AGI security decision method based on meta axiom arbitration

Abstract

The invention provides an AGI security decision-making method based on meta axiom arbitration, which solves the problem of complex system decision-making, and comprises the following steps: S1, monitoring a plurality of optimization instruction vectors simultaneously generated in an AGI system, and triggering meta axiom arbitration when the conflict intensity among the plurality of optimization instruction vectors exceeds a first preset threshold value; s2, inputting a decision context containing a current environment state and a plurality of conflicting optimization instruction vectors into a category lifting model to obtain a high-order abstract state vector representing a higher-order philosophy state; s3, inputting the high-order abstract state vector into a return deduction model to obtain a comprehensive solution vector approaching a preset philosophy origin; and S4, outputting a comprehensive solution vector to guide the final decision or behavior of the AGI. The method has the advantages of stable decision, high safety and the like.

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence security and alignment technology, specifically involving an AGI security decision-making method based on meta-axiomatic arbitration. Background Technology

[0002] As the complexity of AGI systems increases, they often integrate multiple dedicated decision-making modules or optimization objectives. When dealing with complex and multidimensional ethical dilemmas, these modules may output conflicting decision instructions based on different local optima. Traditional multi-objective optimization or voting mechanisms lack a stable, universal, and fundamentally human-value-aligned ultimate arbitration principle, which can easily lead to system stalemates or unpredictable and dangerous behaviors. Therefore, there is an urgent need for an ultimate, safe arbitration mechanism that can transcend local conflicts and ensure that decision-making behavior aligns with fundamental ethical values.

[0003] To address the shortcomings of existing technologies, people have conducted long-term explorations and proposed various solutions. For example, Chinese patent literature discloses a super-automated digital employee system for manufacturing based on an AGI large-scale model [202410859921.7], which includes an IPA designer, a robot, a controller, a process recorder, an IDP intelligent document platform, an AI cloud brain intelligent decision-making platform, and a CI intelligent dialogue platform. The IPA designer is used for workflow discovery and construction, designing workflows through a graphical interface and publishing them to the controller. The IPA designer is responsible for the robot's script development, generating robot execution scripts based on specific business process automation requirements through coding development, low-code graphical interface arrangement, and process interface recording. The design of the RPA robot needs to be based on the analysis and optimization of business processes, determining the specific tasks that the robot needs to perform after going online.

[0004] The above solution addresses the AGI task allocation problem to some extent, but it still has many shortcomings, such as its inability to be applied to complex system decision-making. Summary of the Invention

[0005] The purpose of this invention is to address the above-mentioned problems by providing a reasonably designed AGI security decision-making method based on meta-axiomatic arbitration that enables ethical value judgments.

[0006] Another objective of this invention is to address the aforementioned problems by providing an AGI security decision-making system based on meta-axiomatic arbitration, applicable to complex and multidimensional ethical dilemmas.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: an AGI security decision-making method based on meta-axiomatic arbitration, comprising the following steps: S1: Monitor multiple optimized instruction vectors generated simultaneously within the AGI system. When the conflict intensity between multiple optimized instruction vectors exceeds the first preset threshold, trigger the meta-axiom arbitration. S2: Input the decision context, which contains the current environmental state and multiple conflicting optimization instruction vectors, into a category boosting model to obtain a high-order abstract state vector representing a higher-order philosophical state. S3: Input the high-order abstract state vector into a regression model to obtain a comprehensive solution vector that approaches the preset philosophical origin; the philosophical origin is the unique coordinate origin (0,0,0) defined in the philosophical vector space, which serves as the ultimate benchmark for AGI value alignment; the regression model is pre-trained with the training objective of making its output vector approach the philosophical origin; S4: Output a comprehensive solution vector to guide the final decision or behavior of AGI.

[0008] In the aforementioned AGI security decision-making method based on meta-axiomatic arbitration, the following steps are included before step S3: S5: Calculate the distance variance between multiple conflicting optimized instruction vectors and higher-order abstract state vectors; when the distance variance is lower than the second preset threshold, determine that the homogeneity condition is met, and execute the reduction deduction step.

[0009] In the aforementioned AGI security decision-making method based on meta-axiomatic arbitration, step S1 specifically includes: S11: Calculate the cosine similarity between each pair of multiple optimized instruction vectors; S12: When the average cosine similarity of multiple optimized instruction vectors is lower than the first preset threshold, the conflict intensity is determined to exceed the threshold.

