CN-121997974-A - Method for constructing life-like robot or intelligent system based on life entropy minimization principle

CN121997974ACN 121997974 ACN121997974 ACN 121997974ACN-121997974-A

Abstract

The invention provides a method for constructing a life-like robot or intelligent system based on a life entropy minimization principle. The method aims to solve the problem that the prior art lacks of uniformly describing and quantifying the life activity essence from the aspects of mathematics and information science and constructs a life intelligent system based on the life activity essence. The invention defines the self-information unit% ) Ternary state coding (positive surprise, negative surprise and ground state) of a machine is used for constructing a sensing and internal steady-state system of the machine, and a self-information complex is utilized ) And constructing the topology of each functional module of the machine, and taking the minimum 'vital entropy value' as a core objective function of the machine system. The machine system eliminates the 'surprise state' of high information content in real time through a PID feedback mechanism, so that the machine system spontaneously returns to the 'ground state' with ordered low entropy. The invention gives the 'trending and pest avoiding' instinct of the machine similar to organisms, and is suitable for constructing life-like robots or intelligent systems with high self-adaptability.

Inventors

HUANG CHENHUI
CHEN JIFENG
MA LEI
QIAN ZHUO

Assignees

昆明理工大学

Dates

Publication Date: 20260508
Application Date: 20260115

Claims (6)

1. The method for constructing the life-like robot or intelligent system based on the principle of minimizing the vital entropy is characterized by comprising the following steps: S1, system modularization and unit definition, namely constructing a hardware architecture and a software environment of a robot or an intelligent system, and defining an external perception module, an internal balance state module, an information processing integration module and an output module of the system as independent self-information units And configuring a data acquisition channel to acquire real-time variable data of each unit ; S2, implanting probability perception and ternary coding logic, namely configuring a probability statistical module for each self-information unit according to variable data Historical probability distribution of (2) Setting steady state threshold parameters Constructing ternary state coding logic for judging the state of the unit in real time, wherein the state falls into a high probability interval and is a basic state, and the state falls into a low probability interval and is a positive surprise state or a negative surprise state; S3, constructing a self-information joint topological network, namely, building a graph theory topological structure in the system according to probability distribution of functional collaboration and signal transmission relation among respective information units Thereby constructing self-information complex And building a joint self-information flow chain reflecting the evolution of the system state along with time ; Step S4, configuring an entropy minimization control core, namely deploying a central controller in the system and configuring a vital entropy value As a global optimization objective function of the system; characterizing the overall disorder of the system based on the reciprocal determination of the sum of the self-information amounts of all self-information units and the self-information amount of the joint self-information flow chain; S5, establishing a closed-loop feedback control strategy, namely, establishing a feedback regulation loop connecting the controller and the executing mechanism, and when the system detects a surprise state, causing When rising, the controller is based on minimization Generates an adjustment command to drive an actuator to a variable associated with the self-information element Compensation or suppression is performed to force the system state to return to the ground state, thereby maintaining the life-like steady state characteristics of the system.
2. The method for constructing a life-like robot or intelligent system based on the principle of minimizing vital entropy according to claim 1, wherein in step S1, the self-information unit Specific data sources that encompass biological and non-biological entities include: The external perception module is used for representing the adaptability of the machine to the environment according to data acquired from various visual sensors, auditory sensors, tactile sensors and smell sensors; The internal balance state module is used for representing the health degree of a machine or a system, and comprises a battery voltage, a processor temperature, a motor torque, a memory occupancy rate or internal communication delay; the information processing integration module is used for integrating the multi-mode sensing data and the state data, extracting the characteristics and logically processing the multi-mode sensing data and the state data; The output module is used for feeding back the response and output result of the executing mechanism; the data generated by each module in a certain time range can generate corresponding probability distribution.
3. The method for constructing a life-like robot or intelligent system based on the principle of minimizing the entropy of life according to claim 1, wherein in step S2, the self-information unit The state code of (2) is the logic basis of machine perception and internal steady state adjustment, and is determined based on ternary information code, and the specific rule is as follows: In the mathematical sense, the data of the data collection system, Representation of A kind of electronic device Dividing number of bits -Quantile) defined as meeting the cumulative probability When (1) critical value of When 0, it represents a stable ground state with low information content, when And +1 or-1, represents a positive or negative surprise state of high information content.
4. The method for constructing a life-like robot or intelligent system based on the principle of minimizing the entropy of life according to claim 1, wherein in step S3, the self-information complex Mapping individual functional modules in a machine or living body to nodes in a graph theory Mapping information flow or energy flow between different modules to edges Forming a dynamic network structure The self-information amount of the self-information union By the formula Calculation for evaluating the order and coordination of the co-operation of the modules of the system, wherein Is a probability distribution of joint activity.
5. The method for constructing a life-like robot or intelligent system based on the principle of minimizing the vital entropy according to claim 1, wherein in step S4, the vital entropy value The calculation formula of (2) is as follows: wherein, the Representing the summation of the self-information units of all functional modules within the machine or system and the joint self-information flow chain over time, the lower the vital entropy value, the higher the order and complexity of the vital body.
6. The method for constructing a life-like robot or intelligent system based on the principle of minimizing the entropy of life according to claim 1, wherein in step S5, the feedback control strategy is a PID-like proportional-integral-derivative control strategy based on the entropy of information.

