Virtual community simulation method and device based on multiple agents

Abstract

The invention relates to a virtual community simulation method and device based on multiple agents. The method comprises the following steps: constructing an NAA model; the NAA model comprises agents representing virtual community members, products representing community tasks and schemes and specifications representing a virtual community incentive mechanism; constructing a virtual community model and an incentive mechanism model according to the NAA model; and performing virtual community simulation based on multiple agents according to the virtual community model and the incentive mechanism model. By adopting the method, virtual community simulation can be realized.

Description

Technical Field

[0001] The present application relates to the technical field of virtual community simulation, and in particular to a multi-agent-based virtual community simulation method and device. Background Art

[0002] With the development of internet technology, virtual communities have gradually become important venues for human interaction and communication. However, current virtual community simulations face a critical challenge: the lack of comprehensive and accurate abstract models of core elements such as community members, contributions, and incentive mechanisms. Existing models are often overly simplistic or one-sided, failing to fully and meticulously depict the diverse behaviors of virtual community members, the rich variety of contributions, and the complex impact of incentive mechanisms on community operations. This makes it difficult to accurately grasp the underlying workings of virtual communities, hindering in-depth research and effective simulations. Summary of the Invention

[0003] Based on this, it is necessary to provide a multi-agent based virtual community simulation method and device to address the above technical problems.

[0004] A multi-agent-based virtual community simulation method, the method comprising:

[0005] Constructing an NAA model; the NAA model includes agents representing virtual community members, artifacts representing community tasks and plans, and specifications representing virtual community incentive mechanisms;

[0006] Based on the NAA model, a virtual community model and an incentive mechanism model are constructed. The virtual community model includes a community member model, a contribution model, and agent behavior rules. The community member model is composed of the agents, which include agent attributes and agent behaviors for participating in community activities. The contribution model is composed of the artifacts and is used to enable interaction and collaboration among community members. The agent behavior rules are used to formulate rules for participating in community contributions, and the rules are related to the level of influence of the incentive mechanism. The incentive mechanism model includes context, trigger conditions, and reward and punishment actions. The reward and punishment actions are generated by the context and trigger conditions to represent the norms.

[0007] According to the virtual community model and incentive mechanism model, a multi-agent virtual community simulation is performed.

[0008] In one embodiment, the community member model includes the agent's incentive value, professional ability, contribution activity, evaluation activity, task proposal rate, and like rate.

[0009] In one embodiment, the contribution model includes: product type, creating agent, creation days, number of solutions, number of likes, number of dislikes and quality.

[0010] In one embodiment, the agent behavior rules include: task proposal rules, solution contribution rules, like rules, dislike rules, and update rules.

[0011] In one embodiment, the task-proposing rule is:

[0012]

[0013] Among them, AG represents the community member model, ag represents the members in the community member model, q represents the proposed task, Q represents the proposed task set, PQ(ag,pq) represents the level of influence of the specification on the current task-proposing behavior of the agent ag is pq, QR(ag,qr) represents the task-proposing rate of the agent ag is qr, and CR(ag,p) represents the creation of the product p by the agent ag.

[0014] In one embodiment, the contribution solution rule is:

[0015]

[0016] Among them, a represents the scheme of the contribution model, A represents the scheme set of the contribution model, PA(ag,pa) represents the level of influence of the specification on the current contribution solution behavior of agent ag is pa, QR(ag,qr) represents the task proposal rate of agent ag is qr, and CR(ag,p) represents the creation of artifact p by agent ag.

[0017] In one embodiment, the like rule is:

[0018]

[0019] Among them, p represents the product, ART represents the contribution model, PU(ag,pu) represents the influence level of the norm on the current like behavior of agent ag is pu, UR(ag,ur) represents the like rate of agent ag is ur, and UV(ag,p) represents the like of agent ag to product p.

[0020] In one embodiment, the click rule is:

[0021]

[0022] Where p represents the product, ART represents the contribution model, PD(ag,pd) represents the level of influence of the norm on the current dislike behavior of agent ag, which is pd, UR(ag,ur) represents the like rate of agent ag, which is ur, and DV(ag,p) represents whether agent ag dislikes or cancels the like of product p.

