Intelligent agent situation information enhancement method and system based on knowledge reasoning

Abstract

The invention discloses an agent situation information enhancement method and system based on knowledge reasoning. The method comprises the following steps: determining initial situation information obtained when a target agent perceives a current confrontation environment; constructing rule knowledge for each agent in the confrontation environment based on first-order predicate logic; converting the initial situation information into form expression of first-order predicate logic to obtain form-compatible situation information; based on constructed rule knowledge, performing knowledge reasoning on the form-compatible situation information to obtain enhanced situation information of the target agent; wherein the enhanced situation information comprises hidden situation information obtained through knowledge reasoning. Therefore, on the basis of objectively observed situation information, potential information which cannot be directly observed is reasoned by means of knowledge of field experts, so that analysis and decision-making of the intelligent agent are supported by more comprehensive information.

Description

Technical Field

[0001] The present application relates to the field of computer technology, specifically to the field of game confrontation and other technical fields, and in particular to a method and system for enhancing the situation information of an intelligent agent based on knowledge reasoning. Background Art

[0002] The application of intelligent agent game confrontation is very extensive, covering multiple fields, including game AI, autonomous driving, financial transactions, network security, etc., providing powerful tools and models for solving real-world problems, especially in making simulation decisions, creating economic value, enhancing security, and promoting social impact and research. It has an important role and far-reaching significance.

[0003] In game confrontation, rule-based confrontation and machine learning model-based confrontation are two common strategy design methods. Rule-based confrontation methods mainly rely on manually designed rules or strategies to guide the behavior of intelligent agents. Among them, the rules are usually formulated based on game theory, professional knowledge or experience. The advantage of rule-based confrontation methods is high transparency, and the behavior of intelligent agents is easy to understand and explain; but the design of rules may be limited and difficult to cope with complex, unknown or changing environments. The confrontation method based on machine learning models uses machine learning algorithms to train intelligent agents so that they can automatically learn and optimize strategies in games. This method can handle more complex and dynamic environments and can learn from large amounts of data. But the disadvantage is that the internal decision-making process of the model may not be transparent, and professional knowledge is required to explain the behavior of the model.

[0004] Regardless of the game confrontation strategy adopted, it is very important to obtain complete and accurate situation information. The agent game confrontation process basically includes four parts: observation, analysis, decision-making, and action. Figure 1 As shown in the figure. Both of the above strategies focus on the analysis and decision-making part, and observation, as the first link, has an important impact on analysis and decision-making. Observation mainly refers to the changes in the other party, ourselves, and the environment that the agent objectively perceives, that is, situation information. Through observation, the agent collects information and builds a model of the current state in order to make more reasonable decisions. Therefore, whether the observed information is accurate and complete is very important, which directly affects the rationality and effectiveness of the decision.

[0005] Due to the confrontational nature of game confrontation, the situation information observed intuitively may not be complete and accurate. Game confrontation is a confrontational strategic interaction between the opponent and us in order to achieve our respective goals, using our respective power units. Both parties often use all entities to cooperate under the guidance of their own macro-strategies, hide their true intentions, and gradually move towards their own goals. For example, in a real-time strategy game, the opponent may make a feint to the east and attack in the west, deliberately hiding the coordination relationship between important characters, etc. If we can distinguish the true from the false in the fog of confrontation and accurately infer the implicit information, it will help to adopt an effective confrontation strategy. Summary of the invention

[0006] The present application provides a method and system for enhancing agent situation information based on knowledge reasoning to solve the problem that the agent situation information intuitively obtained in the existing game confrontation process may be incomplete and inaccurate, thus affecting the confrontation decision.

[0007] The technical solution is as follows: In a first aspect, a method for enhancing agent situation information based on knowledge reasoning is provided, comprising: Determine the initial situation information obtained by the target agent when perceiving the current confrontation environment; Based on the first-order predicate logic, rule knowledge is constructed for each intelligent agent in the adversarial environment; wherein the rule knowledge constructed for each intelligent agent can formally express and reason about the intelligent agent and its capabilities and relationships; Converting the initial situation information into a formal expression of first-order predicate logic to obtain situation information that is compatible with the form; Based on the constructed rule knowledge, knowledge reasoning is performed on the form-compatible situation information to obtain enhanced situation information of the target intelligent agent; wherein the enhanced situation information includes hidden situation information obtained through knowledge reasoning.

