Humanoid health care robot ethical decision-making method and system based on game theory

Abstract

The invention discloses a humanoid health care robot ethical decision-making method and system based on the game theory, and particularly relates to the technical field of robots, and the method comprises the steps: obtaining the physiological, psychological and environmental data of old people and the environment; constructing a game model in which multiple parties participate; calculating an optimal strategy of each party according to the game theory model, and determining an optimal behavior path of the robot; under the condition of multi-party benefit conflicts, various decisions are sorted according to the ethical decision algorithm, and the technical scheme has the beneficial effects that through the ethical decision method and system based on the game theory, and in combination with Nash equilibrium, social welfare maximization and ethical conflict minimization theories, the multi-party benefit conflicts are obtained; the robot can obtain physiological and psychological data of the old people in real time, calculate an optimal nursing strategy according to a multi-party game model, dynamically adjust a decision path, ensure that the nursing requirements of the old people are met, minimize ethical conflicts, improve the nursing effect and improve the overall nursing experience and life quality of the old people.

Description

Technical Field

[0001] The present invention relates to the technical field of robots, and in particular to an ethical decision-making method and system for humanoid health care robots based on game theory. Background Art

[0002] With the advent of an aging society, more and more elderly people need long-term care, and the application prospect of robots in the care of the elderly is broad. However, existing technologies usually rely on fixed algorithms or simple rules to perform care tasks, but often lack the ability to dynamically adjust decisions in complex situations, especially failing to fully consider the physiological, psychological states and emotional changes of the elderly. In addition, when existing care robots handle ethical decisions, they have not effectively combined theories such as game theory to optimize the management of multi-party interests and ethical conflicts. Summary of the Invention

[0003] The present invention aims to solve the problems that existing technologies usually rely on fixed algorithms or simple rules to perform care tasks, but often lack the ability to dynamically adjust decisions in complex situations, especially failing to fully consider the physiological, psychological states and emotional changes of the elderly. In addition, when existing care robots handle ethical decisions, they have not effectively combined theories such as game theory to optimize the management of multi-party interests and ethical conflicts.

[0004] To achieve the above object, the present invention adopts the following technical solutions: An ethical decision-making method for a humanoid health care robot based on game theory, the method comprising:

[0005] S1. Obtain the physiological, psychological and environmental data of the elderly and the environment;

[0006] S2. Construct a game model participated by multiple parties;

[0007] S3. Calculate the optimal strategies of all parties according to the game theory model, and determine the optimal behavior path of the robot;

[0008] S4. In the case of multi-party interest conflicts, sort multiple decisions according to the ethical decision-making algorithm, and select the optimal decision that is most in line with ethics.

[0009] Preferably, the game model uses the Nash equilibrium and the theory of maximizing social welfare to calculate the optimal strategies of all parties, so as to obtain the optimal ethical decision. The objective function of the robot in the game model includes the comprehensive objectives of maximizing the satisfaction of the care needs of the elderly, minimizing ethical conflicts and improving the care effect.

[0010] The effects achieved by the above components are as follows: By combining the Nash equilibrium and the theory of maximizing social welfare, an optimal balance can be achieved among multiple interests, ensuring that the robot can make optimal decisions among meeting the nursing needs of the elderly, minimizing ethical conflicts, and improving the nursing effect. This method helps to improve the decision-making rationality and ethics of the robot during the nursing process, ensuring that the robot can dynamically adjust its strategies in different situations, thereby better serving the elderly and optimizing the nursing quality.

[0011] Preferably, in the case of ethical conflicts, the robot establishes a multi-level ethical evaluation mechanism to conduct multi-dimensional evaluations of the ethical consequences of different decisions and selects the decision with the least ethical conflict.

[0012] The effects achieved by the above components are as follows: By establishing a multi-level ethical evaluation mechanism and conducting multi-dimensional evaluations of different decisions in the case of ethical conflicts, it can ensure that the robot selects the decision with the least ethical conflict in complex situations. This method improves the ethical decision-making ability of the robot, ensuring that the robot can balance the interests of all parties during the nursing process, avoid or reduce ethical problems, thereby better serving the elderly and enhancing the nursing quality and social acceptance.

