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CN-122021699-A - Intelligent autonomous decision-making interaction method based on dynamic personality and bionic memory system

CN122021699ACN 122021699 ACN122021699 ACN 122021699ACN-122021699-A

Abstract

The invention provides an intelligent body autonomous decision-making interaction method based on a dynamic personality and a bionic memory system, and relates to the technical field of artificial intelligence and cognitive computing. Initializing multidimensional personality parameters of an intelligent agent, constructing a personality map, and establishing a structured memory base containing scene, semantic and emotion memories, and attaching a time stamp, emotion weight and a label to the memories. And receiving environment input in real time, carrying out intention recognition and emotion assessment by combining personality states and associated memories to generate a candidate behavior set, and selecting target behaviors from the candidate set and generating interactive instructions through a decision mechanism for comprehensively assessing expected emotion benefits, personality consistency and task utility. And finally, dynamically adjusting personality parameters and emotion states according to the behavior feedback, and performing reinforced storage and selective forgetting on the memory bank. The invention realizes personality consistency maintenance, memory-driven autonomous behavior decision and long-term personality evolution of the intelligent agent in interaction, and improves the emotion credibility and active adaptability of anthropomorphic interaction.

Inventors

  • YANG CHENLIANG

Assignees

  • 杭州同瓴信息科技有限公司

Dates

Publication Date
20260512
Application Date
20260119

Claims (10)

  1. 1. The intelligent autonomous decision-making interaction method based on the dynamic personality and the bionic memory system is characterized by comprising the following steps of: S10, personality initialization and modeling, namely initializing multidimensional personality parameters of the intelligent agent according to user setting or historical interaction data, and constructing an initial personality map; s20, constructing and updating a bionic memory system, namely constructing a structured memory bank, wherein the memory bank at least comprises scene memory, semantic memory and emotion memory, and adding a time stamp, emotion weight and an associated label to a memory item; S30, autonomous decision triggering and evaluation, namely receiving environment input information in real time, combining the current personality state and associated memory content, and carrying out intention recognition and emotion state evaluation by simulating a double-path collaborative mechanism of quick thinking and slow thinking of human cognition to generate a candidate behavior set; S40, behavior decision and generation, namely selecting a target behavior from the candidate behavior set based on a preset utility function and personality consistency evaluation rule, and generating a corresponding interaction behavior instruction; S50, personality evolution and memory reinforcement, namely dynamically adjusting personality parameters and updating emotion states according to environment feedback after behavior execution, and executing reinforced storage and selective forgetting on a memory bank.
  2. 2. The method for autonomous decision interaction of an agent based on a dynamic personality and bionic memory system according to claim 1, wherein in step S10, the personality parameters are set based on a large five personality model OCEAN, including openness, responsibility, handedness, pleasure, and neuro-physical dimensions, and the personality map is constructed by mapping each personality dimension in association with emotion response rules and behavioral tendencies.
  3. 3. The intelligent autonomous decision-making interaction method based on the dynamic personality and bionic memory system according to claim 1, wherein in step S20, the memory library uses a graph data structure to store, and memory items are used as nodes to construct side relations by associating labels and emotion weights so as to realize semantic and emotion-based associative memory retrieval.
  4. 4. The intelligent autonomous decision interaction method based on the dynamic personality and bionic memory system according to claim 1, wherein in step S30, the environment input information includes user dialogue text, environment state variables and predefined event triggers, and the activation condition of the slow thinking path includes that the input information relates to a multitasking conflict, emotion blurring, historical feedback contradiction or touching a preset safety keyword.
  5. 5. The method according to claim 1 or 4, wherein in step S30, after the slow thinking path is activated, the preliminary intent generated by the fast thinking path is checked, inhibited or deepened, and the intervention strength is adjusted by the neural and disciplinary dimensions in the current personality parameters.
  6. 6. The intelligent autonomous decision interaction method based on the dynamic personality and bionic memory system according to claim 1 or 4, wherein in step S40, the utility function comprehensively evaluates expected emotional benefits of candidate behaviors, the degree of personality consistency and the task completion utility, and the personality consistency evaluation is realized by calculating the deviation degree of candidate behaviors and historical behavior patterns in personality dimensions.
  7. 7. The method of intelligent autonomous decision interaction based on dynamic personality and biomimetic memory system according to claim 6, wherein step S40 further comprises introducing a controllable randomness factor in the decision process to simulate uncertainty of human behavior, wherein the action intensity of the randomness factor is adjusted by the current neural personality dimension parameter.
  8. 8. The intelligent autonomous decision-making interaction method based on the dynamic personality and the bionic memory system according to claim 1, wherein in step S50, the dynamic adjustment of personality parameters is performed according to the long-term behavior feedback trend, and a personality baseline stability mechanism is introduced to prevent the personality from mutating under short-term feedback, and the selective forgetting mechanism dynamically determines forgetting probability according to memory entry time, emotion weight attenuation and access frequency.
  9. 9. The intelligent autonomous decision-making interaction method based on the dynamic personality and bionic memory system according to claim 1, wherein the method further comprises an active interaction opportunity judging step of calculating a suitability score of active interaction by monitoring idle time of a user state, historical interaction rules and significance of current emotion memory, and when the score exceeds a threshold value, autonomously triggering the processes of steps S30 to S40 to generate active interaction behaviors.
  10. 10. The intelligent autonomous decision interaction method based on the dynamic personality and bionic memory system according to claim 1, further comprising a safety and ethics constraint step, wherein in the behavior decision of step S40, all candidate behaviors are screened through a predefined safety rule and ethics standard filter, behavior options which do not accord with constraints are removed, and the generated behavior instructions accord with a preset safety boundary.

