DANA: Domain-Aware Neurosymbolic Agents for Consistency and Accuracy (2410.02823v1)

Abstract: LLMs have shown remarkable capabilities, but their inherent probabilistic nature often leads to inconsistency and inaccuracy in complex problem-solving tasks. This paper introduces DANA (Domain-Aware Neurosymbolic Agent), an architecture that addresses these issues by integrating domain-specific knowledge with neurosymbolic approaches. We begin by analyzing current AI architectures, including AutoGPT, LangChain ReAct and OpenAI's ChatGPT, through a neurosymbolic lens, highlighting how their reliance on probabilistic inference contributes to inconsistent outputs. In response, DANA captures and applies domain expertise in both natural-language and symbolic forms, enabling more deterministic and reliable problem-solving behaviors. We implement a variant of DANA using Hierarchical Task Plans (HTPs) in the open-source OpenSSA framework. This implementation achieves over 90\% accuracy on the FinanceBench financial-analysis benchmark, significantly outperforming current LLM-based systems in both consistency and accuracy. Application of DANA in physical industries such as semiconductor shows that its flexible architecture for incorporating knowledge is effective in mitigating the probabilistic limitations of LLMs and has potential in tackling complex, real-world problems that require reliability and precision.

• The paper presents DANA as a neurosymbolic architecture that integrates domain-specific knowledge to overcome probabilistic inference issues in current LLMs.
• The methodology uses hierarchical task plans within the OpenSSA framework, achieving over 90% accuracy on the FinanceBench dataset.
• The approach offers practical improvements in industrial workflows, notably enhancing reliability in finance and semiconductor analyses.

Overview of DANA: Domain-Aware Neurosymbolic Agents

The paper presents DANA (Domain-Aware Neurosymbolic Agent), an innovative architecture aimed at enhancing consistency and accuracy in autonomous AI systems. This architecture mitigates the inherent probabilistic issues of LLMs by integrating domain-specific knowledge through a neurosymbolic approach, promising improvements in complex problem-solving tasks.

Current AI System Analysis

The paper begins with a comprehensive analysis of existing AI architectures such as AutoGPT, LangChain ReAct, and OpenAI's ChatGPT, highlighting their reliance on probabilistic inference. The authors argue that these systems frequently exhibit inconsistency and inaccuracy owing to their dependence on neural networks for both program creation and execution, without fully leveraging domain-specific knowledge.

DANA Architecture Principles

DANA's design revolves around three core principles:

1. Domain-Specific Knowledge: Emphasizes the integration of domain expertise in both natural-language and symbolic forms.
2. Neurosymbolic Integration: Utilizes both neural and symbolic structures to achieve more deterministic outcomes.
3. Knowledge Capture and Application: Models the processes of capturing and applying knowledge explicitly to ensure better problem-solving efficiency.

Implementation and Evaluation

The paper details the implementation of a variant of DANA using Hierarchical Task Plans (HTPs) in the OpenSSA framework. This implementation achieved over 90% accuracy on the FinanceBench dataset, significantly outperforming standard LLM-based systems. The use of a neurosymbolic approach allows DANA to maintain consistency and accuracy across complex tasks, particularly in domains requiring precise knowledge application, such as finance and semiconductor industries.

Practical and Theoretical Implications

Practically, the DANA architecture demonstrates its efficacy in industrial workflows, such as semiconductor etching, where precise analysis and recommendations are critical. Theoretically, it provides a framework for integrating domain-specific knowledge in AI systems, influencing future AI design paradigms.

Insights and Future Directions

The research suggests several avenues for further exploration:

• Automated Knowledge Capture: Developing systems to efficiently encode domain knowledge into AI systems promises scalability and enhanced accuracy.
• Hybrid Processing Models: Balancing neural and symbolic processing dynamically may offer improved adaptability across various problem domains.
• Cross-Domain Application: Examining the transferability of domain-specific knowledge across different areas could enhance system versatility.

Conclusion

DANA presents a compelling advancement in AI systems, bridging the gap between probabilistic flexibility and deterministic accuracy. By instilling domain-specific knowledge into the AI architecture, the model significantly enhances reliability in real-world applications. This work lays a foundation for future explorations into neurosymbolic AI, emphasizing the critical role of domain expertise in augmenting AI capabilities.