[0013] Preferably, during the dynamic nursing process, the robot dynamically adjusts its ethical decisions based on real-time feedback and adjusts its behavior strategies according to the changes in the physiological and psychological states of the elderly. The robot optimizes the decision-making path in advance by simulating multi-party games in different situations to ensure that it can meet the ethical and the elderly's needs to the greatest extent during the nursing process.

[0014] The effects achieved by the above components are as follows: By dynamically adjusting the decision-making path, combining real-time feedback and the changes in the physiological and psychological states of the elderly, it can flexibly respond to different situations and needs during the nursing process. This method optimizes the decision-making path by simulating multi-party games in advance, ensuring that the robot can meet the ethical requirements and the nursing needs of the elderly to the greatest extent in various situations, thereby improving the nursing effect, enhancing the ethics, and improving the overall quality of life of the elderly.

[0015] Preferably, the system includes:

[0016] A data perception module for obtaining the physiological, psychological, and environmental data of the elderly in real time;

[0017] A game decision-making module for calculating and selecting the optimal ethical decision-making strategy through a game theory model, taking into account the interests of the robot, the elderly, and the caregivers;

[0018] An action execution module for executing specific nursing actions according to the output of the game decision-making module;

[0019] The feedback adjustment module is used to collect feedback data and dynamically adjust the game decision-making process according to the feedback information.

[0020] Preferably, the game decision-making module adopts non-cooperative game, cooperative game and repeated game models, calculates the optimal strategies of all parties, and selects the optimal decision that conforms to ethical principles. The game decision-making module optimizes the ethical decision-making of the robot in a complex nursing environment through Nash equilibrium, social welfare maximization or minimization of ethical conflict theory.

[0021] The effects achieved by the above components are as follows: By adopting non-cooperative game, cooperative game and repeated game models, combined with Nash equilibrium, social welfare maximization and minimization of ethical conflict theory, it is possible to calculate and optimize the optimal strategies of all parties in a complex nursing environment, so as to select the optimal decision that conforms to ethical principles. This method ensures that the robot can achieve a balance between multi-party interests and ethical conflicts, dynamically adjust decisions, improve the nursing effect, while ensuring the maximum satisfaction of the needs of the elderly and ethical compliance.

[0022] Preferably, the feedback adjustment module adjusts the decision-making behavior of the robot in real time according to the physiological state, psychological needs, emotional changes of the elderly and the feedback information of the nursing staff. The ethical decision-making evaluation mechanism is used to conduct multi-dimensional ethical evaluations on the results output by the game decision-making module.

[0023] The effects achieved by the above components are as follows: By the feedback adjustment module combining the physiological state, psychological needs, emotional changes of the elderly and the feedback of the nursing staff, the decision-making behavior of the robot is adjusted in real time, and at the same time, the ethical decision-making evaluation mechanism is used to conduct multi-dimensional ethical evaluations on the game decision-making results. This method can ensure that the robot makes decisions that conform to ethical principles based on real-time data in a dynamically changing nursing environment, thereby improving the nursing quality, optimizing the nursing experience of the elderly, and effectively reducing the risks of ethical conflicts and improper decisions.

[0024] Preferably, the behavior execution module includes the physical mechanism for the nursing robot to execute nursing tasks. The data perception module collects the physiological and psychological state data of the elderly through physiological sensors and emotion recognition sensors.

[0025] The effects achieved by the above components are as follows: Through the collaborative work of the behavior execution module and the data perception module, combined with physiological sensors and emotion recognition sensors to collect the physiological and psychological state data of the elderly in real time, accurate feedback information is provided for the robot. This enables the robot to dynamically adjust the execution of nursing tasks according to the actual needs and emotional changes of the elderly, thereby improving the nursing efficiency and personalized service while ensuring ethical compliance, and optimizing the nursing experience of the elderly.

[0026] In summary, the beneficial effects of the present invention are as follows:

[0027] Through the ethical decision-making method and system based on game theory, combining the Nash equilibrium, social welfare maximization, and the theory of minimizing ethical conflicts, the robot can obtain the physiological and psychological data of the elderly in real time, calculate the optimal care strategy according to the multi-party game model, dynamically adjust the decision-making path, ensure that while meeting the care needs of the elderly, minimize ethical conflicts, improve the care effect, and enhance the overall care experience and quality of life of the elderly. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] As Figure 1 shown, the method includes:

[0030] S1. Obtain the physiological, psychological, and environmental data of the elderly and the environment;

[0031] S2. Construct a game model involving multiple parties;

[0032] S3. Calculate the best strategies of all parties according to the game theory model and determine the optimal behavior path of the robot;

[0033] S4. In the case of multi-party interest conflicts, rank multiple decisions according to the ethical decision-making algorithm and select the optimal decision that is most in line with ethics.