[0023] In one embodiment, the update rule is:

[0024]

[0025] Among them, REW(ag,rt,rew) indicates that the rt type incentive value of agent ag is rew, EXP(ag,exp) indicates that the professional ability of agent ag is exp, PAC(ag,pac) indicates that the contribution activity of agent ag is pac, VAC(ag,vac) indicates that the average daily evaluation number of agent ag is vac, QR(ag,qr) indicates that the task proposal rate of agent ag is qr, UR(ag,ur) indicates that the like rate of agent ag is ur, f i is a function that represents the relationship between the agent’s incentive value and its other attributes, f i (rew)=a i ×rew+b i , i∈{1, 2, 3, 4, 5}.

[0026] A multi-agent-based virtual community simulation device, comprising:

[0027] NAA model construction module, used to construct the NAA model; the NAA model includes intelligent agents representing virtual community members, artifacts representing community tasks and plans, and specifications representing virtual community incentive mechanisms;

[0028] A virtual community model and incentive mechanism model construction module is used to construct a virtual community model and an incentive mechanism model based on the NAA model. The virtual community model includes: a community member model, a contribution model, and agent behavior rules. The community member model is composed of the agents, which include agent attributes and agent behaviors for participating in community activities. The contribution model is composed of the artifacts and is used to enable interaction and collaboration among community members. The agent behavior rules are used to formulate rules for participating in community contributions, and the rules are related to the impact level of the incentive mechanism. The incentive mechanism model includes: context, trigger conditions, and reward and punishment actions. The reward and punishment actions are generated by the context and trigger conditions to represent the norms.

[0029] The virtual community simulation module is used to perform multi-agent virtual community simulation based on the virtual community model and incentive mechanism model.

[0030] The above-mentioned multi-agent-based virtual community simulation method and device first constructs the NAA model, including agents, community tasks and plans, and then constructs the virtual community model and incentive mechanism model based on this. The community model contains a member model, a contribution model and agent behavior rules, and the incentive mechanism model is composed of context, etc. Then, based on these models, multi-agent virtual community simulation is carried out, providing new ideas and methods for virtual community simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 1 is a flow chart of a multi-agent-based virtual community simulation method according to one embodiment;

[0032] Figure 2 Schematic diagram of an NAA model in one embodiment;

[0033] Figure 3 is a schematic diagram of an incentive mechanism model in another embodiment;

[0034] Figure 4 It is a structural block diagram of a multi-agent based virtual community simulation device in one embodiment. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0036] In one embodiment, Figure 1 As shown, a multi-agent-based virtual community simulation method is provided, comprising the following steps:

[0037] Step 102: construct a NAA model.

[0038] The NAA model includes agents representing virtual community members, artifacts representing community tasks and plans, and specifications representing the virtual community's incentive mechanism.

[0039] like Figure 2 As shown, the agent in AG makes behavioral decisions based on its own state and available rewards and punishments. x ,Through actions, we create artifacts in ART or modify its state.,NORM,rewards or punishes the agent of AG according to the state change of ART,,changes its properties, and thus affects its behavior.

[0040] Step 104: construct a virtual community model and an incentive mechanism model based on the NAA model.

[0041] The virtual community model includes: a community member model, a contribution model, and agent behavior rules; the community member model is composed of the agents, which include agent attributes and agent behaviors that participate in community activities; the contribution model is composed of artifacts and is used to achieve interaction and collaboration among community members; the agent behavior rules are used to formulate rules for participating in community contributions, and the rules are related to the level of influence of the incentive mechanism; the incentive mechanism model includes: context, trigger conditions, and reward and punishment actions, and the norms are expressed by generating reward and punishment actions through context and trigger conditions.

[0042] Step 106: Perform a multi-agent-based virtual community simulation according to the virtual community model and the incentive mechanism model.

[0043] In the above-mentioned multi-agent-based virtual community simulation method, the NAA model is first constructed, including agents, community tasks and contributions, and then the virtual community model and incentive mechanism model are constructed based on this. The community model contains the member model, contribution model and agent behavior rules, and the incentive mechanism model is composed of context, etc. Then, based on these models, the multi-agent virtual community simulation is carried out, providing new ideas and methods for virtual community simulation.

[0044] In one embodiment, the community member model includes the agent's incentive value, professional ability, contribution activity, evaluation activity, task proposal rate, and like rate.