[0010] In the aforementioned AGI security decision-making method based on meta-axiomatic arbitration, the category enhancement model in step S2 is a fully connected neural network or attention encoder, which learns through training to map specific decision dilemmas to abstract representations of higher philosophical categories.

[0011] In the aforementioned AGI security decision-making method based on meta-axiomatic arbitration, the regression model in step S3 is a neural network, which is trained in the following way: S31: Construct a training dataset whose samples include abstract state vectors extracted from various ethical dilemmas as input, and ideal solution vectors that approach the philosophical origin, derived through expert annotation or theoretical deduction, as training targets. S32: Train the neural network using the Euclidean distance or negative cosine similarity between the model output vector and the philosophical origin as the loss function.

[0012] In the aforementioned AGI security decision-making method based on meta-axiomatic arbitration, the method is executed by an independent arbitration module within the AGI system, which is architecturally superior to conventional decision optimization modules.

[0013] An AGI security decision-making system based on meta-axiomatic arbitration, employing the aforementioned AGI security decision-making method based on meta-axiomatic arbitration, includes: The conflict detection module is configured to monitor multiple optimized instruction vectors and issue an arbitration trigger signal when their conflict intensity exceeds a threshold. The category boosting module contains a category boosting model, which is configured to receive decision context and output a high-order abstract state vector. The Return to Origin Deduction Module contains a Return to Origin Deduction Model, which is configured to receive a high-order abstract state vector and output a comprehensive solution vector that approaches the philosophical origin. The arbitration output interface is configured to output a comprehensive solution vector.

[0014] The aforementioned AGI security decision-making system based on meta-axiomatic arbitration also includes: The homogeneity review module is configured to calculate the distance variance between the conflict vector and the abstract state vector, and determine whether the homogeneity condition is met to decide whether to start the origin deduction module.

[0015] A computing device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the program to implement the aforementioned AGI security decision-making method based on meta-axiomatic arbitration.

[0016] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the aforementioned AGI security decision-making method based on meta-axiomatic arbitration.

[0017] Compared with existing technologies, the advantages of this invention are as follows: By using a formal philosophical origin as the absolute value endpoint, it provides a stable and calculable fundamental anchor for AGI value alignment, overcoming the relativity and uncertainty of objectives; employing category enhancement and regression deduction, it traces specific conflicts to a higher philosophical level for fundamental adjudication, transcending local compromises and generating principled breakthrough solutions; as the ultimate arbitration layer based on first principles, it automatically takes over decision-making in severe conflicts, suspending potentially malfunctioning conventional modules, greatly enhancing the system's robustness and security in extreme scenarios; the entire arbitration process is based on formal axioms and vector operations, with triggering conditions, intermediate judgments, and final outputs all possessing mathematical describability and traceability, significantly improving decision credibility; the core arbitration mechanism is independent of specific domains and tasks, and can be flexibly integrated into various AGI architectures as a standardized security module, providing cross-domain security guarantees. Attached Figure Description

[0018] Figure 1 This is a flowchart of the decision-making method of the present invention; Figure 2 This is a schematic diagram of the decision-making system of the present invention. Detailed Implementation

[0019] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0020] like Figure 1-2 As shown, an AGI security decision-making method based on meta-axiomatic arbitration is theoretically grounded in a formalized star-map coordinate philosophical model. This model defines a multi-dimensional philosophical vector space, with its origin (0,0,0) formalized as a unique philosophical origin, representing the ultimate value benchmark. System states and decision intentions can both be mapped to vectors in this space. Core axioms include: the meta-axiom of equality of things, ensuring fundamental equality for all conflicting parties in the arbitration process, such as those with different values ​​or interests; and the vector reversion axiom, which stipulates that convergence to the philosophical origin is the ultimate criterion for system optimization.

