula can be expressed as:

[0109]

[0110] Among them, t pre Used to indicate historical execution time, is used to indicate historical deviation, t is used to indicate task execution time, and E t Used to indicate the preset deviation corresponding to the task execution time.

[0111] like Figure 2 As shown, each time the preset deviation corresponding to the task execution time is obtained, the preset deviation corresponding to the task execution time can be recorded. Accordingly, the computer device can calculate the deviation change rate corresponding to the task execution time based on the recorded task execution time and the preset deviation corresponding to the task execution time.

[0112] For example, when the task execution time is time 4, the preset deviation corresponding to the task execution time is deviation 4 corresponding to time 4, and the closest historical time to time 4 is time 3, the computer device can obtain time 3 and deviation 3 corresponding to time 3 from the recorded task execution time and the preset deviation corresponding to the task execution time according to time 3. Accordingly, the computer device can calculate the deviation change rate corresponding to time 4 according to time 3, time 4, deviation 3, and deviation 4. In an exemplary embodiment, the deviation change rate corresponding to time 4 can be obtained by dividing the difference between deviation 4 and deviation 3 by the difference between time 4 and time 3.

[0113] In this way, by obtaining the historical deviation corresponding to the historical execution time closest to the task execution time; determining the deviation change rate based on the task execution time, preset deviation, historical execution time and historical deviation, the deviation change rate determination process can be discretized and improved to reduce the amount and difficulty of calculating the deviation change rate, which is beneficial to the subsequent improvement of the efficiency of difficulty adjustment of interactive tasks.

[0114] In an exemplary embodiment, the computer device can integrate all parameter adjustment data to adjust the preset interaction parameters of the agent.

[0115] For example, the computer device can adjust the preset interaction parameters of the agent according to the sum of the first adjustment data, the second adjustment data and the third adjustment data. The sum of the first adjustment data, the second adjustment data and the third adjustment data can be represented as the target adjustment data diff out . Target adjustment data diff out It can be expressed as:

[0116]

[0117] By determining the first adjustment data, the second adjustment data and the third adjustment data corresponding to different task execution times, the target adjustment data diff can be adjusted in response to the change of the task execution time. out Adjust accordingly. out In the process of adjusting the preset interaction parameters of the intelligent agent, the task operation level of the user indicated by the first subtask evaluation data during the subsequent task interaction time involved in the interactive task in the running state can be adapted to the target task operation level indicated by the reference evaluation data corresponding to the subsequent task interaction time, thereby making the preset interaction parameters of the intelligent agent adapt to the first subtask evaluation data, that is, the task difficulty of the interactive task gradually adapts to the task operation level of the user, which is conducive to improving the accuracy of the difficulty adjustment of the interactive task.

[0118] like Figure 2 As shown, the computer device can obtain the first subtask evaluation data during the operation of the interactive task, and calculate the first adjustment data, the second adjustment data, and the third adjustment data respectively. For example, the computer device can determine the first adjustment data, the second adjustment data, and the third adjustment data in combination with the preset deviation between the first subtask evaluation data and the reference evaluation data corresponding to the task execution time. Accordingly, the computer device can record the preset deviation cumulative value and the preset deviation corresponding to the task execution time for use at the next task execution time. The computer device can adjust the preset interaction parameters of the agent based on the first adjustment data, the second adjustment data, and the third adjustment data to adjust the second subtask evaluation data of the second subtask within the task execution time, thereby adjusting the difficulty of the interactive task.

[0119] The control method of the interactive task provided by the above embodiment comprises the following steps: the running process of the interactive task includes the execution process of the first subtask and the second subtask, the first subtask can be executed by the user's operation control, and the second subtask is controlled and executed by the agent; when the interactive task is in the running state, the first subtask evaluation data of the first subtask within the task execution time is obtained; according to the reference evaluation data corresponding to the task execution time and the first subtask evaluation data, the preset deviation corresponding to the task execution time is determined; according to the preset deviation, the preset interaction parameters of the agent are adjusted.

