6 research outputs found
Generating Instructions at Different Levels of Abstraction
When generating technical instructions, it is often convenient to describe
complex objects in the world at different levels of abstraction. A novice user
might need an object explained piece by piece, while for an expert, talking
about the complex object (e.g. a wall or railing) directly may be more succinct
and efficient. We show how to generate building instructions at different
levels of abstraction in Minecraft. We introduce the use of hierarchical
planning to this end, a method from AI planning which can capture the structure
of complex objects neatly. A crowdsourcing evaluation shows that the choice of
abstraction level matters to users, and that an abstraction strategy which
balances low-level and high-level object descriptions compares favorably to
ones which don't.Comment: Accepted COLING 2020 long pape
Bringing Order to Chaos – A Compact Representation of Partial Order in SAT-Based HTN Planning
HTN planning provides an expressive formalism to model complex application domains. It has been widely used in realworld applications. However, the development of domainindependent planning techniques for such models is still lacking behind. The need to be informed about both statetransitions and the task hierarchy makes the realisation of search-based approaches difficult, especially with unrestricted partial ordering of tasks in HTN domains. Recently, a translation of HTN planning problems into propositional logic has shown promising empirical results. Such planners benefit from a unified representation of state and hierarchy, but until now require very large formulae to represent partial order. In this paper, we introduce a novel encoding of HTN Planning as SAT. In contrast to related work, most of the reasoning on ordering relations is not left to the SAT solver, but done beforehand. This results in much smaller formulae and, as shown in our evaluation, in a planner that outperforms previous SAT-based approaches as well as the state-of-the-art in search-based HTN planning