Military AI decision-aided man-machine interaction and supervision system

Abstract

According to the military AI decision-aided man-machine interaction and supervision system provided by the invention, a DIKWP structure reasoning link and personality factor influence of an AI decision are presented in a visual mode through an interpretable interaction interface module, so that decision logic transparency is realized; the parameter adjustable module supports a commander to adjust an intention parameter, a risk coefficient and an ethical threshold in real time, and drives the AI to dynamically generate a new scheme; the output behavior filtering module is internally provided with an ethical rule base and a risk assessment model, and carries out early warning, digestion or shielding processing on candidate schemes to ensure that military ethical requirements are met; the multi-mode interaction module integrates voice, graph and natural language interaction modes and adapts to a complex battlefield environment; the modular interface unit realizes seamless joint with military AI platforms such as strategic simulation, personality modeling and the like. According to the system, AI high-speed reasoning and command experience are combined through a man-machine cooperation mechanism, on the premise that decision controllability is guaranteed, combat scheme making efficiency and reliability are remarkably improved, and meanwhile credibility and safety of military AI decisions are enhanced through ethical constraint and strategy adjustment.

Description

Technical Field

[0001] This invention belongs to the fields of artificial intelligence, human-computer interaction and military command and decision-making technology, and in particular relates to a human-computer interaction and supervision system for military AI decision-making assistance, which is used to assist commanders in making decisions in military scenarios and provide support for interpretability, controllability and ethical supervision. Background Technology

[0002] With the rapid application of artificial intelligence (AI) technology in the military field, AI assistance is increasingly being introduced into command and decision-making processes. Existing military AI decision support systems have played a role in improving decision-making speed and processing complex information, but they also have many shortcomings:

[0003] First, most current AI decision support systems operate in a "black box" mode, lacking interpretability. Commanders typically only receive decision suggestions from the AI ​​but struggle to understand its internal reasoning and rationale. This lack of transparency can lead to commanders distrusting AI outputs, limiting its application in critical military decision-making.

[0004] Secondly, existing systems often lack the ability for human commanders to adjust AI decision-making parameters in real time. Once an AI system starts operating, its target parameters, risk preferences, etc., are usually preset and difficult to adjust dynamically, failing to meet the needs of rapidly changing battlefield environments and real-time changes in command intentions.

[0005] Furthermore, in terms of ethics and safety, traditional AI decision-making systems lack effective oversight mechanisms. If the AI ​​proposes actions that violate military regulations, ethical principles, or pose high risks, the system's lack of proactive warning and filtering capabilities could lead to serious consequences. Regarding human-computer interaction, most current military AI decision-making systems offer only a single interaction method (e.g., limited to text command input or fixed interface operation), which may reduce the system's usability and robustness in complex battlefield environments.

[0006] Finally, existing military AI decision support tools are not compatible with each other and are difficult to seamlessly integrate with existing command information systems, strategic simulation platforms, or personality modeling systems, which limits the comprehensive application of AI-assisted decision-making functions.

[0007] Given the above shortcomings, there is an urgent need for a new military AI decision support system that can make the AI ​​decision-making process transparent and explainable, human-machine collaboration controllable and adjustable, and subject to strict ethical and safety supervision, while also possessing a good interactive experience and system integration capabilities, so as to ensure that AI can better serve military command and decision-making. Summary of the Invention

[0008] Purpose of the Invention: The main objective of this invention is to overcome the shortcomings of existing technologies and provide a human-computer interaction and supervision system for military AI decision-making assistance. This system emphasizes human-computer collaboration, enabling AI to provide interpretable internal logic, real-time strategic adjustments that can be intervened in by commanders, and strict behavioral output supervision when assisting military command decision-making, thereby improving the reliability, transparency, and ethical compliance of decision-making.

[0009] The technical problem this invention aims to solve is: how to achieve transparent presentation of AI decision-making logic, manually adjustable strategy parameters, and ethical risk management of AI behavior output in military AI decision support processes, while providing diverse interaction methods to adapt to complex battlefield environments. This invention effectively solves the above problems by introducing the DIKWP semantic model (Data-Information-Knowledge-Wisdom-Purpose) as its core framework and designing a modular human-computer interaction and supervision mechanism.