Description

Intelligent autonomous decision-making interaction method based on dynamic personality and bionic memory system Technical Field The invention relates to the technical field of artificial intelligence and cognitive computation, in particular to an intelligent autonomous decision-making interaction method based on a dynamic personality and bionic memory system. Background With the rapid development of artificial intelligence technology, agents with interactive capability, such as virtual assistants, digital persons, game non-player characters, social robots, and the like, have been widely used in various scenes. Currently, such systems rely mostly on predefined rules, dialogue models trained based on large amounts of data, or finite state machines, etc. conventional technology paths to implement interactive functions. For example, a dialog system based on a large language model can generate smooth, plausible text replies, while characters in a game typically perform a preset sequence of actions according to a script or a behavioral tree. These techniques meet the underlying functional needs to some extent. However, the limitations of the prior art are not revealed when one expects that an agent can exhibit socioeconomic interactions that more closely resemble human partners over a simple instruction response. One core drawback is that personality behavior exhibits fragmentation and inconsistencies. Many systems may be able to simulate certain character traits in a single conversation, but they exhibit lack of stability and continuity across time, across sessions. The user may feel humor of the agent in one interaction, and get a inscribed response in the same situation in the next interaction, and the confidence and emotion depth of the interaction are seriously weakened by the 'memory loss' and jumping of the personality state. Further, existing agent behavior decisions are highly dependent on external explicit instructions or explicit trigger conditions, and remain essentially in a "stimulus-response" passive mode. They lack the ability to actively initiate natural social behavior such as care, sharing, or advice at the right moment, absent the continued perception and understanding of the deep context and potential needs of human users. The interactive behavior of the interactive interaction is often split and responsive, and an active interactive narrative with an internal motivation and a coherent context cannot be formed, so that the interactive experience is mechanically hard, and a real emotional connection is difficult to establish. Another significant problem is the disjoint of emotional response from the inherent personality. Some systems integrate emotion recognition or emotion calculation modules that can recognize the emotion of a user and generate corresponding emotion tags or standardized responses, but such emotion responses are often surface, conditional, or reflective. It is not deeply bound to an evolving intrinsic personality core, and emotional expressions lack individuation and historical consistency. The agent cannot respond to the event with an emotion of personal color based on its unique personality experience and make a corresponding decision like a human, and thus it is difficult to form convincing "self" consciousness. In addition, most existing systems do not have the ability to long-term memory and personality evolution. They typically treat each interaction as an independent event, or only do a short context cache. The intelligent agent cannot accumulate experience from long interaction history and form stable behavior preference, and the character of the intelligent agent cannot show growth, change or adaptation conforming to vital body characteristics after the system is restarted or runs for a long time. This static nature causes the interaction to fall short before the user's viscosity decreases rapidly as the freshness subsides. In summary, it is difficult to construct a high-order intelligent agent with stable personality, autonomous behavioral motivation, emotion rationality and long-term growth in the current technology, which limits the application of the high-order intelligent agent in leading-edge scenes requiring deep emotion accompaniment, complex narrative generation or personification cooperation and the like. In addition, from the aspect of the cognitive architecture, the decision process of the existing agent is mostly presented as a single and linear calculation flow, and simulation of dynamic cooperation and balance of two modes of 'intuitive fast thinking' and 'rational slow thinking' in human thinking is lacking. This results in a behavior generation mechanism that is difficult to reproduce the subtle balance between fast emotional response and slow deliberate reasoning in humans, and the resulting true, fine behavioral tension, thereby limiting the personification depth and psychological credibility of the agent decision process. Disclosure of Invention T