[0008] In a possible implementation, the method further includes: sending the enhanced situation information to a target intelligent agent, so that the target intelligent agent uses the enhanced situation information to make a confrontation decision.

[0009] In one possible implementation, first-order predicate logic includes multiple basic elements: individual constants, predicates, quantifiers, and logical connectives; wherein, individual constants refer to the intelligent agents participating in the game confrontation in the confrontation environment; predicates are used to describe the properties and / or relationships of the intelligent agents; quantifiers include universal quantifiers and existential quantifiers, wherein the universal quantifiers indicate that the predicates are applicable to all intelligent agents, and the existential quantifiers indicate that the predicates are applicable to at least one intelligent agent; logical connectives are used to combine atomic propositions to form more complex propositions.

[0010] In one possible implementation, different intelligent agents in a confrontation environment are divided into different role types according to their capabilities: air attack role, ground attack role, and escort role; among them, the main ability of the air attack role is to attack aerial targets; the main ability of the ground attack role is to attack ground targets; the main ability of the escort role is to protect other roles from being attacked.

[0011] In a possible implementation, in a game confrontation scenario, rule knowledge is constructed for each agent in the confrontation environment based on first-order predicate logic, including: If the agent is a ground attack character, then the agent has no air attack capability, and the corresponding first rule knowledge is constructed as follows: ; If the agent is an air attack character, then the agent has air attack capability, and the corresponding second rule knowledge is constructed as follows: ; If the agent has no air attack capability but enters the alert range of the opponent's air attack role, then the agent has an escort role, and the corresponding third rule knowledge is constructed as follows: ; If the agent has an escort role and it is not observed, it is inferred that the escort role is a stealth agent, and the corresponding fourth rule knowledge is constructed as follows: .

[0012] In a possible implementation, the initial situation information is converted into a formal expression of first-order predicate logic to obtain situation information that is compatible with the form, specifically including: determining a first agent and a second agent of the confrontation game contained in the initial situation information; based on the first-order predicate logic, determining situation information that is compatible with the form of the first agent and the second agent; wherein the situation information that is compatible with the form includes: first situation information: the first agent For ground attack characters: ; Second situation information: The second agent F is an air attack character: ; Third situation information: first agent and the second agent It is an adversarial relationship: ; Fourth situation information: first agent The distance to the second agent F: ; Fifth situation information: second agent Warning range: .

[0013] In a possible implementation, based on the constructed rule knowledge, knowledge reasoning is performed on the form-compatible situation information to obtain enhanced situation information of the target intelligent agent, specifically including: Based on the first rule knowledge and the fifth situation information, the first reasoning information is obtained by reasoning: the first agent No anti-air attack capability: ; Based on the second rule knowledge and the second situation information, the second reasoning information is obtained by reasoning: the second agent Has anti-air attack capability: ; Based on the fourth situation information and the fifth situation information, the third reasoning information is obtained by reasoning: the first agent Entered the second agent Warning range: ; Based on the first reasoning information, the second reasoning information, the third situation information, and the third reasoning information, the fourth reasoning information is obtained by reasoning: the first agent There is escort role y: ; If the escort role y is not observed, then there is the fifth inference information: ; Based on the first situation information, the fourth reasoning information, and the fifth reasoning information, the sixth reasoning information is inferred: the escort character y is a stealth agent: ; The first reasoning information, the second reasoning information, the third reasoning information, the fourth reasoning information, the fifth reasoning information and the sixth reasoning information obtained by reasoning are determined as the enhanced situation information of the target intelligent agent.

[0014] In a second aspect, a computing device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above-mentioned method when executing the computer program.

[0015] According to a third aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned method is implemented.

[0016] In a fourth aspect, a system for enhancing intelligent agent situation information based on knowledge reasoning is provided, comprising: A determination module is used to determine the initial situation information obtained by the target intelligent agent when perceiving the current confrontation environment; A construction module is used to construct rule knowledge for each intelligent agent in the confrontation environment based on first-order predicate logic; wherein the rule knowledge constructed for each intelligent agent can formally express and reason about the intelligent agent and its capabilities and relationships; a conversion module is used to convert the initial situation information into a formal expression of first-order predicate logic to obtain situation information that is compatible with the form; The reasoning module is used to perform knowledge reasoning on the form-compatible situation information based on the constructed rule knowledge to obtain enhanced situation information of the target intelligent agent; wherein the enhanced situation information includes hidden situation information obtained through knowledge reasoning.

