CN113724096B - Group knowledge sharing method based on public evolution game model - Google Patents
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Abstract
The invention belongs to the technical field of information resource sharing, and discloses a community knowledge sharing method based on a public evolution game model. According to the method, a community knowledge sharing corresponding public evolution game model is built based on the characteristics of an online learning community, a particle swarm optimization algorithm is combined, the strategies of the users are continuous according to the behavior characteristics of the users, and the memory coefficients and the imitation coefficients of the users are adjusted to dynamically adjust the sharing strategy of the users, so that the community sharing level can be improved, the community can reach a high sharing level steady state, and the long-term stable development of the community is promoted.
Description
Technical Field
The invention belongs to the technical field of information resource sharing, and particularly relates to a community knowledge sharing method based on a public evolution game model.
Background
Knowledge sharing in an online learning community is an important link, but because a certain cost is required for carrying out knowledge sharing, a plurality of members in the community adopt a non-sharing strategy, which can lead to low knowledge sharing degree of the community, and the network sharing data volume is low, so that the health development of a community network is not facilitated, and therefore, how to promote the members in the community to carry out knowledge sharing and improve the community knowledge sharing degree is a technical problem which needs to be solved urgently by people in the field.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide a community knowledge sharing method based on a public evolution game model so as to improve the community sharing level, so that the community can reach a high sharing level steady state, and the long-term stable development of the community is promoted.
The invention provides a community knowledge sharing method based on a public evolution game model, which comprises the following steps:
step 1, defining a community of an online learning community as a set comprising a plurality of user nodes, taking the proportion of the user for carrying out knowledge sharing as a strategy of the user, taking the degree of the user for adjusting the knowledge sharing proportion as the strategy adjusting speed of the user, taking the total amount of knowledge acquired by the user as the income of the user, and constructing a public evolution game model corresponding to the community knowledge sharing;
wherein, the value range of the knowledge sharing proportion is [0,1];
step 2, initializing all user nodes, randomly setting the strategy of each user with the same probability, setting the strategy adjustment speed of each user to 0, and recording the initialized strategy and the initialized strategy adjustment speed;
step 3, calculating the benefits of each user through the public evolution game model according to the recorded strategy of each user, and recording the benefits;
step 4, obtaining a historical optimal benefit of each user and a historical optimal strategy corresponding to the historical optimal benefit of the user according to the recorded benefit of each user, and obtaining an optimal benefit and an optimal strategy corresponding to the optimal benefit in a group where each user is located;
step 5, updating the memory coefficient and the imitation coefficient of each user according to the historical optimal benefit of each user and the optimal benefit obtained from the group where each user is located;
wherein, the memory coefficient represents the probability of the user changing the strategy according to the experience of the user, and the imitation coefficient represents the probability of the user changing the strategy according to the experience of the group where the user is located;
step 6, updating the strategy and the strategy adjustment speed of each user by combining a particle swarm optimization algorithm, and repeatedly executing the steps 3 to 5 after updating until the knowledge sharing level of the group reaches a stable state;
the method comprises the steps of enabling a strategy of a user to correspond to a particle position in a particle swarm optimization algorithm, enabling a strategy adjustment speed of the user to correspond to a particle speed in the particle swarm optimization algorithm, and enabling benefits of the user to correspond to particle fitness in the particle swarm optimization algorithm.
Preferably, in the step 2, the policy of initializing the user adopts the following manner:
strategy＝slimit(1)+(slimit(2)-slimit(1))*rand(1,N)
the strategy represents a policy, the slimit (1) and the slimit (2) respectively represent an upper policy limit and a lower policy limit, the rand function generates random values, N represents the total number of users, and rand (1, N) represents the generation of N values with the value range of [0,1] and N user nodes are given as an initialization policy.