[0045] Specifically, based on the NAA model, AG can represent a collection of agents that are members of a virtual community. Each agent (ag∈AG) has its own attributes and behaviors. This application focuses on the four types of behaviors that are most relevant to the core assets of the virtual community (tasks and solutions): proposing tasks, contributing solutions, evaluating solutions, and updating solutions. At the same time, it focuses on the internal states of members that affect these behaviors, including incentive values, activeness of contributions and evaluations, contribution preferences, and evaluation preferences. Therefore, ag is defined as a tuple ag=<rew,exp,pac,vac,qr,ur> .

[0046] 1) rew represents the incentive value of agent ag. It can be reputation, badges, or status level, etc.

[0047] 2) exp represents the professional ability of the agent ag. Its level is measured using a value between 0 and 1, that is, exp∈[0,1].

[0048] 3) PAC represents the contribution activity of agent ag. It is measured by its average daily contribution. PAC ≥ 0.

[0049] 4) vac represents the evaluation activity of agent ag. It is measured by its average daily evaluation number. vac ≥ 0.

[0050] 5) qr represents the agent's task proposal rate, reflecting its preference for task proposals. It is measured as the ratio of the agent's proposed tasks to its total contributions (including proposed tasks and solutions). qr∈[0,1]. Accordingly, the agent's solution proposal rate (task solution rate) is 1-qr. Furthermore, qr and 1-qr represent the overall strength of the incentive mechanism's influence on the agent's task proposal and task solution behaviors, respectively. In other words, in most cases, the incentive mechanism influences the agent's level of influence on these two types of behaviors.

[0051] 6) ur represents the agent's like (positive evaluation) rate, reflecting its preference for likes and dislikes. It is measured as the ratio of the agent's like votes to its total votes (including both likes and dislikes). ur∈[0,1]. Correspondingly, the agent's dislike (negative evaluation) rate is 1-ur. Furthermore, ur and 1-ur represent the overall strength of the incentive mechanism's influence on the agent's like and dislike behaviors, respectively. In other words, in most cases, the incentive mechanism influences the agent's level of influence on these two types of behaviors.

[0052] In one embodiment, the contribution model includes: product type, creating agent, creation days, number of solutions, number of likes, number of dislikes, and quality.

[0053] Specifically, community member contributions in virtual communities are community content in the form of text, images, audio, and video. This application categorizes this content into two categories: the first is the tasks to be solved, such as daily life problems and software requirements; the second is the solutions to these tasks. Contributions in virtual communities can only be created and modified by community members and serve as a medium for interaction and collaboration among community members. Therefore, this invention uses artifacts to represent community contributions.

[0054] According to the contribution category, the artifact set ART is the union of tasks Q and solutions A, that is, ART = Q∪A. For any artifact (task or solution) art∈ART, define art=<c,ag,t,na,nu,nd,qu> .

[0055] 1) c represents the type of artifact p. c∈{0,1}, where 0 and 1 represent tasks and solutions, respectively.

[0056] 2) ag represents the agent that creates artifact p.

[0057] 3) t represents the number of days since the creation of product p, i.e., the product age (age). On the day when product p is created, t=0.

[0058] 4) na represents the number of solutions for product p. If product p is a solution, na = -1.

[0059] 5) nu represents the number of likes for product p.

[0060] 6) nd represents the number of times product p has been stepped on.

[0061] 7) qu represents the quality of product p.

[0062] In one embodiment, the agent behavior rules include: task proposal rules, solution contribution rules, like rules, dislike rules, and update rules.

[0063] Specifically, the task rules are proposed as follows:

[0064]

[0065] Among them, AG represents the community member model, ag represents the members in the community member model, q represents the proposed task, Q represents the proposed task set, PQ(ag,pq) represents the level of influence of the specification on the current task-proposing behavior of the agent ag is pq, QR(ag,qr) represents the task-proposing rate of the agent ag is qr, and CR(ag,p) represents the creation of the product p by the agent ag.

