debate-system-design.md

AI Debate System: Agentic Framework Design Document

1. System Overview

The AI Debate System implements an agentic framework where multiple autonomous AI agents collaborate to conduct structured debates. The system utilizes the HuggingFace Inference API with the Zephyr-7b-beta model for natural language processing capabilities.

2. Agent Architecture

2.1 Core Agent Components

Each agent in the system follows a common agentic structure with five key properties:

• Goals: Specific objectives driving agent behavior
• Actions: Available operations the agent can perform
• Perception: Ability to analyze and understand context
• Memory: Storage of historical information and learned patterns
• Learning: Adaptation mechanisms based on experience

2.2 Agent Types

2.2.1 Debate Agent (DebateAgent)

• Purpose: Present and defend assigned viewpoints in debates
• Properties:

  {
    "name": "string",
    "stance": "string (in favor/against)",
    "memory": "list of past arguments",
    "strategy": "string (balanced/aggressive/defensive)",
    "stats": {
      "arguments_made": "integer",
      "rebuttals_made": "integer",
      "points_addressed": "integer"
    }
  }

• Key Methods:
  • generate_opening_statement(topic, parameters)
  • generate_rebuttal(topic, opponent_argument, parameters)
  • generate_closing_statement(topic, parameters)
  • analyze_opponent(argument)
  • remember(content, type)

2.2.2 Fact Checker Agent (FactCheckerAgent)

• Purpose: Ensure factual accuracy of debate content
• Properties:

  {
    "verified_facts": "dictionary of checked statements",
    "verification_history": "list of past verifications"
  }

• Key Methods:
  • check_facts(statement)
  • Track verification history with timestamps

2.2.3 Moderator Agent (ModeratorAgent)

• Purpose: Guide debate flow and maintain order
• Properties:

  {
    "debate_history": "list of moderation actions"
  }

• Key Methods:
  • moderate(topic, stage)
  • Different moderation styles for introduction, transition, and closing

3. System Architecture

3.1 Core System (DebateSystem)

• Main Components:

  {
    "topic": "debate topic",
    "parameters": "debate configuration",
    "debater_pro": "DebateAgent instance",
    "debater_con": "DebateAgent instance",
    "fact_checker": "FactCheckerAgent instance",
    "moderator": "ModeratorAgent instance",
    "debate_log": "list of debate events"
  }

3.2 Interaction Flow

1. System Initialization:

   graph TD
      A[User Input] --> B[Parameter Setup]
      B --> C[Agent Initialization]
      C --> D[Debate Start]
   

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2. Debate Round Flow:

   graph TD
      A[Moderator Introduction] --> B[Opening Statements]
      B --> C[Main Arguments]
      C --> D[Rebuttals]
      D --> E[Closing Statements]
      E --> F[Moderator Closing]
      B & C & D --> G[Fact Checking]
   

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4. Agent Decision Making

4.1 Debate Agent Strategy

• Analyzes opponent arguments using structured prompts
• Adapts tone and approach based on debate parameters
• Maintains memory of previous points for coherent arguments
• Validates content against inappropriate terms

4.2 Fact Checker Process

• Identifies specific, verifiable claims
• Rates claims with confidence levels
• Provides supporting evidence
• Notes missing context or biases

4.3 Moderator Controls

• Guides debate progression through stages
• Maintains neutrality while ensuring flow
• Provides appropriate transitions
• Summarizes key points

5. Implementation Details

5.1 Language Model Interface

class HFInferenceLLM:
    - Initialization with API token
    - Text generation with parameters:
        - max_new_tokens=256
        - temperature=0.7
        - repetition_penalty=1.1

5.2 Content Validation

• Checks for inappropriate terms:

  inappropriate_terms = [
      "violent", "abusive", "hate", "discriminatory",
      "threatening", "explicit", "offensive"
  ]

5.3 Debate Parameters

• Configurable options:

  parameters = {
      'debate_style': ["Formal", "Casual", "Academic"],
      'debate_rounds': 1-5,
      'focus_points': 1-5,
      'fact_checking': boolean,
      'show_thinking': boolean
  }

6. Future Enhancements

6.1 Planned Improvements

• Enhanced agent learning capabilities
• More sophisticated fact-checking algorithms
• Dynamic strategy adaptation
• Real-time audience interaction
• Expanded content filtering

6.2 Performance Metrics

• Track debate quality metrics
• Monitor fact-checking accuracy
• Measure agent adaptation effectiveness
• Evaluate content appropriateness

7. Usage Guidelines

7.1 System Requirements

• HuggingFace API access
• Streamlit for UI
• Python 3.7+
• Required libraries:
  • huggingface_hub
  • streamlit
  • time
  • typing
  • json

7.2 Setup Instructions

1. Install dependencies
2. Configure HuggingFace API token
3. Launch Streamlit interface
4. Configure debate parameters
5. Input debate topic
6. Start debate session

8. Security and Safety

8.1 Content Filtering

• Real-time content validation
• Inappropriate term detection
• Civil discourse enforcement

8.2 API Security

• Secure token handling
• Rate limiting
• Error handling

9. Monitoring and Logging

9.1 Event Logging

• Detailed debate logs
• Agent action tracking
• System performance metrics

9.2 Debug Information

• Real-time agent thinking display
• Progress tracking
• Error reporting