4.2.1 Core Architecture Design

Core Architecture Design

The agent framework consists of multiple modules that work in close coordination to support the perception, decision-making, and action capabilities of agents. According to the architecture diagram, the agent framework includes the following core components:

1. World Rules Module

  • This includes trading systems, housing systems, education systems, and other modules related to the operation of a virtual society. These rules define the basic operational logic of the environment in which agents exist and constrain their behavior.

  • For example, the trading system enables economic activities for agents, and the election system supports democratic decision-making among agents.

2. Behavior and Planning Module

  • The Planner is the core module for agent behavior, encompassing the following functionalities:

  • Environmental Perception: Captures environmental data through sensors or virtual inputs, identifying task objectives and external changes.

  • Daily Planner: Generates daily activity plans for agents, dynamically adjusting behavior based on time and resources.

  • Detailed Planner and Behavior Sequence Generator: Further refines task planning, generating specific behavior sequences for the execution module.

  • The behavior planning module integrates with the Event Queue to enable dynamic task distribution and priority management.

3. Memory and State Management Module

  • Running State serves as the memory module for agents, storing the current task state and long-term memory data. It supports behavior optimization through the following functionalities:

  • Short-Term Memory: Enables real-time task scheduling and state feedback.

  • Long-Term Memory: Helps agents retain experiences and recall relevant information for future decisions.

  • The Memory Module also facilitates data sharing among internal submodules (e.g., Action Agent and Conversation Agent), improving overall efficiency.

4. Dialogue Module and Natural Language Processing

  • The dialogue module enables agents to interact with users or other agents through natural language, including the following core features:

  • Message Distribution: Distributes user inputs or environmental information to the relevant modules for processing.

  • Time and Topic Planner: Determines the logic and content direction of conversations based on context.

  • Agents can dynamically adjust task behaviors through dialogue, enhancing interaction with users.

5. Advanced Abstraction Modules

  • Agents possess advanced modules such as emotion, psychological states, and constitutions, making their behavior more human-like and complex.

  • These modules support the long-term behavioral evolution of agents and the establishment of relationships with other agents through continuously updated state management.

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