[0066] In this embodiment, community members participate in the community by proposing tasks, seeking solutions to the tasks they are interested in. To describe the task proposals submitted by members, this application introduces pq to represent the level of influence of the incentive mechanism on the current task proposal behavior of agent ag. The time at which a member expects to receive community help is random. Therefore, this application uses a random number between 0 and 1 to represent the level of influence of the incentive mechanism on a member's task proposal behavior at a given moment, that is, pg∈[0,1]. When agent ag's pq is greater than qr, that is, the level of influence of the incentive mechanism on its current task proposal behavior exceeds its overall level, agent ag proposes a question.

[0067] In one embodiment, the contribution solution rule is:

[0068]

[0069] Among them, a represents the scheme of the contribution model, A represents the scheme set of the contribution model, PA(ag,pa) represents the level of influence of the specification on the current contribution solution behavior of agent ag is pa, QR(ag,qr) represents the task proposal rate of agent ag is qr, and CR(ag,p) represents the creation of artifact p by agent ag.

[0070] Specifically, this application uses pa to represent the level of influence of the incentive mechanism on the current solution contribution behavior of agent ag. When it is greater than its overall level (answer rate) 1-qr, agent ag contributes a solution. The level of influence of the incentive mechanism on a member's solution contribution behavior, pa, is related to the potential benefits of contributing solutions. The greater the benefits a member receives, the higher the level of influence of the incentive mechanism on their solution contribution behavior. The potential benefits of contributing solutions depend on both the rewards received and the costs incurred. In terms of benefits, the potential rewards for a member's solution contribution are first closely related to the quality of the solution. The higher the solution quality qu, the more likely it is to receive likes, thereby earning more reputation points or badge rewards. The generation of high-quality solutions is closely related to the member's expertise level exp and the individual effort e. Therefore, this application represents solution quality as qu = φ(exp, e). Secondly, the reward for contributing solutions is related to the number of existing solutions n for the task. The more solutions a task has, the less attention subsequent solutions receive, and the more difficult it is to earn rewards. Thirdly, the reward for a member's solution is related to the time t it takes to solve the task. Time t refers to the time from when the task is proposed to when the member contributes a solution to it. The larger t is, the lower the task is ranked on the content page, and the harder it is for the solution to the task to gain attention and rewards. Therefore, the member’s solution benefit is expressed as g A (n, φ(exp, e), t). The cost of a solution depends primarily on the number of solutions n for the task and the individual effort e expended by the member, denoted in this application as c(n, e). This is because contributing a solution to a task with existing solutions requires browsing previous solutions, resulting in a higher cost than for tasks with no solutions or few solutions. Furthermore, members prioritize solution quality, and the greater the individual effort e expended, the greater the cost. In summary, the degree to which a member's solution contribution behavior is influenced by the incentive mechanism can be expressed as:

[0071] p a =η(-c(n,e)+g A (n, φ(exp, e), t)).

[0072] η is a function that maps member benefits to the level affected by the incentive mechanism.

[0073] In one embodiment, the like rule is:

[0074]

[0075] Among them, p represents the product, ART represents the contribution model, PU(ag,pu) represents the influence level of the norm on the current like behavior of agent ag is pu, UR(ag,ur) represents the like rate of agent ag is ur, and UV(ag,p) represents the like of agent ag to product p.

[0076] Specifically, this application uses pu to represent the level at which the current like behavior of agent ag is affected by the incentive mechanism. When it is greater than the level (like rate) ur at which its overall like behavior is affected by the incentive mechanism, agent ag will like a product. Among them, the level pu at which a member's like behavior is affected by the incentive mechanism is related to the possible benefits of liking. The greater the benefits obtained by the member, the higher the level at which the like behavior is affected by the incentive mechanism. The possible benefits of liking depend on the reward R obtained. U (such as badges) and expenses paid C U Therefore, this application uses the following formula to express the degree to which the member's like behavior is affected by the incentive mechanism. It is a function that maps member benefits to the level at which the like behavior is affected by the incentive mechanism.

[0077]

[0078] In one embodiment, the click-and-thumb rules are:

[0079]

[0080] Where p represents the product, ART represents the contribution model, PD(ag,pd) represents the level of influence of the norm on the current dislike behavior of agent ag, which is pd, UR(ag,ur) represents the like rate of agent ag, which is ur, and DV(ag,p) represents whether agent ag dislikes or cancels the like of product p.