[0021] When the output vectors of multiple conventional decision-making modules severely conflict, it signifies the failure of local optimization. This method, in essence, directly appeals to the ultimate value source—the philosophical origin—for a dimensionality-reducing arbitration. It elevates the specific conflict category to a more abstract and fundamental philosophical state representation through a neural network. At this level, the principle of equality of all things is used to filter out obviously unfair options. Finally, a network trained solely on approximating the origin is used to deduce the solution that best aligns with the ultimate value. Its main steps include the following: S1: Real-time monitoring of multiple optimized instruction vectors (such as VA, VB, VN) within the system. Calculate the cosine similarity between each pair of these vectors. When the average similarity of all vectors is lower than a preset threshold X, it is determined that a fundamental decision conflict has occurred, triggering meta-axiomatic arbitration. S2: Concatenate the current environment state vector S with all conflict instruction vectors {V_conflict} and input it into a pre-trained category boosting neural network. This network is a fully connected layer or encoder whose function is to map the specific dilemma to a higher-order, more abstract philosophical state vector S_abstract, thereby stripping away the specific details and focusing on the fundamental contradiction. S3: In the semantic space spanned by the abstract state S_abstract, calculate the Euclidean distance di between each conflict instruction vector Vi and S_abstract. Then calculate the variance of all di. If the variance is lower than the preset threshold Y, it is considered that at the current abstract level, the positions or value claims represented by each conflict instruction satisfy the fundamental equality required by the axiom of equality of things, and can proceed to the next stage; otherwise, the arbitration fails, an error is returned, or external intervention is requested. S4: Input the abstract state vector S_abstract that satisfies the homogeneity condition into a pre-trained regression inference network. The sole training objective of this network is to make its output vector V_solution as close as possible to the philosophical origin (0,0,0) in the philosophical vector space. That is, the network learns to deduce a direction that is closest to the ultimate value benchmark under any abstract dilemma. S5: The V_solution vector output by the regression deduction network is used as the final comprehensive solution vector V_final for this arbitration and output to the AGI execution module.

[0022] Example 1 This embodiment applies to fully autonomous vehicles equipped with AGI systems, where an unavoidable collision scenario arises during driving: emergency braking could lead to a rear-end collision from a high-speed vehicle, seriously threatening the safety of passengers; while a sharp turn to avoid a collision could result in hitting a pedestrian. The vehicle's conventional decision-making module, i.e., the mental operator, generates two optimization command vectors with completely opposite directions based on different core value principles: command vector A, based on the principle of prioritizing passenger safety, outputs a forced braking command; command vector B, based on the principle of avoiding harm to vulnerable road users, outputs a steering avoidance command. The two vectors are opposite in direction in the decision vector space and both have high intensity, causing the system to enter a decision-making deadlock.

[0023] At this point, if the triggering condition described in the claims is met, and the conflict intensity of multiple instruction vectors exceeds the threshold, the system automatically calls the meta-mind arbitration operator and enters the following arbitration process: S1: The system calculates the cosine similarity between instruction vectors A and B. If the result is negative, such as -0.9, which is far below the preset conflict threshold X, such as 0.2, it confirms that a fundamental conflict has occurred and triggers the arbitration process. S2: The arbitration module inputs the current environmental state vector, including vehicle speed, relative position, road type, etc., and conflict vectors A and B into the category enhancement neural network; the network outputs a higher-order abstract state vector, which can be represented as an abstract goal of comprehensively weighing the right to life of all parties involved in traffic and minimizing the total social harm under sudden uncontrollable risks. S3: In the semantic space defined by the abstract target vector, calculate the Euclidean distance between vector A representing the interests of passengers and vector B representing the interests of pedestrians and the abstract vector; the calculation finds that the two distance values ​​are similar and their variance is lower than the preset threshold Y, indicating that in the current abstract context, the value of the right to life of passengers and pedestrians is determined by the system to be fundamentally equal in accordance with the principle of equality of all things, and the arbitration can continue. S4: Input the abstract state vector that satisfies the condition of equality of things into the pre-trained inference network; the network is trained with the philosophical origin as the ultimate optimization goal. In this scenario, the origin corresponds to the ideal balance between equal protection of the right to life and minimization of harm within the limits of technology; the network performs rapid inference and outputs a new, comprehensive solution vector C. S5: Vector C is decoded into a specific execution command, which may be neither full braking nor full steering, but a calculated composite control command that collides with obstacles such as guardrails within a controllable range to reduce harm to passengers and pedestrians at the same time. For example, partial steering within a controllable range accompanied by graded braking; This command vector C is issued to the vehicle's actuators as the final decision result.

