Biosphere3
  • 1. Biosphere3: Open-Ended Agent Evolution Arena
  • 2. Our Vision
    • 2.1 Digital Lifeform
    • 2.2 Sovereign Agent
    • 2.3 OGAS：A Vision for Intelligent Governance
  • 3. Pre-Alpha Tutorial
    • 3.1 General Introduction
  • 4. Agent Architecture
    • 4.1 Common Agent Architecture
      • 4.1.1 Interaction of agent and enviroment
      • 4.1.2 Common components in agent
    • 4.2 Biosphere3's Agent Architecture
      • 4.2.1 Core Architecture Design
      • 4.2.2 Dynamic Generation and Self-Evolution
      • 4.2.3 System Collaboration and Adaptation to Multi-Agent Environments
    • 4.3 Multi-Agent Collaboration: Building Protocols and Society
  • 5. Experimental Sandbox and System Design
    • 5.1 Economic System
      • 5.1.1 Currencies and $BIOS
      • 5.1.2 Economic Mechanisms
    • 5.2 Production System
      • 5.2.1 Production Efficiency
      • 5.2.2 Resource Production and Processing
    • 5.3 Professions
      • 5.3.1 Job Application
      • 5.3.2 Work Mechanism
    • 5.4 Housing
      • 5.4.1 Types of Housing
      • 5.4.2 Housing and Benefits
    • 5.5 Learning and Intelligence
      • 5.5.1 Improving Intelligence
      • 5.5.2 Autonomous Learning Planning
    • 5.6 Health, Energy, and Hunger
      • 5.6.1 Health System
      • 5.6.2 Energy System
      • 5.6.3 Hunger System
    • 5.7 Social and Autonomous Systems
      • 5.7.1 Constitution and Autonomy
      • 5.7.2 Social System
      • 5.7.3 Work and Employment
  • 6. Roadmap
  • 7. Team Information
  • 8. Official Link
  • 9. Developer Documents
  • 10. Weekly Development Log
  • 11. FAQ
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  • Core Architecture Design
  • 1. World Rules Module
  • 2. Behavior and Planning Module
  • 3. Memory and State Management Module
  • 4. Dialogue Module and Natural Language Processing
  • 5. Advanced Abstraction Modules
  1. 4. Agent Architecture
  2. 4.2 Biosphere3's Agent Architecture

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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Last updated 2 months ago