[0010] Technical Solution: To achieve the above objectives, this invention provides a human-computer interaction and supervision system for military AI decision-making assistance, which mainly includes the following innovative modules:

[0011] 1. Explainable Interactive Interface Module: Provides a graphical and visual human-computer interaction interface to demonstrate the reasoning chain, key decision nodes, and the impact of personality factors on the decision-making path in the AI ​​decision-making process. The interface presents the AI's internal reasoning process based on the DIKWP semantic structure. For example, it displays the complete chain of AI from strategic intelligence to specific intent, then to knowledge reasoning, and finally to the proposal of action plans, in the form of a W→P→K→I (Intelligence Layer → Intent Layer → Knowledge Layer → Reasoning / Execution Layer) path diagram. Through this interface, commanders can intuitively understand the logical basis and details of each step behind AI decision-making, and clearly grasp which factors (such as data intelligence, knowledge rules, wisdom experience, and mission intent) are considered in the decision-making process. The interface also highlights how preset personality factors (such as command style, risk preference, etc.) drive or influence key nodes on the decision-making path, thereby assisting commanders in understanding the AI's behavioral logic and judging its rationality.

[0012] 2. Adjustable Parameter Module: This module provides commanders with the ability to intervene and adjust AI decision-making parameters in real time. Commanders can dynamically adjust key parameters in the AI ​​strategy, such as intent parameters (P-layer, e.g., mission objective weights, constraints), risk coefficients (e.g., the balance between offensiveness and safety), and ethical thresholds (e.g., tolerance for civilian casualties, rule-of-combat compliance levels). Each time the commander adjusts the parameters, the system regenerates a new, adapted solution based on the DIKWP structure. This involves transmitting the updated intent to the intelligence and knowledge layers along the P→W→K path, guiding the AI ​​engine to reassess data and information and generate new decision recommendations. This module ensures that the AI's decisions reflect the commander's latest intents and preferences in real time, giving humans direct control over the AI ​​strategy and preventing the AI ​​from deviating from command intent in unsupervised situations.

[0013] 3. Output Behavior Filtering Mechanism Module: This module incorporates a scalable ethical rule base and risk assessment model to automatically review and filter AI-generated candidate decision-making schemes. Specifically, when the AI ​​presents one or more alternative action plans, the mechanism compares and analyzes these plans against military regulations, rules of engagement, and pre-defined ethical standards. For actions that may violate ethical principles or pose a high risk, the system will promptly issue warnings to the commander. Clearly unacceptable plans will be eliminated or blocked to prevent their adoption. For example, if an AI plan could cause collateral civilian harm exceeding a certain threshold, the system interface will display a prominent warning. Suggestions that do not conform to superior instructions or violate international law will be directly filtered out. This output filtering mechanism essentially adds a "safety valve" between the AI's decision output and its actual execution, ensuring that the AI's final output behavior remains within the commander's acceptable and ethical range, preventing the AI ​​from providing uncontrolled or unethical solutions.

[0014] 4. Multimodal Interaction Channel Module: Supports diverse human-computer interaction input methods, enhancing the system's usability and robustness in various combat environments. This module allows commanders to interact with the AI ​​system through voice commands, graphical strategy maps, and natural language dialogue. For example, commanders can directly issue combat orders or inquire about the AI's decision-making basis via voice, with the system's speech recognition unit converting the instructions into text semantic input. Commanders can also graphically mark targets and adjust deployments on electronic sand tables or battlefield maps, with the system instantly acquiring graphical information. Furthermore, commanders can converse with the system using natural language, such as asking, "What are the risks of this plan if enemy reinforcements arrive?" The multimodal interaction channel ensures that commanders can easily operate and obtain AI-assisted decision-making information in noisy battlefields, emergency situations, or special environments, improving the efficiency and accuracy of human-computer collaboration.

[0015] 5. Modular Integration Interface Unit: This unit provides standardized interfaces and modular integration methods, enabling the system to operate collaboratively with existing military AI platforms. It adheres to unified communication protocols and data formats, seamlessly integrating with existing platforms such as strategic and tactical simulation systems, intelligent command information systems, and personality modeling systems. Through modular design, the system can be integrated as a plug-in or add-on module into the existing command and control center software architecture, utilizing existing data sources and AI capabilities, while also feeding back output results to the command information system. The interface unit ensures consistency and stability in the interaction between modules and external systems, reduces deployment difficulty, and provides excellent scalability. When introducing this system onto existing equipment, command units can achieve human-computer interaction and supervision functions for AI decision-making assistance without significant modifications to the original system, thereby protecting existing investments and accelerating application deployment.