[0017] The beneficial effects of the technical solution provided by this application include at least: It can be seen from the above technical solution that the initial situation information obtained by the target intelligent agent when perceiving the current confrontation environment is determined; based on the first-order predicate logic, rule knowledge is constructed for each intelligent agent in the confrontation environment; the initial situation information is converted into a formal expression of the first-order predicate logic to obtain a situation information that is compatible with the form; based on the constructed rule knowledge, the situation information that is compatible with the form is subjected to knowledge reasoning to obtain the enhanced situation information of the target intelligent agent; wherein the enhanced situation information contains hidden situation information obtained through knowledge reasoning. In this way, based on the objectively observed situation information, potential but directly observable information is inferred through the knowledge of domain experts, thereby supporting the analysis and decision-making of the intelligent agent with more comprehensive information.

[0018] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in 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 only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 It is a schematic diagram of the intelligent agent game confrontation process in the prior art.

[0021] Figure 2 It is a schematic diagram of the steps of a method for enhancing the situation information of an intelligent agent based on knowledge reasoning proposed in an embodiment of the present application.

[0022] Figure 3 It is a schematic diagram of the technical solution for the intelligent agent game confrontation process proposed in this application.

[0023] Figure 4 It is a schematic diagram of a game confrontation scenario proposed in an embodiment of the present application.

[0024] Figure 5 It is a schematic diagram of initial situation information in an embodiment of the present application.

[0025] Figure 6 It is a schematic diagram of enhancing situation information in an embodiment of the present application.

[0026] Figure 7 It is a structural diagram of an intelligent agent situation information enhancement system based on knowledge reasoning proposed in an embodiment of the present application.

[0027] Figure 8 It is a block diagram of a computing device according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0029] Obviously, the described embodiments are only part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0030] It should be noted that the terminal devices involved in the embodiments of the present application may include but are not limited to mobile phones, personal digital assistants (PDAs), wireless handheld devices, tablet computers and other smart devices; display devices may include but are not limited to personal computers, televisions and other devices with display functions.

[0031] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0032] In order to solve the problem mentioned above that the situation information of the intelligent agent intuitively obtained in the existing game confrontation process may be incomplete and inaccurate, which affects the confrontation decision, this application proposes an intelligent agent situation information enhancement scheme based on knowledge reasoning, and its inventive concept is: determine the initial situation information obtained by the target intelligent agent to perceive the current confrontation environment; based on the first-order predicate logic, construct rule knowledge for each intelligent agent in the confrontation environment; convert the initial situation information into a formal expression of the first-order predicate logic to obtain a situation information that is compatible with the form; based on the constructed rule knowledge, perform knowledge reasoning on the situation information that is compatible with the form to obtain the enhanced situation information of the target intelligent agent; wherein the enhanced situation information contains hidden situation information obtained through knowledge reasoning. In this way, based on the objectively observed situation information, the knowledge of domain experts is used to infer potential but directly observable information, thereby supporting the analysis and decision-making of the intelligent agent with more comprehensive information.

[0033] The present application scheme is described in detail below through specific embodiments.

[0034] Reference Figure 2 As shown, it is a schematic diagram of the steps of a method for enhancing the situation information of an intelligent agent based on knowledge reasoning proposed in an embodiment of the present application. The executor of the method can be a software module with computing and data processing capabilities, or a computing device integrated with similar software modules.

[0035] The method for enhancing the situation information of an intelligent agent based on knowledge reasoning may include the following steps: Step 202: Determine the initial situation information obtained by the target agent when perceiving the current confrontation environment.

[0036] In the present application scheme, the target intelligent agent can be an intelligent agent that plays a decision-making role in the confrontation environment. The target intelligent agent can obtain the initial situation information of all intelligent agents in the current confrontation environment. The initial situation information can reflect the changes of the opponent, our side, and the environment in the confrontation environment.

[0037] Step 204: Based on the first-order predicate logic, construct rule knowledge for each intelligent agent in the adversarial environment; wherein the rule knowledge constructed for each intelligent agent can formally express and reason about the intelligent agent and its capabilities and relationships.