[0120] For example, in the process of controlling the execution of the first subtask according to the user's operation, the user's operation may change. For example, the user's operation may be different at different task execution times. Accordingly, the first subtask evaluation data of the first subtask within the task execution time may also change. The first subtask evaluation data may be used to indicate the user's task operation level. Furthermore, since the reference evaluation data corresponds to the task execution time, the reference evaluation data may also change with the change of the task execution time. Accordingly, in the case where both the reference evaluation data and the first subtask evaluation data may change with the change of the task execution time, the preset deviation corresponding to different task execution times may also change. Based on this, in the case where the preset interaction parameters of the agent are adjusted according to the preset deviation, the preset interaction parameters of the agent may be adapted to the first subtask evaluation data within the task execution time. The preset interaction parameters of the agent may be used to determine the task interaction level of the agent. Accordingly, in the case where the preset interaction parameters of the agent are adapted to the first subtask evaluation data, the difficulty of the interactive task may be adapted to the user's task operation level, which is conducive to improving the accuracy of difficulty adjustment of the interactive task.

[0121] See also Figure 3 , Figure 31 is a schematic block diagram of a control device for an interactive task provided by an embodiment of the present application. The control device can be configured in a server or a computer device to execute the control method for the interactive task described above.

[0122] like Figure 3 As shown, the control device of the interactive task includes: a data acquisition module 110 , a deviation determination module 120 and a parameter adjustment module 130 .

[0123] The data acquisition module 110 is used to acquire the first subtask evaluation data of the first subtask within the task execution time when the interactive task is in the running state.

[0124] The deviation determination module 120 is used to determine a preset deviation corresponding to the task execution time according to the reference evaluation data corresponding to the task execution time and the first subtask evaluation data.

[0125] The parameter adjustment module 130 is used to adjust the preset interaction parameters of the agent according to the preset deviation.

[0126] It should be noted that technicians in the relevant field can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described devices and modules and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0127] The method of the present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0128] Exemplarily, the above method and apparatus may be implemented in the form of a computer program, which may be run on a computer device.

[0129] See also Figure 4 , Figure 41 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device may be a server or an electronic device.

[0130] like Figure 4 As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus, wherein the memory may include a storage medium and an internal memory.

[0131] The storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any control method of an interactive task.

[0132] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0133] The internal memory provides an environment for the operation of the computer program in the storage medium. When the computer program is executed by the processor, the processor can execute any control method of an interactive task.

[0134] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0135] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0136] In one embodiment, the processor is used to execute a computer program and implement the following steps when executing the computer program:

[0137] When the interactive task is in a running state, obtaining first subtask evaluation data of the first subtask within the task execution time;

[0138] Determining a preset deviation corresponding to the task execution time according to the reference evaluation data corresponding to the task execution time and the first subtask evaluation data;

[0139] According to the preset deviation, the preset interaction parameters of the agent are adjusted.

[0140] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the control of the interactive task described above can refer to the corresponding process in the aforementioned interactive task control method embodiment, and will not be repeated here.

[0141] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. The method implemented when the computer program is executed by a processor can refer to the various embodiments of the control method of the interactive task of the present application.

[0142] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc., equipped on the computer device.

[0143] It should be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0144] It should also be understood that the term "and / or" used in the specification of this application and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations. It should be noted that, in this article, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "including a..." does not exclude the presence of other identical elements in the process, method, article or system including the element.

[0145] It should also be understood that the non-Company software tools or components appearing in the embodiments of the present application are merely examples and do not represent actual use.

[0146] The serial numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments. The above description is only a specific implementation mode of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A control method for an interactive task, characterized in that: The running process of the interactive task includes the execution process of a first subtask and a second subtask, wherein the first subtask can be executed by the user's operation control, and the second subtask is executed by the agent control; The control method comprises: When the interactive task is in a running state, obtaining first subtask evaluation data of the first subtask within the task execution time; Determining a preset deviation corresponding to the task execution time according to the reference evaluation data corresponding to the task execution time and the first subtask evaluation data; According to the preset deviation, the preset interaction parameters of the agent are adjusted.