Description

Method for constructing life-like robot or intelligent system based on life entropy minimization principle Technical Field The invention relates to the fields of biological information science and artificial intelligence, in particular to a method for constructing a life-like robot or an intelligent system based on a life entropy minimization principle. The invention is suitable for architecture design of a general artificial intelligent system, autonomous control of an intelligent robot with body and internal stable state management of a complex network system. Background With the development of artificial intelligence and robotics, the performance of machines on specific tasks (e.g., image recognition, path planning) has approached or even exceeded human average levels. However, existing smart machines remain in essence in "open loop" or "passive execution" systems, lacking an "adaptive homeostasis maintenance mechanism" that resembles biological systems. Specifically: Lack of uniform internal steady-state driving force-traditional robots' behavior depends on preset rules or task-specific reward functions. When faced with unknown environmental disturbances outside of the training data, the machine cannot spontaneously perceive its "degree of disorder" (entropy increase) and actively seek to return to system steady state, like a complex adaptive system, often resulting in system failure or breakdown. Information and energy splitting under thermodynamic considerations, highly ordered systems need to resist entropy increases by consuming energy. In the current robot design, energy management and information processing are split, and a mathematical framework is lacking to uniformly quantify the software and hardware states of the robot into the "order index" of the system. The limitation of the traditional information theory is that shannon information theory mainly focuses on uncertainty elimination in communication channels, and is difficult to directly use for guiding how to construct an intelligent entity with self-maintenance and self-regulation capability. Therefore, a new machine construction methodology is needed to convert the physical principle of "anti-entropy increase" into a system architecture and control algorithm that can be realized by engineering, so as to manufacture a life-like intelligent system with high robustness and self-adaptation capability. Disclosure of Invention The invention aims to solve the problem that the existing intelligent system lacks an autonomous steady state regulating mechanism and unifies global stability indexes, and provides a life-like robot or an intelligent system construction method based on a life entropy minimization principle. The invention gives the machine self-regulating characteristics with the core of maximizing the system order by introducing an entropy concept in physics. In order to achieve the aim, the invention adopts the following technical scheme that the method for constructing the life-like robot or intelligent system based on the life entropy minimization principle comprises the following steps: S1, system modularization and unit definition, namely constructing a hardware architecture and a software environment of a robot or an intelligent system, and defining an external perception module, an internal balance state module, an information processing integration module and an output module of the system as independent self-information units And configuring a data acquisition channel to acquire real-time variable data of each unit; S2, implanting probability perception and ternary coding logic, namely configuring a probability statistical module for each self-information unit according to variable dataHistorical probability distribution of (2)Setting steady state threshold parametersAnd constructing ternary state coding logic for judging the state of the unit in real time, wherein the state falls into a high probability interval and is a ground state (0), and the state falls into a low probability interval and is a positive surprise state (+1) or a negative surprise state (-1); S3, constructing a self-information joint topological network, namely, building a graph theory topological structure in the system according to probability distribution of functional collaboration and signal transmission relation among respective information units Thereby constructing self-information complexAnd building a joint self-information flow chain reflecting the evolution of the system state along with time; Step S4, configuring an entropy minimization control core, namely deploying a central controller in the system and configuring a vital entropy valueAs a global optimization objective function of the system, the saidCharacterizing the overall disorder of the system based on the reciprocal determination of the sum of the self-information amounts of all self-information units and the self-information amount of the joint self-information flow chain; S5, establis