[0034] The game model uses the Nash equilibrium and the theory of maximizing social welfare to calculate the optimal strategies of all parties, thereby obtaining the optimal ethical decision. The objective function of the robot in the game model includes the comprehensive objectives of maximizing the satisfaction of the elderly's care needs, minimizing ethical conflicts, and improving the care effect. By combining the Nash equilibrium and the theory of maximizing social welfare, an optimal balance can be achieved among the interests of multiple parties, ensuring that the robot can make the optimal decision among meeting the elderly's care needs, minimizing ethical conflicts, and improving the care effect. This method helps to improve the rationality and ethics of the robot's decision-making during the care process, ensuring that the robot can dynamically adjust its strategies in different situations, so as to better serve the elderly and optimize the care quality. Among them, in the case of ethical conflicts, the robot establishes a multi-level ethical evaluation mechanism to conduct multi-dimensional evaluations of the ethical consequences of different decisions and selects the decision with the least ethical conflict. By establishing a multi-level ethical evaluation mechanism and conducting multi-dimensional evaluations of different decisions in the case of ethical conflicts, it can ensure that the robot selects the decision with the least ethical conflict in complex situations. This method improves the robot's ethical decision-making ability, ensures that the robot can balance the interests of all parties during the care process, avoid or reduce ethical problems, so as to better serve the elderly and improve the care quality and social acceptance. The robot dynamically adjusts its ethical decision-making according to real-time feedback during the dynamic care process and adjusts its behavioral strategies according to the changes in the physiological and psychological states of the elderly. Among them, the robot optimizes the decision-making path in advance by simulating multi-party games in different situations, ensuring that it can meet ethical and the elderly's needs to the greatest extent during the care process. By dynamically adjusting the decision-making path, combining real-time feedback and the changes in the physiological and psychological states of the elderly, it can flexibly respond to different situations and needs during the care process. This method optimizes the decision-making path by simulating multi-party games in advance, ensuring that the robot can meet the ethical requirements and the elderly's care needs to the greatest extent in various situations, thereby improving the care effect, enhancing ethics, and improving the overall quality of life of the elderly.

[0035] The system includes:

[0036] A data perception module for obtaining the physiological, psychological, and environmental data of the elderly in real time;

[0037] A game decision-making module for calculating and selecting the optimal ethical decision-making strategy through a game theory model, considering the interests of the robot, the elderly, and the caregivers comprehensively;

[0038] An action execution module for executing specific care actions according to the output of the game decision-making module;

[0039] A feedback adjustment module for collecting feedback data and dynamically adjusting the game decision-making process according to the feedback information.

[0040] The game decision-making module adopts non-cooperative game, cooperative game and repeated game models. By calculating the optimal strategies of all parties, it selects the optimal decision that conforms to ethical principles. The game decision-making module optimizes the ethical decision-making of the robot in a complex nursing environment through Nash equilibrium, social welfare maximization or minimization of ethical conflict theory. By adopting non-cooperative game, cooperative game and repeated game models and combining Nash equilibrium, social welfare maximization and minimization of ethical conflict theory, it is able to calculate and optimize the optimal strategies of all parties in a complex nursing environment, so as to select the optimal decision that conforms to ethical principles. This method ensures that the robot can achieve a balance between the interests of multiple parties and ethical conflicts, dynamically adjust decisions, improve the nursing effect, and at the same time ensure the maximum satisfaction of the needs of the elderly and ethical compliance. The feedback adjustment module adjusts the decision-making behavior of the robot in real time according to the physiological state, psychological needs, emotional changes of the elderly and the feedback information of the nursing staff. The ethical decision-making evaluation mechanism is used to conduct multi-dimensional ethical evaluations on the results output by the game decision-making module. By combining the physiological state, psychological needs, emotional changes of the elderly and the feedback of the nursing staff through the feedback adjustment module, the decision-making behavior of the robot is adjusted in real time. At the same time, the ethical decision-making evaluation mechanism is used to conduct multi-dimensional ethical evaluations on the game decision results. This method can ensure that the robot makes decisions that conform to ethical principles based on real-time data in a dynamically changing nursing environment, thereby improving the quality of care, optimizing the nursing experience of the elderly, and effectively reducing the risks of ethical conflicts and improper decisions. The behavior execution module includes the physical mechanism for the nursing robot to execute nursing tasks. The data perception module collects the physiological and psychological state data of the elderly through physiological sensors and emotion recognition sensors. Through the coordinated work of the behavior execution module and the data perception module, combined with the real-time collection of the physiological and psychological state data of the elderly by physiological sensors and emotion recognition sensors, accurate feedback information is provided for the robot. This enables the robot to dynamically adjust the execution of nursing tasks according to the actual needs and emotional changes of the elderly, so as to improve the nursing efficiency and personalized service while ensuring ethical compliance, and optimize the nursing experience of the elderly.

