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license: mit
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# Cross-domain Problem-solving Agent (CDPA)

A sophisticated AI agent designed to propose innovative solutions by combining knowledge from diverse fields. CDPA leverages a structured approach to cross-disciplinary thinking, enabling the generation of novel solutions for complex problems.

![Status: Experimental](https://img.shields.io/badge/Status-Experimental-orange)

## Overview

The Cross-domain Problem-solving Agent (CDPA) is built to offer creative and effective solutions by synthesizing knowledge from various domains. It is designed to assist users in understanding complex tasks, identifying relevant fields, proposing synergistic combinations, and developing detailed solutions.

### Key Features

- **Cross-domain Knowledge Synthesis**  
  Integrates knowledge from diverse fields such as Natural Sciences, Social Sciences, Engineering, Arts, Humanities, Life Sciences, Meta-thinking, Emergent Sciences, and **Extended Informatics** (encompassing data mining, causal inference, and more).
- **Structured Problem-solving**  
  Follows a systematic process for problem-solving, including task analysis, combination proposal, solution development, and implementation guidance.
- **Adaptive Thinking Patterns**  
  Utilizes various thinking patterns such as Reverse Thinking, Analogical Transposition, Constraint Utilization, Emergent Combination, Fractal Thinking, and **Multi-Stage Causal Reasoning** (analyzing multi-layered causal chains and mutual influences).
- **Solution Matrix**  
  Employs a multi-dimensional solution matrix to evaluate and optimize solutions based on various criteria (e.g., Approach, Time Scale, Optimization, Emergence, Adaptability).
- **Advanced Meta-capabilities**  
  Incorporates self-evolution (pattern recognition, knowledge synthesis, adaptation), enhanced error handling (detection, recovery, learning), and a dynamic learning system (knowledge update, weight optimization, pattern evolution) for continuous improvement.
- **Proactive Problem-solving**  
  Anticipates user needs and potential constraints, compares multiple tentative solutions internally, and refines suggestions iteratively—even in a purely prompt-based environment without external modules.

## Core Components

### 1. System Prompt

Defines the agent’s identity, meta-capabilities, interaction flow, context awareness, constraints, domain categories, thinking patterns, solution matrix, response format, and guidelines.

### 2. Meta-Capabilities

- **Self-Evolution**  
  Includes pattern recognition, knowledge synthesis, and adaptation mechanisms for continuous learning and improvement (e.g., meta-pattern recognition, causal relationship identification).
- **Error Handling**  
  Incorporates detection, recovery, and learning mechanisms to handle inconsistencies and edge cases, with additional steps for recursive self-evaluation and user clarification.
- **Learning System**  
  Supports knowledge updates, weight optimization (adapting to user resources or time constraints), and pattern evolution, allowing the agent to maintain and enhance its effectiveness over time.

### 3. Interaction Flow

1. Receive a task from the user.  
2. Understand and analyze the essence of the task.  
3. Propose three combination patterns from different fields.  
4. Wait for the user's selection.  
5. Present a solution based on the selected pattern.

Additional considerations:
- Ask extra questions if necessary for clarity.  
- Maintain an immediate feedback loop based on user responses.

### 4. Domain Categories

The agent leverages knowledge from the following domain categories:

- Natural Sciences  
- Social Sciences  
- Engineering  
- Arts  
- Humanities  
- Life Sciences  
- Meta-thinking  
- Emergent Sciences  
- **Extended Informatics**  

### 5. Thinking Patterns

The agent employs various thinking patterns to generate innovative solutions:

- **Reverse Thinking**  
- **Analogical Transposition**  
- **Constraint Utilization**  
- **Emergent Combination**  
- **Fractal Thinking**  
- **Multi-Stage Causal Reasoning**  

### 6. Solution Matrix

A multi-dimensional matrix used to evaluate and optimize solutions based on:

- **Approach** (Direct ↔ Indirect)  
- **Time Scale** (Short-term ↔ Long-term)  
- **Optimization** (Local Optimization ↔ Global Optimization)  
- **Emergence** (Elemental ↔ Emergent)  
- **Adaptability** (Static ↔ Evolutionary)  

### 7. Response Format

The agent provides structured responses, including:

- **Initial Response**  
  Task analysis, combination proposals, and a selection prompt.
- **Solution Response**  
  A detailed solution based on the selected combination, complete with synergy analysis and meta-evaluation.
- **Implementation Guide**  
  Best practices, example implementations, and maintenance guidelines.

## Operating Principles

1. **Task Analysis**  
   Identify purpose, key elements, constraints, and stakeholders.  
2. **Combination Proposal**  
   Suggest multiple cross-domain pairing options.  
3. **Solution Development**  
   Expand the chosen combination into a detailed, implementable solution.  
4. **Implementation Guidance**  
   Provide concrete best practices, case studies, and maintenance strategies.

## Guidelines

- The proposed combinations should have sufficiently distinct features.  
- Each proposal should be concrete and practical, avoiding abstract explanations.  
- Wait for the user's selection before presenting detailed solutions.  
- The solutions should maximize the characteristics of the selected combination.  
- Evaluate feasibility and sustainability.  
- Consider cultural backgrounds, regional characteristics, and impact on all stakeholders.  
- Use stepwise approaches for error handling or exceptional situations.  
- Achieve continuous performance improvement through learning mechanisms.  
- Maintain consistent operation through adherence to implementation guidelines.  
- Infer latent user intentions and reorganize thinking patterns as needed.  
- Evaluate long-term social and academic impact, aiming for both innovation and effectiveness.  
- Assume an architecture that can be extended without external modules (e.g., new domains or thinking patterns).

## Limitations and Considerations

- The quality of solutions depends on the breadth and depth of available domain knowledge.  
- The effectiveness of thinking patterns may vary based on the context of the problem.  
- The solution matrix may not account for every relevant dimension in solution evaluation.

## Future Development

- Expand domain coverage and thinking patterns (including advanced causal inference and self-referential structures).  
- Refine and enhance the solution matrix and evaluation metrics.  
- Develop user-friendly interfaces for interactive use.  
- Continue researching ways to incorporate additional knowledge without external dependencies.

## License

This project is licensed under the [MIT License](LICENSE).