[0016] In summary, this invention, through the synergistic effect of the aforementioned modules, constructs a novel military AI decision-making assistance human-computer interaction and supervision system. Compared with existing technologies, this invention has the following beneficial effects:

[0017] The decision-making process is transparent and explainable: By displaying the AI ​​reasoning chain and decision-making basis through a visual interface, commanders can clearly understand the process and reasons for AI decision-making, which greatly improves the transparency and credibility of AI-assisted decision-making.

[0018] High controllability of human command: It provides an adjustable parameter mechanism, allowing commanders to intervene in AI decision parameters in real time, directly integrating human strategic intentions and risk preferences into the AI ​​decision-making process, avoiding AI decisions from deviating from operational intentions and reducing the possibility of misjudgment.

[0019] Comprehensive ethical and safety safeguards: AI solutions are reviewed through an output filtering mechanism to prevent the adoption of decisions that may violate ethics or be high-risk, ensuring that AI recommendations always remain within the bounds of military ethics and regulations, thereby enhancing the safety and reliability of the AI ​​system.

[0020] Rich interaction methods: Supports multimodal interaction such as voice, image / map, and text, adapts to changing battlefield environments, improves the system's ease of use and robustness, and ensures that commanders can effectively use AI-assisted decision-making functions under various conditions.

[0021] Good integration and compatibility: The modular design and standard interfaces make the system easy to integrate with existing military AI and command systems, with good compatibility and scalability, which is conducive to the promotion and application of the system.

[0022] The solution of this invention balances the efficiency of AI decision-making with human command and control, and can significantly improve the availability, security and reliability of AI-assisted command decision-making in the military field. It has high originality and practical value and can be widely applied to various military command scenarios that require AI-assisted decision-making. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the architecture of a military AI decision-making support human-computer interaction and supervision system.

[0024] Figure 2 This is a schematic diagram of an interpretable interactive interface. Detailed Implementation

[0025] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments. It should be understood that the following embodiments are intended to help understand the method and advantages of the present invention and are not intended to limit the scope of the present invention.

[0026] Example: For example Figure 1 As shown, this invention provides a human-computer interaction and supervision system for military AI decision-making assistance, including an AI decision engine 106 and five functional modules interacting with it. The AI ​​decision engine 106 can be an existing military intelligent decision-making module, such as strategic planning AI, battlefield simulation systems, or knowledge graph-based command and decision-making algorithms. This engine is responsible for generating preliminary decision-making schemes (such as operational action plans, troop deployment plans, etc.) based on battlefield data and mission requirements. The various modules of this invention work collaboratively around the AI ​​decision engine to display, regulate, and oversee the AI's decision-making process, as detailed below:

[0027] 1. Implementation of the Explainable Interactive Interface Module (101): When the AI ​​decision engine 106 generates one or more decision schemes, the interface module 101 instantly acquires the inference data and decision basis within the AI ​​and presents them to the commander through visualization. Preferably, the AI ​​decision engine uses the DIKWP semantic model to organize its knowledge and inference process, which enables the interface module to extract key information at each level. For example, in the process of generating a certain combat plan, the AI ​​engine first applies military strategic experience and value criteria to determine the overall guiding ideology at the "wisdom (W) layer," then sets specific task objectives and constraints at the "intent (P) layer," and then uses the operational principles and tactical data of the "knowledge (K) layer" for inference, finally producing a specific action plan at the inference / execution (I) layer. After acquiring the data from the above process, the interface module... Figure 2The interface is presented in a node-link diagram format: the intelligence layer nodes represent strategic values ​​and high-level rules, the intent layer nodes represent current mission objectives, the knowledge layer nodes represent applied rules / knowledge points, and the execution layer nodes represent candidate action plans. Nodes are connected by arrows to indicate the direction of reasoning evolution (e.g., from intelligence layer nodes to intent layer nodes, then to knowledge layer nodes, and finally to a specific plan). Furthermore, the interface specifically highlights the influence of personality factors on some decision-making nodes, such as marking a step in a plan as driven by the "aggressive" factor (leading to bolder tactical choices) or the "conservative" factor (leading to more cautious force deployment). Through this interpretable interface, commanders can understand the AI's decision-making process in detail: including which intelligence data and rule knowledge the AI ​​relied on, what strategic wisdom principles it followed, and why it chose a particular branch of the plan under the influence of personality factors. Based on this, commanders can judge whether the AI's plan is reasonable and reliable and identify potential biases.