Preferably, in the step 3, the calculating the benefit of the user is as follows:
wherein ,Ui,j (t) represents the benefits obtained by the participation of the user i in the group centering on the neighbor j in the t-th round of game, i epsilon N, j epsilon N, N representing the total number of users; q (Q) i Representing the amount of knowledge possessed by user i, S i,t Representing the strategy of the user i in the t-th round of game; k (k) i Representing the number of neighbors, k, of user i i +1 denotes the sum of the number of users i and their neighbors, r denotes the gain factor, k j Representing the number of neighbors, k, of user j j +1 represents the sum of the number of neighbors of user j; omega shape j Representing a neighbor set centered on user j; l E (omega) j ∪j) Indicating that user l belongs to user j and neighbor set thereof, Q l Representing the amount of knowledge possessed by user l, S l,t Representing the strategy of user l in the t-th round game, k l Representing the number of neighbors, k, of user l l +1 represents the sum of the number of users/and their neighbors;
wherein ,Ui (t) represents the accumulated revenue for user i in the t-th round of gaming, Ω i Represents a neighbor set centered on user i, j e (Ω i U.i) indicates that user j belongs to user i and neighbor set thereof; c represents a cost factor.
Preferably, in the step 5, the memory coefficient is denoted as w, the imitation coefficient is denoted as 1-w, and the updating of the memory coefficient and imitation coefficient of the user is performed in the following manner:
wherein ,representing historical optimal benefits obtained by a user i before starting a t+1st round of game; />Representing the optimal benefits obtained in the group of the user i when the t-th round of game is performed, wherein the group of the user i comprises the user i and a neighbor set thereof; p represents the revenue that user i obtains in the t-th game.
Preferably, in the step 6, the following method is adopted for updating the policy and the policy adjustment speed of the user in combination with the particle swarm optimization algorithm:
S i,t+1 ＝S i,t +v i,t+1
wherein ,vi,t+1 The strategy adjustment speed of the user i in the t+1 round of game is represented; v i,t The strategy adjustment speed of the user i in the t-th round game is represented; w represents the coefficient of memory and,representing a history optimal strategy obtained by a user i before starting a t+1st round of game; s is S i,t Representing the strategy of the user i in the t-th round of game; />The method comprises the steps that a strategy adopted by a user obtaining the highest benefit in a group where a user i is located in a t-th round of game is represented, wherein the group where the user i is located comprises the user i and a neighbor set thereof; s is S j,t Representing a policy representing a user j when gaming in the t-th round; s is S i,t+1 Representing the strategy of user i in the t+1 turn of game.
One or more technical schemes provided by the invention have at least the following technical effects or advantages:
in the invention, a community group of an online learning community is defined as a set containing a plurality of user nodes, the proportion of the users for carrying out knowledge sharing is used as a strategy of the users, the degree of the users for adjusting the knowledge sharing proportion is used as the strategy adjusting speed of the users, the total amount of knowledge acquired by the users is used as the income of the users, and a public evolution game model corresponding to community knowledge sharing is constructed; initializing strategies and strategy adjustment speeds corresponding to all users; calculating the income of each user through a public evolution game model based on the strategy of the user; based on the profits of the users, obtaining the historical optimal profits of each user and the historical optimal strategies corresponding to the historical optimal profits of the users, and obtaining the optimal profits of each user in the group and the optimal strategies corresponding to the optimal profits; updating the memory coefficient and the imitation coefficient of each user according to the historical optimal benefit of each user and the optimal benefit obtained from the group of each user; the strategy of the user is corresponding to the particle position in the particle swarm optimization algorithm, the strategy adjustment speed of the user is corresponding to the particle speed in the particle swarm optimization algorithm, the income of the user is corresponding to the particle fitness in the particle swarm optimization algorithm, the strategy and the strategy adjustment speed of each user are updated by combining the particle swarm optimization algorithm, and the steps are repeatedly executed until the population reaches a steady state after updating. The community knowledge sharing method based on the online learning features constructs a community knowledge sharing corresponding public evolution game model, combines a particle swarm optimization algorithm, and continuously changes the strategies of users according to the behavior characteristics of the users, and adjusts the memory coefficients and the imitation coefficients of the users to dynamically adjust the sharing strategies of the users, so that the community sharing level can be improved, the community can reach a high sharing level steady state, and the long-term stable development of the community is promoted.