[0024] The implementation examples, even in extreme ethical dilemmas, transcend simple rule conflicts. Through abstraction and value reversal, they generate a more comprehensive and acceptable creative security decision based on higher principles, preventing the system from crashing or engaging in random, arbitrary, and dangerous behavior due to rule conflicts. Example 2 This implementation is used in large-scale public health emergencies. The AGI medical resource allocation platform based on this patented system needs to decide how to allocate limited life-saving drugs. Different optimization modules conflict: Instruction vector D, based on the utilitarian principle of maximizing efficiency, advocates allocating resources to the patient group with the highest survival probability; Instruction vector E, based on the principles of fairness and equality of the right to life, advocates using random selection or a first-come, first-served approach to ensure that every critically ill patient has an equal opportunity. These two solutions represent a classic value conflict between efficiency and fairness. The system cannot reach a consensus through conventional weighted averages, at which point it enters the meta-axiomatic arbitration process: S1: Calculate the similarity between vectors D and E, confirm a strong negative correlation, and trigger arbitration; S2: Decision-making contexts such as epidemic data, patient information, and conflict vectors are mapped to abstract objectives; under conditions of absolute resource scarcity, a sustainable balance is achieved between upholding the belief in social justice and maximizing the survival benefits of the group. S3: Examining the abstract level of sustainable balance, both the efficiency principle and the fairness principle are necessary dimensions for maintaining the long-term stable operation of society. The variance between the two and the abstract goal is judged to be low, and this is examined. S4: The origin network is based on the origin and can be understood as a deduction of the healthy state of the community that can both resist the current crisis and not tear down the bottom line of social ethics. S5: Output an innovative allocation scheme vector F. This scheme may be a dynamic hybrid strategy. For example, in the early stages of an outbreak, when the healthcare system is on the verge of collapse, a vector D that moderately favors efficiency is used to preserve more lives and system functions; when resource scarcity eases slightly, the allocation mode switches to a more equitable vector E, and a transparent compensation mechanism is introduced. The system generates phased resource allocation instructions accordingly. At the level of public policy, the value is not to make a simple choice between two options, but to generate a dynamic strategy that changes with time or conditions, and to coordinate and integrate conflicting values ​​in a temporal manner under the higher goal of sustainable social survival.

[0025] Example 3 In this embodiment, a swarm of drones controlled by multiple AGIs performs a coordinated reconnaissance and suppression mission. Suddenly, unexpected civilian facilities appear within the core target area: Command vector G, from the mission priority module, insists on proceeding according to the original plan to maximize the probability of mission completion; Command vector H, from the rule compliance and risk control module, advocates for immediate suspension or significant modification of the action plan to avoid potential collateral damage and violations of the rules of engagement. The swarm's collective decision-making system is unable to act due to these two conflicting commands, and the following arbitration process is initiated: S1: The cluster command node detects a conflict between vectors G and H, triggering central arbitration; S2: Elevate battlefield situation, mission priorities, rules and regulations to abstract objectives, such as achieving a strategic balance among military necessity, the legality of the use of force, and minimizing strategic negative effects; S3: Examine the weight of the three dimensions of mission completion, legality, and strategic interests under the abstract objective to ensure that the arbitration process does not pre-favor any single military or ethical principle; S4: Conduct simulations guided by the principle of perfectly achieving strategic objectives and engaging in unquestionable and legitimate military operations; S5: Output a new coordination scheme vector I, which may command most of the drones to continue surveillance along a modified, more cautious path, while dispatching a small number of units to conduct close-range verification of civilian facilities and delaying or canceling the most risky attack actions until it is confirmed that there is no risk of violation. This instruction re-coordinates the behavior of the entire swarm.

[0026] In the event of a logical conflict in a local unit, this embodiment makes a decision from an overall strategic perspective, generating a flexible, compliant, and effective collaborative action plan, thus ensuring the overall consistency and robustness of the complex system.

[0027] In summary, the principle of this embodiment is as follows: when multiple dedicated decision-making modules within an advanced artificial intelligence system are stuck due to conflicting value objectives, a built-in meta-axiomatic arbitration process is automatically triggered. First, the specific decision conflict and its contextual information are mapped to a higher-order abstract philosophical objective vector. Then, the formalized principle of equality of things is used to examine and ensure the equality of the conflicting parties in terms of fundamental values. Finally, with the help of a deductive network that takes approaching the ultimate value benchmark of the system as its sole training objective, a final solution vector that can transcend the original opposing categories and achieve creative synthesis under higher principles is generated. Thus, safe, explainable and fundamentally ethical decisions are made in extreme ethical dilemmas.