[0038] It should be understood that first-order predicate logic includes multiple basic elements: individual constants, predicates, quantifiers, and logical connectives; among them, individual constants refer to the intelligent agents participating in the game confrontation in the confrontation environment; predicates are used to describe the properties and / or relationships of the intelligent agents; quantifiers include universal quantifiers and existential quantifiers, the universal quantifiers indicate that the predicate is applicable to all intelligent agents, and the existential quantifiers indicate that the predicate is applicable to at least one intelligent agent; logical connectives are used to combine atomic propositions to form more complex propositions.

[0039] Construct domain expert knowledge based on first-order predicate logic. First-order predicate logic is a formal system in mathematical logic, which is used to formally express and reason about individuals and their properties and relationships. It includes basic elements such as individual constants, predicates, quantifiers, logical connectives, etc. Among them, individual constants refer to specific individuals: such as "Socrates", "Earth", etc.; predicates are used to describe the properties or relationships of individuals. The predicate is usually followed by individual constants in parentheses. For example, "Socrates is a human" can be expressed as Human("Socrates"); quantifiers are divided into universal quantifiers and existential quantifiers. Universal quantifiers indicate that the predicate applies to all individuals, expressed as "for all" or "for each one", and the symbol is " "; existential quantifiers indicate that the predicate applies to at least one individual, and are expressed as "there is a certain" or "there is a certain" and are symbolized by " "; logical connectives are used to combine atomic propositions to form more complex propositions, including conjunctions ( , and), disjunction ( , or), not ( , not), implies ( , if ... then ...), equivalent ( , if and only if…), etc.

[0040] Based on first-order predicate logic, the logical reasoning process of experts in different fields can be represented so that it can be understood and executed by machines.

[0041] Optionally, in the present application scheme, different intelligent agents in the confrontation environment are divided into different role types according to their capabilities: air attack role, ground attack role, and escort role; among which, the main ability of the air attack role is to attack aerial targets; the main ability of the ground attack role is to attack ground targets; and the main ability of the escort role is to protect other roles from being attacked.

[0042] In the game scenario, if the agent is a ground attack character, then the agent has no air attack capability, and the corresponding first rule knowledge is constructed as follows: ; If the agent is an air attack character, then the agent has air attack capability, and the corresponding second rule knowledge is constructed as follows: ; If the agent has no air attack capability but enters the alert range of the opponent's air attack role, then the agent has an escort role, and the corresponding third rule knowledge is constructed as follows: ; If the agent has an escort role and it is not observed, it is inferred that the escort role is a stealth agent, and the corresponding fourth rule knowledge is constructed as follows: .

[0043] Step 206: Convert the initial situation information into a formal expression of first-order predicate logic to obtain situation information that is compatible with the form.

[0044] Considering that the situation information data directly observed by the target intelligent agent has different formats and may be unstructured or semi-structured data, it needs to be converted into a representation compatible with first-order predicate logic. It is mainly converted into individual constants or atomic propositions in first-order predicate logic.

[0045] Optionally, in the solution of the present application, the initial situation information is converted into a formal expression of first-order predicate logic to obtain situation information that is compatible in form, and specifically, the first agent and the second agent of the confrontation game contained in the initial situation information can be determined; based on the first-order predicate logic, the situation information that is compatible in form and includes the first agent and the second agent is determined; wherein the situation information that is compatible in form can include: First situation information: first agent For ground attack characters: ; Second situation information: Second agent For anti-air attack characters: ; Third situation information: First agent and the second agent It is an adversarial relationship: ; Fourth situation information: the first agent With the second agent Distance between: ; Fifth situation information: Second agent Warning range: .

[0046] Step 208: Based on the constructed rule knowledge, knowledge reasoning is performed on the form-compatible situation information to obtain enhanced situation information of the target intelligent agent; wherein the enhanced situation information includes hidden situation information obtained through knowledge reasoning.

[0047] In specific implementation, the situation information represented by step 106 can be used as input, and based on the rule knowledge constructed in step 104, new facts can be inferred to serve as enhanced situation information.