Drawings
Fig. 1 is a flowchart of a community knowledge sharing method based on a public item evolution game model according to an embodiment of the present invention.
Detailed Description
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
For knowledge sharing, the traditional gaming model employs a pure strategy, i.e., sharing or not sharing. In real-world situations, the decision-making process is complex and has a certain degree of uncertainty for users with independent action, thinking, judgment and decision-making capabilities, so that the hybrid strategy is more reasonable than the pure strategy. Therefore, according to the characteristics of the online learning community, the method simulates the online learning community by adopting the public game model, wherein the model belongs to the multi-person game type and is more compatible with the characteristics of the interaction behavior of the online learning community.
The invention defines the proportion that each user can determine to share, and the proportion is the strategy that the user performs knowledge sharing selection. Defining a policy in the interval of [0,1] to characterize the degree and willingness of users to share knowledge, wherein 0 indicates that users do not share any knowledge, but according to community characteristics, the policy can also obtain benefits from sharing of others without bearing any loss; and 1 represents that the user shares all knowledge, and the cost is loss caused by loss of knowledge monopolization due to sharing, time, energy and the like spent in the knowledge sharing process. Thus, different sharing proportions of users correspond to different policies.
The particle swarm optimization algorithm (PSO) has good evolution optimizing characteristics, is mostly used for optimizing in continuous variables, and has a plurality of similar characteristics to the behavior of particles in the PSO in the individual strategy updating process in the continuous strategy space public evolution game, so that the PSO is inspired by the invention.
The PSO algorithm is initialized to generate a group of random examples, the core of the algorithm is that each iteration, the particle adjusts the speed and the position by following two extreme values of the self historical optimal solution and the population historical optimal solution, the dynamically adjusted speed is the key for updating the position of the particle, the position of the particle symbolizes the distance from the optimal solution, and the fitness is the unique standard for evaluating the solution of the particle. From both biological and social perspectives, users want to be able to obtain higher benefits by themselves through interactions, so benefits have an important impact on the user's policy selection. Therefore, the PSO algorithm is introduced in the process of updating the user strategy, the strategy (contribution ratio) of the user is corresponding to the particle position, the benefit of the user is corresponding to the fitness of the particles, and the user adjusts the contribution ratio by combining individual experience and social group experience to update and evolve the strategy.
The invention combines the particle swarm optimization algorithm with the public game technology, the strategy of the user is continuous according to the behavior characteristics of the user, and the memory coefficient and the imitation coefficient of the user are adjusted by setting the dynamic function so as to dynamically adjust the sharing strategy of the user, thereby improving the sharing level of the swarm, enabling the community to reach a steady state with a high sharing level and promoting the long-term stable development of the community.
The embodiment provides a community knowledge sharing method based on a public evolution game model, referring to fig. 1, comprising the following steps:
step 1: the community group of the online learning community is defined as a set comprising a plurality of user nodes, the proportion of the user for carrying out knowledge sharing is used as a strategy of the user, the degree of the user for adjusting the knowledge sharing proportion is used as the strategy adjusting speed of the user, the total amount of knowledge acquired by the user is used as the income of the user, and a community knowledge sharing corresponding public evolution game model is constructed.
Wherein, the value range of the knowledge sharing proportion is [0,1].
And constructing a corresponding public evolution game model according to the online learning community and the learning characteristics of the users in the community. The characteristics mainly refer to: community users have a certain knowledge level (i.e. the amount of knowledge they possess), users' share willingness, costs the users need to spend on knowledge sharing, and the users can obtain knowledge benefits.
A group of N user nodes is set, represented by the set n= {1,2,., N; according to learning characteristics of online learning community users, definition of 'knowledge quantity', 'sharing cost', 'strategy adjustment speed', 'game income' is carried out on each user node i epsilon N.