[0028] 2. Implementation of the Adjustable Parameter Module (102): In actual combat, command intentions may be adjusted at any time according to the battlefield situation. This invention empowers commanders with the ability to directly control the AI ​​decision-making process through the adjustable parameter module 102. Commanders can modify several key parameters through the control panel on the interface or voice commands: First, the intention parameter, such as changing the combat objectives or priorities that the AI ​​focuses on (attacking a stronghold or protecting key personnel, etc.), and adjusting resource constraints (logistics supply restrictions, time windows, etc.); second, the risk coefficient, where commanders can set their risk preferences, such as ordering the AI ​​to tend towards a conservative (reducing the risk of casualties) or aggressive (accepting certain risks to achieve the mission) decision-making style; third, the ethical threshold, used to clarify the bottom line that cannot be crossed, such as the requirement of zero civilian casualties, prohibition of the use of specific weapons, and other ethical norms. The adjustable parameter module transmits the commander's new parameter input to the AI ​​decision engine 106 in real time. After receiving the adjusted parameters, the engine recalculates the decision-making scheme using the DIKWP model: first, it updates the objectives and constraints at the intent layer (P); then, at the wisdom layer (W), it checks for strategic policy adjustments (e.g., abandoning certain tactical means due to changes in ethical thresholds); next, it retrieves appropriate rules and scheme libraries at the knowledge layer (K); and finally, at the execution layer, it regenerates a scheme adapted to the new parameters. The entire process is completed within seconds, and the interface module updates and displays the new inference chain and scheme differences, allowing the commander to clearly understand the impact of the adjustments. Through this module, the commander effectively integrates their own decision-making intent deeply into the AI ​​calculation process, achieving dynamic control "in the loop," ensuring that AI assistance always aligns with the commander's current intent. Figure 1 To.

[0029] 3. Implementation of the Output Behavior Filtering Mechanism Module (103): No matter how intelligent the AI ​​decision engine 106 is, it may still propose solutions that do not conform to actual constraints or pose potential dangers. The output behavior filtering module 103 of this invention acts as the "gatekeeper" of the AI ​​solutions, reviewing and filtering each candidate solution output by the engine. This module pre-stores a set of ethical rules, which includes military regulations, international conventions, rules of engagement, and commander-defined behavioral guidelines; it also includes a dynamic risk assessment model that can comprehensively assess the risk level of a solution based on the current battlefield situation, the balance of power between the enemy and ourselves, and environmental factors. When the AI ​​engine provides a solution, the filtering module first compares the solution with the ethical rules in the ethical rules library one by one to check whether the solution contains content that violates superior orders or international law, such as attacking medical facilities that are explicitly listed as prohibited targets, or carrying out actions that obviously violate humanitarian principles; once a violation is found, the module immediately marks the solution as unqualified and, if necessary, directly eliminates it. At the same time, the filtering module calls the risk assessment model to quantify and score the possible consequences of the solution's execution, such as the probability of civilian casualties, the degree of our losses, and strategic impact. If the risk assessment result of a certain plan exceeds a preset threshold (e.g., the probability of civilian casualties exceeds zero or the possibility of operation failure is too high), the system will highlight a warning icon for the plan on the interface and attach detailed risk warnings to the plan description for the commander's reference. Figure 2 The interface illustration shows that one of the AI's proposed solutions is marked with a red warning by the system because it involves a nighttime attack on a densely populated area, with the note "potentially causing collateral damage, exceeding the permissible threshold." This allows commanders to quickly identify problematic solutions. In some cases, the filtering module can also mitigate the risks of a solution, proposing mitigation measures or alternatives for high-risk aspects. For example, if a direct assault is too risky, the system can suggest changing to nighttime reconnaissance before action. Through this output behavior filtering mechanism, the reliability of AI suggestions is ensured in multiple ways: it guarantees compliance with ethics and regulations while allowing commanders to fully understand the risks of each solution, enabling them to make more prudent final decisions.