[0048] Optionally, based on the constructed rule knowledge, knowledge reasoning is performed on the form-compatible situation information to obtain enhanced situation information of the target intelligent agent, specifically including: Based on the first rule knowledge and the fifth situation information, the first reasoning information is obtained by reasoning: the first agent No anti-air attack capability: ; Based on the second rule knowledge and the second situation information, the second reasoning information is obtained by reasoning: the second agent Has anti-air attack capability: ; Based on the fourth situation information and the fifth situation information, the third reasoning information is obtained by reasoning: the first agent Entered the second agent Warning range: ; Based on the first reasoning information, the second reasoning information, the third situation information, and the third reasoning information, the fourth reasoning information is obtained by reasoning: the first agent Escort role exists : ; Escort role If it is not observed, then there is the fifth inference information: ; Based on the first situation information, the fourth reasoning information, and the fifth reasoning information, the sixth reasoning information is obtained: escort role For an invisible agent: ; The first reasoning information, the second reasoning information, the third reasoning information, the fourth reasoning information, the fifth reasoning information and the sixth reasoning information obtained by reasoning are determined as the enhanced situation information of the target intelligent agent.

[0049] Optionally, after obtaining the enhanced situation information of the target intelligent agent, the enhanced situation information may also be sent to the target intelligent agent so that the target intelligent agent uses the enhanced situation information to make a confrontation decision.

[0050] Compared with the traditional intelligent agent decision-making process, the technical solution proposed in this application is as follows: Figure 3As shown in the figure, a knowledge reasoning engine module is added. It takes situation information as input, obtains new facts through reasoning, and then adds them to the original situation information. The enhanced situation information is used as input and provided to the intelligent agent for decision-making. Among them, the knowledge reasoning engine mainly consists of three parts, namely rule knowledge construction, situation information formalization representation, and situation information reasoning.

[0051] Take a two-party game as an example. Assume that there are different game agents in the game, and different game agents are divided into different roles according to their abilities, including: ① air attack role, whose main ability is to attack air targets; ② ground attack role, whose main ability is to attack ground targets; ③ escort role, whose main ability is to protect other roles from being attacked. Assume that the purpose of the opponent's agent is to destroy our command server, and the purpose of our agent is to protect the command server from being destroyed by the opponent. What we need to do specifically is to observe the real-time situation and control our agent to make necessary action decisions, such as Figure 4 It should be understood that in this embodiment, the ground attack role is mainly described by taking a drone as an example.

[0052] Assume that the current situation information obtained by the game agent is as follows Figure 5 As shown in the figure, it is found that the opponent's ground attack character B (such as a drone) intends to attack our command server. It is 180km away from our air attack character F and has entered F's 200km warning range. If only based on the current situation information observed by the game agent, since the ground attack character has no air attack capability, the intuitive decision should be to send the air attack character F to attack.

[0053] However, since the opponent often hides the truth and shows false information in a confrontational environment, the observed situation information may not be comprehensive and accurate. Here, we use this application solution to construct some knowledge rules to enhance the situation information and assist our intelligent agent to make reasonable decisions. According to the above method, the specific steps are as follows.

[0054] 1. Rule knowledge construction First, construct rule knowledge as shown in formulas (1)-(4).

[0055] If the game agent type is a ground attack character, it has no air attack capability:

[0056] If the game agent type is an air attack character, it has air attack capabilities:

[0057] If the game agent has no anti-air attack capability, but enters the alert range of the opponent's anti-air attack character, there should be an escort character:

[0058] If there is an escort character and it is not observed, it is presumed to be an invisible agent:

[0059] 2. Formal representation of situation information Then you need to Figure 5 The situation information is formally expressed as shown in formulas (5)-(9).

[0060] Game agent B is a ground attack character:

[0061] Game agent F is an anti-air attack character:

[0062] Game agent B and game agent F are in an enemy relationship:

[0063] The distance between game agent B and game agent F is 180km:

[0064] The alert range of game agent F is 200km:

[0065] 3. Situation Information Reasoning Combining rules (1)-(4) and observed situation information (5)-(9), the reasoning process is as follows: Combining (1) and (5), we can infer that agent B has no anti-air attack capability:

[0066] Combining (2) and (6), we can infer that the game agent F has the ability to attack the air:

[0067] Combining (8) and (9), we can infer that the game agent B has entered the warning range of F:

[0068] Combining (10), (11), (7), and (12), we can infer that the game agent B has an escort role:

[0069] But since y is not observed, we have:

[0070] Combining (5), (13), and (14), we can infer that there is an invisible agent:

[0071] In this way, the actual situation information, i.e., the enhanced situation information, may be as follows: Figure 6 As shown in the figure, implicit information can be inferred based on knowledge, which enhances the agent's grasp of the situation. The agent can make full use of the enhanced situation information to make decisions, such as strengthening reconnaissance and air defense forces, while maintaining vigilance to avoid rash attacks based solely on observed information and causing losses.

