Welcome to HEdLAMP

For an autonomous system to produce plans and decisions rationally using symbolic reasoning, it has to have explicit knowledge of its domain’s actions, resources, goals, objects, states and environment. A representation of such knowledge is called a domain model, and separation of the concerns of creating a domain model, and the creation of a planning algorithm, is the basis of what is termed domain independent planning.

Experience has shown that eliciting and validating domain models involves a great deal of expert time and effort. In fact acquiring, validating and maintaining a domain model for the purposes of automated reasoning is a key research challenge, and has long been a limiting factor in the exploitation of domain independent planning.

The aim of the HEdLAMP project is to work towards overcoming this research challenge, expressed in the research hypothesis:

Automatically learning and adapting an accurate and adequate domain model for the purposes of symbolic reasoning, in particular for the processes of automated planning, enables effective, sustained goal-directed behaviour for real time dynamic autonomous systems.