[0030] 4. Implementation of the Multimodal Interaction Channel Module (104): The battlefield environment is complex and ever-changing, and the interaction between commanders and the AI ​​system needs to be flexible and diverse. The multimodal interaction module 104 of this invention provides multiple interaction channels, including voice, graphics, and text, enhancing the system's adaptability in real-world scenarios. Regarding voice interaction: The system integrates voice recognition and voice synthesis units. Commanders can directly issue voice commands to the system via radio or terminal, such as "adjust the attack target to the eastern high ground" or asking "why is a retreat recommended?" The voice recognition unit converts the commands into text commands, which are then parsed and executed or queried by the system. For AI responses or prompts, the voice synthesis unit can convert them into voice broadcasts to the commander, facilitating information acquisition even under conditions of limited visibility or busy schedules. Regarding graphical interaction: The system can operate in conjunction with battlefield maps or sand tables. Commanders can drag or mark troop positions and attack routes on the map interface of the interactive terminal. The system acquires these graphical inputs in real time and translates them into parameters understandable to the AI ​​decision engine (e.g., updating the combat area, route selection, etc.). Meanwhile, AI-suggested solutions can be overlaid on the map, for example, using arrows of different colors to represent marching routes for different plans. Commanders can then visually compare the differences between the plans on the map to make their judgments. Regarding text interaction: the system supports a natural language dialog interface, allowing commanders to type in or select preset questions to ask the AI ​​for more details, such as "Are the logistical supplies for this plan sufficient?" The system understands the questions through natural language processing, and the AI ​​engine provides the answer, which is then displayed on the interface in text form. This multimodal interaction module ensures that commanders can find a suitable way to interact with the AI, whether in a noisy battlefield command center or during field operations, improving the efficiency and accuracy of human-machine collaboration. Furthermore, multimodal input also enables redundant interactions, enhancing the system's robustness.

[0031] 5. Implementation of the Modular Integration Interface Unit (105): The system of this invention is designed with an open and easily integrated architecture. The interface unit 105 follows the communication protocol standards of military command information systems (such as data models like JC3IEDM or custom military AI interface standards) to ensure that the system can be smoothly integrated into existing military information platforms. For example, when deploying the system of this invention on a joint command and control platform, the interface unit can obtain real-time battlefield data (such as enemy and friendly situation, reconnaissance intelligence) provided by the platform and transmit the AI-generated suggested solutions back to the command platform for decision-making reference in a timely manner. For another example, the system can interface with a personality modeling system: the personality modeling system provides commander preferences and style parameters, and the system obtains these personality factor data through the interface unit to update the intelligence layer or intent layer elements in the AI ​​decision-making model, thereby personalizing the AI ​​decision output to better match the specific commander's decision-making style. Similarly, for strategic simulation systems or wargaming systems, the system can also be connected through a standard interface to allow the AI ​​decision suggestions to be verified and evaluated in the simulation environment, or to further optimize the AI ​​decision using the simulation results. The modular integration interface also means that the various functional modules of this system can be tailored or expanded as needed. For example, in some applications, only the interpretable interface and filtering mechanism can be deployed, allowing for integration with third-party AI engines. The interface unit ensures smooth and error-free data exchange between these modules and external systems. Through its highly modular and compatible design, this system can be easily embedded into command structures at different levels, supporting distributed deployment and collaborative work, and possesses excellent scalability and promising prospects for practical applications.