[0072] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0073] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0074] It should be understood that in this application, the ground attack character B and the game agent B are both represented as specific roles of the first agent B in different scenes, and therefore, are represented by the same symbol B. Similarly, the air attack character F and the game agent F are both represented as specific roles of the second agent F in different scenes, and therefore, are represented by the same symbol F.

[0075] Figure 7 FIG. 1 shows a structural block diagram of an intelligent agent situation information enhancement system based on knowledge reasoning provided by an embodiment of the present application, such as Figure 7 As shown. The agent situation information enhancement system 700 based on knowledge reasoning of this embodiment may include a determination module 701, a construction module 702, a conversion module 703 and a reasoning module 704. Among them, the determination module 701 is used to determine the initial situation information obtained by the target agent when perceiving the current confrontation environment; the construction module 702 is used to construct rule knowledge for each agent in the confrontation environment based on first-order predicate logic; wherein the rule knowledge constructed for each agent can formally express and reason about the agent and its capabilities and relationships; the conversion module 703 is used to convert the initial situation information into a formal expression of first-order predicate logic to obtain formally compatible situation information; the reasoning module 704 is used to perform knowledge reasoning on the formally compatible situation information based on the constructed rule knowledge to obtain the enhanced situation information of the target agent; wherein the enhanced situation information contains hidden situation information obtained through knowledge reasoning.

[0076] It should be noted that part or all of the intelligent agent situation information enhancement system based on knowledge reasoning of this embodiment can be an application located in the local terminal, or it can also be a functional unit such as a plug-in or software development kit (SDK) set in the application located in the local terminal, or it can also be a processing engine located in a network side server, or it can also be a distributed system located on the network side.

[0077] It is understandable that the application may be a local program (nativeApp) installed on the local terminal, or may also be a webpage program (webApp) of a browser on the local terminal, which is not limited in this embodiment.

[0078] Optionally, in a possible implementation of this embodiment, it also includes: a sending module, used to send the enhanced situation information to the target intelligent agent, so that the target intelligent agent uses the enhanced situation information to make confrontation decisions.

[0079] Optionally, in a possible implementation of this embodiment, the first-order predicate logic includes multiple basic elements: individual constants, predicates, quantifiers, and logical connectives; wherein, individual constants refer to intelligent agents participating in game confrontation in an adversarial environment; predicates are used to describe the properties and / or relationships of intelligent agents; quantifiers include universal quantifiers and existential quantifiers, the universal quantifiers indicate that the predicate is applicable to all intelligent agents, and the existential quantifiers indicate that the predicate is applicable to at least one intelligent agent; logical connectives are used to combine atomic propositions to form more complex propositions.

[0080] Optionally, in a possible implementation of this embodiment, different intelligent agents in a confrontation environment are divided into different role types according to their capabilities: air attack role, ground attack role, and escort role; among which, the main ability of the air attack role is to attack aerial targets; the main ability of the ground attack role is to attack ground targets; and the main ability of the escort role is to protect other roles from being attacked.

[0081] Optionally, in a possible implementation of this embodiment, in a game confrontation scenario, when the construction module 702 constructs rule knowledge for each agent in the confrontation environment based on first-order predicate logic, it is specifically used to: If the agent is a ground attack character, then the agent has no air attack capability, and the corresponding first rule knowledge is constructed as follows: ; If the agent is an air attack character, then the agent has air attack capability, and the corresponding second rule knowledge is constructed as follows: ; If the agent has no air attack capability but enters the alert range of the opponent's air attack role, then the agent has an escort role, and the corresponding third rule knowledge is constructed as follows: ; If the agent has an escort role and it is not observed, it is inferred that the escort role is a stealth agent, and the corresponding fourth rule knowledge is constructed as follows: .

[0082] Optionally, in a possible implementation of this embodiment, when the conversion module 703 converts the initial situation information into a formal expression of first-order predicate logic to obtain the situation information with compatible form, it is specifically used to: Determine a first agent and a second agent of the confrontation game included in the initial situation information; Based on the first-order predicate logic, situation information including the first agent and the second agent that is compatible in form is determined; wherein the situation information that is compatible in form includes: First situation information: first agent For ground attack characters: ; Second situation information: Second agent For anti-air attack characters: ; Third situation information: First agent and the second agent It is an adversarial relationship: ; Fourth situation information: the first agent With the second agent Distance between: ; Fifth situation information: Second agent Warning range: .