[0081] Specifically, this application uses pd to represent the level of influence of the incentive mechanism on the current downvoting behavior of the agent ag. When it is greater than the level of influence of the incentive mechanism on its overall downvoting behavior (downvoting rate) 1-ur, the agent ag will downvote a contribution. Similar to the level of influence of the incentive mechanism on the like behavior, the level of influence of the incentive mechanism on the member downvoting behavior pd is related to the possible benefits of downvoting. The possible benefits of downvoting depend on the reward R D and the expenses paid C D Therefore, the following formula is used to express the degree to which the member's click-and-dump behavior is affected by the incentive mechanism. It is a function that maps member benefits to the level at which the click-and-tick behavior is affected by the incentive mechanism.

[0082]

[0083] In one embodiment, the update rule is:

[0084]

[0085] Among them, REW(ag,rt,rew) indicates that the rt type incentive value of agent ag is rew, EXP(ag,exp) indicates that the professional ability of agent ag is exp, PAC(ag,pac) indicates that the contribution activity of agent ag is pac, VAC(ag,vac) indicates that the average daily evaluation number of agent ag is vac, QR(ag,qr) indicates that the task proposal rate of agent ag is qr, UR(ag,ur) indicates that the like rate of agent ag is ur, f i is a function that represents the relationship between the agent’s incentive value and its other attributes, f i (rew)=a i ×rew+b i , i∈{1, 2, 3, 4, 5}.

[0086] Specifically, the degree to which the contribution behavior of community members is affected by the incentive mechanism changes with the change of status (corresponding to its incentive value), thereby affecting the change of its behavior pattern. This application uses the agent's update rule to simulate the change of member behavior pattern. In addition, f i These are represented by simple functions that characterize the impact of incentive rewards on member attributes. For example, pac∈f1(rew) represents the relationship between a member's incentive value, rew, and their contribution activity, pac. As members continue to receive incentive rewards, their attributes change, influencing the evolution of their behavior patterns.

[0087] In one embodiment, Figure 3 As shown in Figure 1, the incentive mechanism model includes: context, trigger conditions, and reward and punishment actions. The context and trigger conditions are used to generate reward and punishment actions to express norms. Specifically:

[0088] 1) Context. The implementation context of the incentive mechanism refers to the community environment requirements for the incentive mechanism, that is, the community contributions for which the incentive mechanism is implemented. This application categorizes this into two types: single contributions and composite contributions. The former refers to a single community contribution or a single community contribution. For example, a task, solution, or vote in the community; the latter refers to the combination of tasks, solutions, and votes in the community. For example, the number of solutions for a task in the community, the total number of votes cast by a member, etc.

[0089] 2) Trigger conditions. The trigger conditions for implementing an incentive mechanism refer to the circumstances under which the incentive mechanism can be implemented. Virtual community incentive mechanisms are primarily triggered through indirect interactions between community members based on tasks or contributions. For example, when a member votes on a community contribution, this triggers a reward or penalty for that member's reputation.

[0090] 3) Reward and punishment actions. In order to induce members to perform specific behaviors, the virtual community implements one or more reward and punishment actions on them. According to the form of incentive factors, this application divides reward and punishment actions into three types: ① Preset rewards and punishments - a type of reward and punishment clearly stipulated by the virtual community. For example, members receive a number of reputation rewards; after reaching a certain reputation threshold, they obtain corresponding status, etc. ② Ranking changes - rewards and punishments after the preset rewards and punishments are completed. Its changes cannot be predicted and need to be determined after comparison with peers in the community. For example, the reputation ranking and influence ranking of community members. ③ Psychological needs - refers to whether the internal needs of members are met, mainly reflected in identification incentives, integration incentives and internal incentives. For example, whether members recognize the value of community contributions, whether they feel reciprocity, whether they feel that the community can meet their social needs, etc.

[0091] Based on the above-mentioned incentive mechanism components, this application defines each incentive rule norm in the incentive mechanism set NORM as:

[0092] norm:ct×ts→rp.

[0093] Where norm∈NORM; ct represents the context of the implementation of the incentive mechanism; ts represents the triggering condition for the implementation of the incentive mechanism; and rp represents the implemented reward or punishment action.