Claims

1. A method for ethical decision-making of humanoid health care robots based on game theory, characterized in that The method includes: S1. Obtain the physiological, psychological and environmental data of the elderly and the environment; S2. Construct a game model involving multiple parties; S3. Calculate the optimal strategies of all parties according to the game theory model and determine the optimal behavior path of the robot; S4. In the case of multi-party interest conflicts, rank multiple decisions according to the ethical decision-making algorithm and select the optimal decision that best conforms to ethics.

2. The ethical decision-making method for a humanoid healthcare robot based on game theory according to claim 1, wherein: The game model uses the Nash equilibrium and the theory of maximizing social welfare to calculate the optimal strategies of all parties, so as to obtain the optimal ethical decision. The objective function of the robot in the game model includes the comprehensive objectives of maximizing the satisfaction of the elderly's care needs, minimizing ethical conflicts and improving the care effect.

3. The ethical decision-making method for humanoid healthcare robots based on game theory according to claim 1, characterized in that: Among them, in the case of ethical conflicts, the robot establishes a multi-level ethical evaluation mechanism to multi-dimensionally evaluate the ethical consequences of different decisions and selects the decision with the least ethical conflict.

4. The ethical decision-making method for a humanoid health care robot based on game theory according to claim 1, characterized in that: The robot dynamically adjusts the ethical decision according to the real-time feedback during the dynamic care process, and adjusts its behavior strategy according to the changes in the physiological and psychological states of the elderly. The robot optimizes the decision-making path in advance by simulating multi-party games in different scenarios to ensure that the ethics and the needs of the elderly can be maximally met during the care process.

5. A humanoid health care robot ethical decision-making system based on game theory, characterized in that: Using the ethical decision-making method for a humanoid health care robot based on game theory according to any one of claims 1-4, the system includes: A data perception module for real-time obtaining the physiological, psychological and environmental data of the elderly; A game decision-making module for calculating and selecting the optimal ethical decision-making strategy through a game theory model, comprehensively considering the interests of the robot, the elderly and the caregivers; An action execution module for executing specific care actions according to the output of the game decision-making module; A feedback adjustment module for collecting feedback data and dynamically adjusting the game decision-making process according to the feedback information.

6. The ethical decision-making system of the humanoid healthcare robot based on game theory according to claim 5, characterized in that: The game decision-making module adopts non-cooperative game, cooperative game and repeated game models, selects the optimal decision that conforms to ethical principles by calculating the optimal strategies of all parties, and optimizes the ethical decision of the robot in a complex care environment through the Nash equilibrium, maximizing or minimizing social welfare or minimizing ethical conflict theory.

7. The ethical decision-making system for a humanoid healthcare robot based on game theory according to claim 5, characterized in that: The feedback adjustment module adjusts the decision-making behavior of the robot in real time according to the physiological state, psychological needs, emotional changes of the elderly and the feedback information of the caregivers. The ethical decision evaluation mechanism is used to multi-dimensionally evaluate the results output by the game decision-making module.

8. The ethical decision-making system for a humanoid healthcare robot based on game theory according to claim 5, wherein: The action execution module includes the physical mechanism for the care robot to execute care tasks. The data perception module collects the physiological and psychological state data of the elderly through physiological sensors and emotion recognition sensors.

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