[0083] Optionally, in a possible implementation of this embodiment, when the reasoning module 704 performs knowledge reasoning on the form-compatible situation information based on the constructed rule knowledge to obtain the enhanced situation information of the target agent, it is specifically used to: Based on the first rule knowledge and the fifth situation information, the first reasoning information is obtained by reasoning: the first agent No anti-air attack capability: ; Based on the second rule knowledge and the second situation information, the second reasoning information is obtained by reasoning: the second agent Has anti-air attack capability: ; Based on the fourth situation information and the fifth situation information, the third reasoning information is obtained by reasoning: the first agent Entered the second agent Warning range: ; Based on the first reasoning information, the second reasoning information, the third situation information, and the third reasoning information, the fourth reasoning information is obtained by reasoning: the first agent Escort role exists : ; Escort role If it is not observed, then there is the fifth inference information:

[0084] Based on the first situation information, the fourth reasoning information, and the fifth reasoning information, the sixth reasoning information is obtained by reasoning: escort role For an invisible agent: ; The first reasoning information, the second reasoning information, the third reasoning information, the fourth reasoning information, the fifth reasoning information and the sixth reasoning information obtained by reasoning are determined as the enhanced situation information of the target intelligent agent.

[0085] In this embodiment, the initial situation information obtained by the target intelligent agent when perceiving the current confrontation environment is determined; based on the first-order predicate logic, rule knowledge is constructed for each intelligent agent in the confrontation environment; the initial situation information is converted into a formal expression of the first-order predicate logic to obtain situation information that is compatible with the form; based on the constructed rule knowledge, the formally compatible situation information is subjected to knowledge reasoning to obtain enhanced situation information of the target intelligent agent; wherein the enhanced situation information includes hidden situation information obtained through knowledge reasoning. In this way, based on the objectively observed situation information, potential but directly observable information is inferred by means of the knowledge of domain experts, thereby supporting the analysis and decision-making of the intelligent agent with more comprehensive information.

[0086] An embodiment of the present application provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is loaded and executed by a processor to implement the method for enhancing the situation information of an intelligent agent based on knowledge reasoning as described above.

[0087] An embodiment of the present application provides an electronic device, which includes a processor and a memory, wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the above-mentioned method for enhancing the situation information of an intelligent agent based on knowledge reasoning.

[0088] In the technical solution of this application, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the relevant laws and regulations and do not violate public order and good morals.

[0089] Figure 8 A schematic block diagram of an example computing device 800 that can be used to implement an embodiment of the present application is shown. The computing device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The computing device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0090] like Figure 8 As shown, the computing device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 to a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the computing device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0091] A number of components in the computing device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the computing device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0092] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as a method for enhancing the situational information of an intelligent agent based on knowledge reasoning. For example, in some embodiments, the method for enhancing the situational information of an intelligent agent based on knowledge reasoning may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the computing device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the method for enhancing the situational information of an intelligent agent based on knowledge reasoning described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to execute the agent situation information enhancement method based on knowledge reasoning in any other appropriate manner (eg, by means of firmware).

[0093] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0094] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, implements the functions / operations specified in the flow chart and / or block diagram. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0095] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0096] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0097] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0098] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0099] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in this application can be achieved, and this document is not limited here.

[0100] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for enhancing agent situation information based on knowledge reasoning, characterized in that: include: Determine the initial situation information obtained by the target agent when perceiving the current confrontation environment; Based on the first-order predicate logic, rule knowledge is constructed for each intelligent agent in the adversarial environment; wherein the rule knowledge constructed for each intelligent agent can formally express and reason about the intelligent agent and its capabilities and relationships; Converting the initial situation information into a formal expression of first-order predicate logic to obtain situation information that is compatible with the form; Based on the constructed rule knowledge, knowledge reasoning is performed on the form-compatible situation information to obtain enhanced situation information of the target intelligent agent; wherein the enhanced situation information includes hidden situation information obtained through knowledge reasoning.

2. The method according to claim 1, characterized in that The method further comprises: The enhanced situation information is sent to the target intelligent agent so that the target intelligent agent uses the enhanced situation information to make confrontation decisions.