Knowledge quantity Q refers to: knowledge quantity owned by the user himself. Shared cost C (cost coefficient) refers to: the user spends time and effort for sharing knowledge. Policy S refers to: the proportion of knowledge sharing by the user. Policy adjustment speed v refers to: the user adjusts the degree of the knowledge sharing proportion, specifically, the user adjusts the direction (i.e. the degree) of the strategy according to the self inertia speed (i.e. the strategy adjustment speed of the user in the last game is 0 for the first time), the individual experience (i.e. the degree of the user learning the self optimal strategy), the social experience (i.e. the degree of the user learning the strategy with the highest benefit to the user in the group). Revenue P refers to: knowledge amount obtained after the user plays the game.
Step 2: initializing all user nodes, randomly setting the strategy of each user with the same probability, setting the strategy adjustment speed of each user to 0, and recording the initialized strategy and the initialized strategy adjustment speed.
On the one hand, when initializing policies, let strategy represent a policy set of each user, different policies represent different contribution ratios, which are continuous values, where the initialization can be performed in the following way:
strategy＝slimit(1)+(slimit(2)-slimit(1))*rand(1,N)
the method comprises the steps that the slimit (1) and the slimit (2) respectively represent an upper policy limit and a lower policy limit, the rand function generates random values, N represents the total number of users, and the rand (1, N) represents the generation of N values with the value range of [0,1] and N user nodes are given to serve as an initialization policy.
For example, slimit (1) =0, slimit (2) =1.
On the other hand, the policy adjustment speed of each user is initialized to 0.
In the following game process, each user can adjust the strategy adjustment speed according to the historical benefits and the benefits of other neighbors, and then further update the strategy.
Step 3: and calculating the benefits of each user through the public evolution game model according to the recorded strategy of each user, and recording the benefits.
User i participates in co-k centered on itself and centered on its neighbors i Gaming of +1 populations, where k i Is the number of neighbors of user i.
Said step 3 comprises the sub-steps of:
step 3.1: the benefits each user receives in each round of gaming involving a certain neighbor-centric community, including the user, are calculated.
In the t-th round game, the benefits obtained by the participation of the user i in the group taking the neighbor j as the center are expressed as follows:
wherein ,Ui,j (t) represents the benefits obtained by the participation of the user i in the group centering on the neighbor j in the t-th round of game, i epsilon N, j epsilon N, N representing the total number of users; q (Q) i Representing the amount of knowledge possessed by user i, S i,t Representing the strategy (sharing proportion) of the user i in the t-th round game, wherein S is more than or equal to 0 i,t ≤1；k i Representing the number of neighbors, k, of user i i +1 represents the sum of the number of users i and their neighbors, r represents the gain factor, k j Representing the number of neighbors, k, of user j j +1 represents the sum of the number of neighbors of user j; omega shape j Representing a neighbor set centered on user j; l E (omega) j U.j) indicates that user l belongs to user j and neighbor set thereof, Q l Representing the amount of knowledge possessed by user l, S l,t Representing the strategy of user l in the t-th round game, k l Representing the number of neighbors, k, of user l l +1 represents the sum of the number of users/and their neighbors.
For example, the gain coefficient is 1.56, the shared knowledge quantity in the group is multiplied by 1.56 and then is evenly distributed to the members in the group, and the shared quantity of the members is subtracted, so that the benefit obtained by the members in the group is obtained.
Step 3.2: and calculating the total income obtained by each user in each round of game.
Accumulated revenue U obtained by user i in t-th round game i (t) is expressed as:
wherein ,Ωi Representing a set of neighbors centered on user i, j∈(Ωi∪i) indicating that user j belongs to user i and neighbor set thereof; c represents a cost factor.
For example, the cost coefficient is 0.01, and the amount of knowledge shared by the user is multiplied by the cost coefficient of 0.01, which is the cost spent by the user for sharing the knowledge. The cost should be much less than the amount of shared knowledge of the user, so the cost factor should be small enough.
Step 4: according to the recorded benefits of each user, obtaining the historical optimal benefits of each user and the historical optimal strategies corresponding to the historical optimal benefits of the user, and obtaining the optimal benefits and the optimal strategies corresponding to the optimal benefits in the group where each user is located.
