How does PLINTH solve these problems?

Firstly, PLINTH augments hypertext networks with semantic properties. The author can assign types and slots to nodes and links to mark the function of the nodes in the network and the structural, logical and rhetorical relations between them. This allows the author to: PLINTH then supports this augmented hypertext with rule-based intelligent navigation, to interactively compute a customised path through the document for the reader, based on the semantic properties and directed by commands and queries. This reduces the cognitive overhead problem even further. The author can write sets of navigation rules to perform different text retrieval or consultation functions for each view of the network. Here are some examples:
free browsing:
Here the navigator follows basic structural links like next and part-of in response to commands from the reader like next, previous, up, back, and so on, displaying the text of each node visited. Where a DR node is attached to a document node, the navigator can dip into the DR network and browsing rules for exploring this will be activated.

The consultation facilities of conventional expert systems for regulations are achieved using navigation based on logical typing of nodes and links, e.g. requirement, scope, necessary, sufficient, applies-to and so on. In this case the navigator follows the links in logical order, assigning truth values to nodes by asking the reader whether or not the conditions expressed in the node text are met. These truth values are combined according to the link types in order to decide which requirements apply and advise the reader on how to satisfy them. This method alleviates the expressiveness, parsimony and maintenance problems:

design rationale processing:
The process of building a DR network can be supported by navigation rules which not only traverse existing parts of the network but also allow the author to extend it in a principled manner.

What does PLINTH provide?