3. The method according to claim 1 or 2, characterized in that First-order predicate logic includes several basic elements: individual constants, predicates, quantifiers, and logical connectives; Among them, individual constants refer to the intelligent agents participating in the game confrontation in the confrontation environment; predicates are used to describe the properties and / or relationships of intelligent agents; quantifiers include universal quantifiers and existential quantifiers, the universal quantifiers indicate that the predicate is applicable to all intelligent agents, and the existential quantifiers indicate that the predicate is applicable to at least one intelligent agent; logical connectives are used to combine atomic propositions to form more complex propositions.

4. The method according to claim 1 or 2, characterized in that: Different agents in the confrontation environment are divided into different role types according to their capabilities: air attack role, ground attack role, escort role; Among them, the main ability of the air attack type character is to attack aerial targets; the main ability of the ground attack type character is to attack ground targets; the main ability of the escort character is to protect other characters from being attacked.

5. The method according to claim 4, characterized in that In the game confrontation scenario, based on the first-order predicate logic, rule knowledge is constructed for each agent in the confrontation environment, including: If the agent is a ground attack character, then the agent has no air attack capability, and the corresponding first rule knowledge is constructed as follows: ; If the agent is an air attack character, then the agent has air attack capability, and the corresponding second rule knowledge is constructed as follows: ; If the agent has no air attack capability but enters the alert range of the opponent's air attack role, then the agent has an escort role, and the corresponding third rule knowledge is constructed as follows: ； If the agent has an escort role and it is not observed, it is inferred that the escort role is a stealth agent, and the corresponding fourth rule knowledge is constructed as follows: 。 6. The method according to claim 5, characterized in that The initial situation information is converted into a formal expression of first-order predicate logic to obtain situation information that is compatible with the form, specifically including: Determine a first agent and a second agent of the confrontation game included in the initial situation information; Based on the first-order predicate logic, situation information including the first agent and the second agent that is compatible in form is determined; wherein the situation information that is compatible in form includes: First situation information: first agent For ground attack characters: ; Second situation information: Second agent For anti-air attack characters: ; Third situation information: First agent and the second agent It is an adversarial relationship: ; Fourth situation information: the first agent With the second agent Distance between: ; Fifth situation information: Second agent Warning range: .

7. The method according to claim 6, characterized in that Based on the constructed rule knowledge, knowledge reasoning is performed on the situation information compatible with the form to obtain enhanced situation information of the target intelligent agent, specifically including: Based on the first rule knowledge and the fifth situation information, the first reasoning information is obtained by reasoning: the first agent No anti-air attack capability: ; Based on the second rule knowledge and the second situation information, the second reasoning information is obtained by reasoning: the second agent Has anti-air attack capability: ; Based on the fourth situation information and the fifth situation information, the third reasoning information is obtained by reasoning: the first agent Entered the second agent F's warning range: ; Based on the first reasoning information, the second reasoning information, the third situation information, and the third reasoning information, the fourth reasoning information is obtained by reasoning: the first agent Escort role exists : ; Escort role If it is not observed, then there is the fifth inference information: ; Based on the first situation information, the fourth reasoning information, and the fifth reasoning information, the sixth reasoning information is obtained by reasoning: escort role For an invisible agent: ; The first reasoning information, the second reasoning information, the third reasoning information, the fourth reasoning information, the fifth reasoning information and the sixth reasoning information obtained by reasoning are determined as the enhanced situation information of the target intelligent agent.

8. A computing device, characterized in that: The invention comprises a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the method described in any one of claims 1 to 7 when executing the computer program.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method described in any one of claims 1 to 7 is implemented.

10. An intelligent agent situation information enhancement system based on knowledge reasoning, characterized in that: include: A determination module is used to determine the initial situation information obtained by the target intelligent agent when perceiving the current confrontation environment; A construction module is used to construct rule knowledge for each intelligent agent in the adversarial environment based on first-order predicate logic; wherein the rule knowledge constructed for each intelligent agent can formally express and reason about the intelligent agent and its capabilities and relationships; A conversion module, used for converting the initial situation information into a formal expression of first-order predicate logic to obtain situation information with compatible form; The reasoning module is used to perform knowledge reasoning on the form-compatible situation information based on the constructed rule knowledge to obtain enhanced situation information of the target intelligent agent; wherein the enhanced situation information includes hidden situation information obtained through knowledge reasoning.

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