2. The control method according to claim 1, characterized in that: The determining, based on the reference evaluation data corresponding to the task execution time and the first subtask evaluation data, the preset deviation corresponding to the task execution time includes: determining a task evaluation difference between the first subtask evaluation data and the second subtask evaluation data of the second subtask within the task execution time; The preset deviation corresponding to the task execution time is obtained by subtracting the task evaluation difference from the reference evaluation data.

3. The control method according to claim 1 or 2, characterized in that: The step of adjusting the preset interaction parameters of the agent according to the preset deviation includes: Determining parameter adjustment data corresponding to the task execution time according to the preset deviation; The preset interaction parameters of the agent are adjusted according to the parameter adjustment data.

4. The control method according to claim 3, characterized in that: The parameter adjustment data includes first adjustment data; The step of determining parameter adjustment data corresponding to the task execution time according to the preset deviation includes: Determining first adjustment data corresponding to the task execution time according to the product of the preset deviation and the first gain parameter; The step of adjusting the preset interaction parameters of the agent according to the parameter adjustment data includes: According to the first adjustment data, preset interaction parameters of the agent are adjusted.

5. The control method according to claim 3, characterized in that: The parameter adjustment data includes second adjustment data; The step of determining parameter adjustment data corresponding to the task execution time according to the preset deviation includes: Determine a preset deviation cumulative value within a preset time period corresponding to the task execution time according to the task execution time, the preset deviation and a deviation weight coefficient corresponding to the task execution time; Determining second adjustment data corresponding to the task execution time according to the product between the preset deviation cumulative value and the second gain parameter; The step of adjusting the preset interaction parameters of the agent according to the parameter adjustment data includes: The preset interaction parameters of the agent are adjusted according to the sum of the first adjustment data corresponding to the task execution time and the second adjustment data.

6. The control method according to claim 5, characterized in that: The step of determining a preset deviation cumulative value within a preset time period corresponding to the task execution time according to the task execution time, the preset deviation, and a deviation weight coefficient corresponding to the task execution time includes: Obtaining a historical deviation cumulative value within a historical time period corresponding to a historical execution time closest to the task execution time; The preset deviation cumulative value is determined according to the task execution time, the preset deviation, the historical execution time and the historical deviation cumulative value.

7. The control method according to claim 3, characterized in that: The parameter adjustment data includes third adjustment data; The step of determining parameter adjustment data corresponding to the task execution time according to the preset deviation includes: According to the task execution time, the preset deviation is differentiated to obtain the deviation change rate corresponding to the task execution time; According to the difficulty adjustment response speed corresponding to the deviation change rate, a third gain parameter corresponding to the task execution time is determined; Determining third adjustment data corresponding to the task execution time according to the product of the deviation change rate and the third gain parameter; The step of adjusting the preset interaction parameters of the agent according to the parameter adjustment data includes: The preset interaction parameters of the agent are adjusted according to the sum of at least one of the first adjustment data corresponding to the task execution time, the second adjustment data corresponding to the task execution time, and the third adjustment data.

8. The control method according to claim 7, characterized in that: The step of performing differentiation processing on the preset deviation according to the task execution time to obtain the deviation change rate corresponding to the task execution time includes: Obtaining a historical deviation corresponding to a historical execution time closest to the task execution time; The deviation change rate is determined according to the task execution time, the preset deviation, the historical execution time, and the historical deviation.

9. A computer device, characterized in that: The computer device includes a memory and a processor; The memory is used to store computer programs; The processor is configured to execute the computer program and implement the control method of the interactive task according to any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for controlling an interactive task according to any one of claims 1 to 8 are implemented.

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