Step 5: and updating the memory coefficient and the imitation coefficient of each user according to the historical optimal benefit of each user and the optimal benefit obtained in the group of each user.
The memory coefficient represents the probability of the user changing the strategy according to the experience of the user, and the imitation coefficient represents the probability of the user changing the strategy according to the experience of the group where the user is located.
Namely, the memory coefficient w and the imitation coefficient (1-w) of the user are updated according to the related data obtained in the step 4. The update formula is as follows:
wherein ,indicating the historical optimum gain of user i before starting the t+1st round,/for game>The optimal benefits obtained in the group (including the user) of the user i in the t-th round game are represented, and P represents the benefits obtained in the current (t-th round) game by the user i.
Step 6: and (3) updating the strategy and the strategy adjustment speed of each user by combining the particle swarm optimization algorithm, and repeatedly executing the steps 3 to 5 after updating until the knowledge sharing level of the swarm reaches a stable state.
The method comprises the steps of enabling a strategy of a user to correspond to a particle position in a particle swarm optimization algorithm, enabling a strategy adjustment speed of the user to correspond to a particle speed in the particle swarm optimization algorithm, and enabling benefits of the user to correspond to particle fitness in the particle swarm optimization algorithm.
That is, the invention updates the policy by using the core idea of the PSO algorithm, where the attribute of the policy corresponds to the "location" in the PSO algorithm, the benefit represents the "fitness" in the PSO algorithm, and the policy adjustment speed corresponds to the "speed" in the PSO algorithm.
The strategy of the user and the strategy adjustment speed are updated by combining a particle swarm optimization algorithm in the following way:
S i,t+1 ＝S i,t +v i,t+1
wherein ,vi,t+1 The strategy adjustment speed of the user i in the t+1 round of game is represented; v i,t The strategy adjustment speed of the user i in the t-th round game is represented; w represents the coefficient of memory and,representing a history optimal strategy obtained by a user i before starting a t+1st round of game; s is S i,t Representing the strategy of the user i in the t-th round of game; />The method comprises the steps that a strategy adopted by a user obtaining the highest benefit in a group where a user i is located in a t-th round of game is represented, wherein the group where the user i is located comprises the user i and a neighbor set thereof; s is S j,t Representing a policy representing a user j when gaming in the t-th round; s is S i,t+1 Representing the strategy of user i in the t+1 turn of game.
When the knowledge sharing level in the group reaches stability, all users can select strategies which are beneficial to the healthy development of the community, so that the technical effects of improving the knowledge sharing degree of the group, improving the network sharing data volume and promoting the healthy development of the community network can be achieved.
In conclusion, the community knowledge sharing method based on the public evolution game model provided by the invention can promote the users to learn to the users with high income, and can enable the community to reach a higher cooperation level.
Finally, it should be noted that the above-mentioned embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same, and although the present invention has been described in detail with reference to examples, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention, and all such modifications and equivalents are intended to be encompassed in the scope of the claims of the present invention.
Claims (5)
1. The community knowledge sharing method based on the public evolution game model is characterized by comprising the following steps of:
step 1, defining a community of an online learning community as a set comprising a plurality of user nodes, taking the proportion of the user for carrying out knowledge sharing as a strategy of the user, taking the degree of the user for adjusting the knowledge sharing proportion as the strategy adjusting speed of the user, taking the total amount of knowledge acquired by the user as the income of the user, and constructing a public evolution game model corresponding to the community knowledge sharing;
wherein, the value range of the knowledge sharing proportion is [0,1];
step 2, initializing all user nodes, randomly setting the strategy of each user with the same probability, setting the strategy adjustment speed of each user to 0, and recording the initialized strategy and the initialized strategy adjustment speed;
step 3, calculating the benefits of each user through the public evolution game model according to the recorded strategy of each user, and recording the benefits;
step 4, obtaining a historical optimal benefit of each user and a historical optimal strategy corresponding to the historical optimal benefit of the user according to the recorded benefit of each user, and obtaining an optimal benefit and an optimal strategy corresponding to the optimal benefit in a group where each user is located;
step 5, updating the memory coefficient and the imitation coefficient of each user according to the historical optimal benefit of each user and the optimal benefit obtained from the group where each user is located;
wherein, the memory coefficient represents the probability of the user changing the strategy according to the experience of the user, and the imitation coefficient represents the probability of the user changing the strategy according to the experience of the group where the user is located;
step 6, updating the strategy and the strategy adjustment speed of each user by combining a particle swarm optimization algorithm, and repeatedly executing the steps 3 to 5 after updating until the knowledge sharing level of the group reaches a stable state;
the method comprises the steps of enabling a strategy of a user to correspond to a particle position in a particle swarm optimization algorithm, enabling a strategy adjustment speed of the user to correspond to a particle speed in the particle swarm optimization algorithm, and enabling benefits of the user to correspond to particle fitness in the particle swarm optimization algorithm.
