Developing autonomous agents to deal with real-world problems is challenging, especially when developers are not necessarily specialists in artificial intelligence. This poses two key challenges regarding the interface of the programming with the developer, and the efficiency of the resulting agents. In this talk, we cover a recent work to tackle both challenges by modelling 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. The resulting architecture uses existing reinforcement learning techniques to bootstrap the agent's reasoning capabilities while allowing a developer to instruct the agent more directly using natural language as its programming interface. We empirically show the efficiency gains of natural language plans over a pure machine learning approach in the ScienceWorld environment.
- Speaker
- Alexandre Yukio Ichida
- Hosted by
- Prof Felipe Meneguzzi
- Venue
- Meston G05