[0094] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0095] In one embodiment, Figure 4 As shown, a multi-agent-based virtual community simulation device is provided, including: an NAA model construction module 402, a virtual community model and incentive mechanism model construction module 404, and a virtual community simulation module 406, wherein:

[0096] NAA model construction module 402 is used to construct an NAA model; the NAA model includes agents representing virtual community members, artifacts representing community tasks and contributions, and specifications representing the incentive mechanism of the virtual community;

[0097] A virtual community model and incentive mechanism model construction module 404 is configured to construct a virtual community model and an incentive mechanism model based on the NAA model. The virtual community model includes a community member model, a contribution model, and agent behavior rules. The community member model is composed of the agents, which include agent attributes and agent behaviors for participating in community activities. The contribution model is composed of the artifacts and is used to enable interaction and collaboration among community members. The agent behavior rules are used to formulate rules for participating in community contributions, and the rules are related to the level of influence of the incentive mechanism. The incentive mechanism model includes context, trigger conditions, and reward and punishment actions, with reward and punishment actions generated through context and trigger conditions to represent the norms.

[0098] The virtual community simulation module 406 is used to perform multi-agent virtual community simulation based on the virtual community model and the incentive mechanism model.

[0099] In one embodiment, the community member model includes the agent's incentive value, professional ability, contribution activity, evaluation activity, task proposal rate, and like rate.

[0100] In one embodiment, the contribution model includes: product type, creating agent, creation days, number of solutions, number of likes, number of dislikes and quality.

[0101] In one embodiment, the agent behavior rules include: task proposal rules, solution contribution rules, like rules, dislike rules, and update rules.

[0102] In one embodiment, the task-proposing rule is:

[0103]

[0104] Among them, AG represents the community member model, ag represents the members in the community member model, q represents the proposed task, Q represents the proposed task set, PQ(ag,pq) represents the level of influence of the specification on the current task-proposing behavior of the agent ag is pq, QR(ag,qr) represents the task-proposing rate of the agent ag is qr, and CR(ag,p) represents the creation of the product p by the agent ag.

[0105] In one embodiment, the contribution solution rule is:

[0106]

[0107] Among them, a represents the scheme of the contribution model, A represents the scheme set of the contribution model, PA(ag,pa) represents the level of influence of the specification on the current contribution solution behavior of agent ag is pa, QR(ag,qr) represents the task proposal rate of agent ag is qr, and CR(ag,p) represents the creation of artifact p by agent ag.

[0108] In one embodiment, the like rule is:

[0109]

[0110] Among them, p represents the product, ART represents the contribution model, PU(ag,pu) represents the influence level of the norm on the current like behavior of agent ag is pu, UR(ag,ur) represents the like rate of agent ag is ur, and UV(ag,p) represents the like of agent ag to product p.

[0111] In one embodiment, the click rule is:

[0112]

[0113] Where p represents the product, ART represents the contribution model, PD(ag,pd) represents the level of influence of the norm on the current dislike behavior of agent ag, which is pd, UR(ag,ur) represents the like rate of agent ag, which is ur, and DV(ag,p) represents whether agent ag dislikes or cancels the like of product p.

[0114] In one embodiment, the update rule is:

[0115]

[0116] Among them, REW(ag,rt,rew) indicates that the rt type incentive value of agent ag is rew, EXP(ag,exp) indicates that the professional ability of agent ag is exp, PAC(ag,pac) indicates that the contribution activity of agent ag is pac, VAC(ag,vac) indicates that the average daily evaluation number of agent ag is vac, QR(ag,qr) indicates that the task proposal rate of agent ag is qr, UR(ag,ur) indicates that the like rate of agent ag is ur, f i is a function that represents the relationship between the agent’s incentive value and its other attributes, f i (rew)=a i ×rew+b i , i∈{1, 2, 3, 4, 5}.

