HyperBrain: Human-inspired Hypermedia Guidance using a Large Language Model

We present HyperBrain, a hypermedia client that autonomously navigates hypermedia environments to achieve user goals specified in natural language. To achieve this, the client makes use of a large language model to decide which of the available hypermedia controls should be used within a given application context. In a demonstrative scenario, we show the client's ability to autonomously select and follow simple hyperlinks towards a high-level goal, successfully traversing the hypermedia structure of Wikipedia given only the markup of the respective resources. We show that hypermedia navigation based on language models is effective, and propose that this should be considered as a step to create hypermedia environments that are used by autonomous clients alongside people.