2. The community knowledge sharing method based on the public evolution game model according to claim 1, wherein in the step 2, the policy of initializing the user adopts the following manner:
strategy＝slimit(1)+(slimit(2)-slimit(1))*rand(1,N)
the strategy represents a policy, the slimit (1) and the slimit (2) respectively represent an upper policy limit and a lower policy limit, the rand function generates random values, N represents the total number of users, and rand (1, N) represents the generation of N values with the value range of [0,1] and N user nodes are given as an initialization policy.
3. The community knowledge sharing method based on the public evolution game model according to claim 1, wherein in the step 3, the profit of the user is calculated by the following way:
wherein ,Ui,j (t) represents the benefits obtained by the participation of the user i in the group centering on the neighbor j in the t-th round of game, i epsilon N, j epsilon N, N representing the total number of users; q (Q) i Representing the amount of knowledge possessed by user i, S i,t Representing the strategy of the user i in the t-th round of game; k (k) i Representing the number of neighbors, k, of user i i +1 denotes the sum of the number of users i and their neighbors, r denotes the gain factor, k j Representing the number of neighbors, k, of user j j +1 represents the sum of the number of neighbors of user j; omega shape j Representing a neighbor set centered on user j; l E (omega) j U.j) indicates that user l belongs to user j and neighbor set thereof, Q l Representing the amount of knowledge possessed by user l, S l,t Representing the strategy of user l in the t-th round game, k l Representing the number of neighbors, k, of user l l +1 represents the sum of the number of users/and their neighbors;
wherein ,Ui (t) represents the accumulated revenue for user i in the t-th round of gaming, Ω i Represents a neighbor set centered on user i, j e (Ω i U.i) indicates that user j belongs to user i and neighbor set thereof; c represents a cost factor.
4. The community knowledge sharing method based on the public evolution game model according to claim 1, wherein in the step 5, the memory coefficient is marked as w, the imitation coefficient is marked as 1-w, and the memory coefficient and the imitation coefficient of the user are updated by the following ways:
wherein ,representing historical optimal benefits obtained by a user i before starting a t+1st round of game; />Representing the optimal benefits obtained in the group of the user i when the t-th round of game is performed, wherein the group of the user i comprises the user i and a neighbor set thereof; p represents the revenue that user i obtains in the t-th game.
5. The community knowledge sharing method based on the public evolution game model according to claim 1, wherein in the step 6, the updating of the policy and the policy adjustment speed of the user in combination with the particle swarm optimization algorithm adopts the following modes:
S i,t+1 ＝S i,t +v i,t+1
wherein ,vi,t+1 The strategy adjustment speed of the user i in the t+1 round of game is represented; v i,t The strategy adjustment speed of the user i in the t-th round game is represented; w represents the coefficient of memory and,representing a history optimal strategy obtained by a user i before starting a t+1st round of game; s is S i,t Representing the strategy of the user i in the t-th round of game; />The method comprises the steps that a strategy adopted by a user obtaining the highest benefit in a group where a user i is located in a t-th round of game is represented, wherein the group where the user i is located comprises the user i and a neighbor set thereof; s is S j,t Representing a policy representing a user j when gaming in the t-th round; s is S i,t+1 Representing the strategy of user i in the t+1 turn of game.
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