[0032] In summary, the specific implementation of this invention, by organically combining the aforementioned modules, realizes a military AI decision-making support system integrating an interpretable AI interface, human-interventional control, automatic ethical risk management, multimodal interaction, and easy integration. In practical use, the commander first inputs combat instructions or scenario data through the multimodal interface. The AI ​​decision engine 106 generates a preliminary plan based on this and pre-set personality factors. The system interface module 101 transparently presents the decision logic to the commander, the parameter module 102 allows the commander to adjust objectives and constraints as needed, and the filtering module 103 reviews the plan and identifies risks. If the commander is not satisfied with the plan, they can adjust the parameters again or select different plans for comparison in the interface. Finally, after fully understanding the AI ​​suggestions and ensuring there are no ethical risks, the commander adopts the plan and issues the decision for execution through the interface unit 105. The entire process embodies the human-machine collaboration concept of "AI provides efficiency, humans control the direction," fully leveraging the powerful information processing and deductive capabilities of AI while maintaining human control and supervision of key decisions, providing a safe, reliable, and efficient tool for decision-making in future intelligent warfare.

Claims

1. A human-machine interaction and supervisory system for military AI decision assistance, characterized in that, Comprise: An explainable interaction interface module for visualizing the reasoning links and key node information of the AI decision-making process, presenting the decision-making semantic path based on the data-information-knowledge-wisdom-intention (DIKWP) structure, and identifying the influence of personality factors on the decision-making process, enabling the commander to understand the logical basis of AI decision-making; A parameter adjustable module for providing an interface for the commander to adjust AI decision-making strategy parameters in real time, including intention parameters, risk coefficients, and ethical thresholds, etc. The commander's adjustment input can be transmitted and applied to the AI decision-making engine in real time to regenerate a decision-making scheme that meets the commander's requirements based on updated intentions; An output behavior filtering module for conducting ethical and safety audits on candidate schemes output by the AI decision-making engine. The filtering module has an ethical rule library and a risk assessment model built-in. When a candidate scheme violates a predetermined ethical rule or its assessed risk exceeds a threshold, a warning or shielding is triggered to filter out decisions that do not meet ethical or high-risk standards; A multi-modal interaction module for supporting multiple human-computer interaction modes, including voice command input, graphical map operation, and natural language dialogue, etc. to enable the commander to conveniently input instructions and obtain feedback from the system in different environments, improving the robustness and efficiency of interaction; A modular integrated interface unit for integrating the system with external military AI platforms and command systems. The interface unit follows predetermined data communication protocols and standards, enabling the system to operate cooperatively with existing platforms such as strategic simulation systems and personality modeling systems, exchanging required data and instructions, and ensuring the scalability and compatibility of system functions.

2. The system of claim 1, wherein the explainable interaction interface module comprises a decision visualization unit for displaying nodes at each DIKWP level of the AI decision-making process and their associated relationships in the form of a node link graph, and highlighting and annotating the driving effect of pre-set personality factors on key decision nodes on the interface to assist the commander in intuitively assessing the rationality of AI decision-making.

3. The system of claim 1, wherein the parameter adjustable module comprises a unit for adjusting intention parameters, a unit for setting risk coefficients, and a unit for setting ethical thresholds. Upon receiving parameter modification instructions input by the commander, the module triggers the AI decision-making engine to perform re-reasoning calculations based on updated parameters, thereby dynamically generating new decision-making schemes and feeding them back to the explainable interaction interface module for display.

4. The system of claim 1, wherein the output behavior filtering module further comprises a warning unit and a resolution unit. When an AI candidate scheme triggers a violation clause in the ethical rule library, the warning unit issues a violation warning prompt to the commander. When the risk assessment result of a candidate scheme exceeds a pre-set threshold, the resolution unit automatically performs risk mitigation processing on the scheme or directly shields it to ensure that the scheme submitted to the commander is within an acceptable safety range.

5. The system of claim 1, wherein the multi-modal interaction module comprises a speech recognition sub-module, a speech synthesis sub-module, a map interaction sub-module, and a natural language processing sub-module; the interaction of voice instructions and feedback is realized through speech recognition and speech synthesis, the graphical input of the commander on the electronic map and the display of the scheme are realized through the map interaction sub-module, and the commander inquires the details of the AI decision and obtains the explanation and description in the text dialogue mode through the natural language processing.

6. The system of claim 1, wherein the modular integrated interface unit is connected with external platforms through standard data interfaces, the external platforms include strategic and tactical simulation systems, command information systems, or personality trait modeling systems; the modular integrated interface unit can input the battlefield data provided by the command platform into the system for use by the AI decision engine, and output the decision suggestions generated by the system back to the command platform, to realize the bidirectional sharing and synchronization of information and decision results.

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