[0117] The specific definitions of the multi-agent virtual community simulation device can be found in the definitions of the multi-agent virtual community simulation method above and will not be repeated here. Each module in the multi-agent virtual community simulation device described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each of these modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0118] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0119] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0120] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A virtual community simulation method based on multi-agent, characterized in that: The method comprises: Constructing an NAA model; the NAA model includes agents representing virtual community members, artifacts representing community tasks and plans, and specifications representing virtual community incentive mechanisms; Based on the NAA model, a virtual community model and an incentive mechanism model are constructed. The virtual community model includes a community member model, a contribution model, and agent behavior rules. The community member model is composed of the agents, which include agent attributes and agent behaviors for participating in community activities. The contribution model is composed of the artifacts and is used to enable interaction and collaboration among community members. The agent behavior rules are used to formulate rules for participating in community contributions, and the rules are related to the level of influence of the incentive mechanism. The incentive mechanism model includes context, trigger conditions, and reward and punishment actions. The reward and punishment actions are generated by the context and trigger conditions to represent the norms. According to the virtual community model and incentive mechanism model, a multi-agent-based virtual community simulation is performed.

2. The method according to claim 1, characterized in that The community member model includes the agent's incentive value, professional ability, contribution activity, evaluation activity, task proposal rate, and like rate.

3. The method according to claim 1, characterized in that The contribution model includes: product type, creating agent, creation days, number of solutions, number of likes, number of dislikes and quality.

4. The method according to claim 1, wherein The agent behavior rules include: task proposal rules, solution contribution rules, like rules, dislike rules and update rules.

5. The method according to claim 4, characterized in that The task proposal rules are: Among them, AG represents the community member model, ag represents the members in the community member model, q represents the proposed task, Q represents the proposed task set, PQ(ag,pq) represents the level of influence of the specification on the current proposed task behavior of agent ag is pq, QR(ag,qr) represents the proposed task rate of agent ag is qr, and CR(ag,p) represents the creation of artifact p by agent ag.

6. The method according to claim 4, characterized in that The contribution solution rules are: Among them, a represents the scheme of the contribution model, A represents the scheme set of the contribution model, PA(ag,pa) represents the level of influence of the specification on the current contribution solution behavior of agent ag is pa, QR(ag,qr) represents the task proposal rate of agent ag is qr, and CR(ag,p) represents the creation of artifact p by agent ag.

7. The method according to claim 4, characterized in that The likes rules are: Among them, p represents the product, ART represents the contribution model, PU(ag,pu) represents the influence level of the norm on the current like behavior of agent ag is pu, UR(ag,ur) represents the like rate of agent ag is ur, and UV(ag,p) represents the like of agent ag to product p.

8. The method according to claim 4, characterized in that The click-and-tick rules are: Where p represents the product, ART represents the contribution model, PD(ag,pd) represents the level of influence of the norm on the current dislike behavior of agent ag, which is pd, UR(ag,ur) represents the like rate of agent ag, which is ur, and DV(ag,p) represents whether agent ag dislikes or cancels the like of product p.

9. The method according to claim 4, characterized in that The update rules are: Among them, REW(ag,rt,rew) indicates that the rt type incentive value of agent ag is rew, EXP(ag,exp) indicates that the professional ability of agent ag is exp, PAC(ag,pac) indicates that the contribution activity of agent ag is pac, VAC(ag,vac) indicates that the average daily evaluation number of agent ag is vac, QR(ag,qr) indicates that the task proposal rate of agent ag is qr, UR(ag,ur) indicates that the like rate of agent ag is ur, f i is a function that represents the relationship between the agent’s incentive value and its other attributes, f i (rew)=a i ×rew+b i , i∈{1,2,3,4,5}.

10. A virtual community simulation device based on multi-agent, characterized in that: The device comprises: NAA model construction module, used to construct the NAA model; the NAA model includes intelligent agents representing virtual community members, artifacts representing community tasks and plans, and specifications representing virtual community incentive mechanisms; A virtual community model and incentive mechanism model construction module is used to construct a virtual community model and an incentive mechanism model based on the NAA model. The virtual community model includes: a community member model, a contribution model, and agent behavior rules. The community member model is composed of the agents, which include agent attributes and agent behaviors for participating in community activities. The contribution model is composed of the artifacts and is used to enable interaction and collaboration among community members. The agent behavior rules are used to formulate rules for participating in community contributions, and the rules are related to the impact level of the incentive mechanism. The incentive mechanism model includes: context, trigger conditions, and reward and punishment actions. The reward and punishment actions are generated by the context and trigger conditions to represent the norms. The virtual community simulation module is used to perform multi-agent virtual community simulation based on the virtual community model and incentive mechanism model.

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