NatBDI: Combining BDI Reasoning and Natural Language Inference for Autonomous Agents

Developing autonomous agents to deal with real-world problems is challenging, especially when developers are not necessarily specialists in artificial intelligence. Recent advances in machine learning to address natural language processing tasks are reaching performance levels suitable for practical applications, although these approaches rely on opaque and inscrutable models. This poses three key challenges: the interface of the programming with the developer, the efficiency of the resulting agents, and the scrutiny of their behaviour. Purpose: We tackle the challenge of developing autonomous agents over natural language environments in an efficient agent architecture that leverages recent developments in natural language processing, and the intuitive folk psychology abstraction of the beliefs, desires, intentions (BDI) architecture. Methods: This article introduces NatBDI, a new class of agent architecture that uses the BDI reasoning cycle with components driven by natural language processing. The resulting architecture handles natural language environments using a combination of language models and natural language inference to bootstrap the agent’s reasoning processing. Results: NatBDI agents leverage natural language components based on mental attitudes, enhancing intuitive understanding of the agent’s mental state. This allows a developer to instruct the agent more directly using a combination of controlled natural language structure and natural language knowledge as its programming interface.We empirically assess the efficiency gains of this combination while introducing a more intuitively programmed autonomous agent. Instructions in this interface substantially improve agent performance in the experimental scenario over a baseline agent created using a pure machine learning approach. Conclusion: The resulting architecture shows that combining learned policies with intuitively engineered domain knowledge yields substantial performance gains. We expect this class of agents to provide a powerful, yet intuitive, tool for agent-driven programming.