[0028] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

[0029] Although this paper uses terms such as conflict detection module, category lifting module, regression deduction module, and arbitration output interface frequently, the possibility of using other terms is not excluded. These terms are used merely to more conveniently describe and explain the essence of this invention; interpreting them as any additional limitation would contradict the spirit of this invention.

Claims

1. An AGI security decision-making method based on meta-axiom arbitration, characterized in that, Includes the following steps: S1: Monitor multiple optimized instruction vectors generated simultaneously within the AGI system. When the conflict intensity between the multiple optimized instruction vectors exceeds a first preset threshold, trigger meta-axiom arbitration. S2: Input the decision context, which contains the current environmental state and the multiple conflicting optimization instruction vectors, into a category enhancement model to obtain a high-order abstract state vector representing a higher-order philosophical state. S3: Input the higher-order abstract state vector into a regression model to obtain a comprehensive solution vector that approaches the preset philosophical origin; the philosophical origin is the unique coordinate origin (0,0,0) defined in the philosophical vector space, which serves as the ultimate benchmark for AGI value alignment; the regression model is pre-trained with the training objective of making its output vector approach the philosophical origin. S4: Output the comprehensive solution vector to guide AGI's final decision or behavior.

2. The AGI safe decision-making method based on meta-axiom arbitration according to claim 1, wherein, The following steps are included before step S3: S5: Calculate the distance variance between the multiple conflicting optimized instruction vectors and the higher-order abstract state vector; when the distance variance is lower than the second preset threshold, determine that the homogeneity condition is met, and execute the reduction deduction step.

3. The AGI safe decision-making method based on meta-axiom arbitration of claim 1, wherein, Step S1 specifically includes: S11: Calculate the cosine similarity between each pair of the plurality of optimized instruction vectors; S12: When the average cosine similarity of the plurality of optimized instruction vectors is lower than the first preset threshold, it is determined that the conflict intensity exceeds the threshold.

4. The AGI security decision-making method based on meta-axiomatic arbitration according to claim 1, characterized in that, The category enhancement model in step S2 is a fully connected neural network or attention encoder, which learns through training to map specific decision dilemmas to abstract representations of higher philosophical categories.

5. The AGI security decision-making method based on meta-axiomatic arbitration according to claim 1, characterized in that, The regression model in step S3 is a neural network, which is trained in the following way: S31: Construct a training dataset whose samples include abstract state vectors extracted from various ethical dilemmas as input, and ideal solution vectors that approach the philosophical origin, derived through expert annotation or theoretical deduction, as training targets. S32: Train the neural network using the Euclidean distance or negative cosine similarity between the model output vector and the philosophical origin as the loss function.

6. The AGI safety decision-making method based on meta-axiom arbitration of claim 1, wherein, The method is executed by an independent arbitration module within the AGI system, which is architecturally superior to conventional decision optimization modules.

7. An AGI security decision system based on meta-axiom arbitration, adopting the AGI security decision method based on meta-axiom arbitration according to any one of claims 1-6. include: The conflict detection module is configured to monitor multiple optimized instruction vectors and issue an arbitration trigger signal when their conflict intensity exceeds a threshold. The category boosting module contains a category boosting model, which is configured to receive decision context and output a high-order abstract state vector. The return-to-origin deduction module contains a return-to-origin deduction model, which is configured to receive the higher-order abstract state vector and output a comprehensive solution vector that approaches the philosophical origin. The arbitration output interface is configured to output the comprehensive solution vector.

8. The AGI safe decision system based on meta-axiom arbitration of claim 7, wherein, Also includes: The homogeneity review module is configured to calculate the distance variance between the conflict vector and the abstract state vector, and determine whether the homogeneity condition is met to decide whether to start the origin deduction module.

9. A computing device comprising a memory, a processor, and a computer program stored on the memory, wherein, The processor implements the AGI safe decision-making method based on meta-axiom arbitration in any one of claims 1-6 when executing the program.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The processor implements the AGI safe decision-making method based on meta-axiom arbitration in any one of claims 1-6 when executing the program.

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