English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The paper addresses the problem of computing goal orderings, which is one ofthe longstanding issues in AI planning. It makes two new contributions. First,it formally defines and discusses two different goal orderings, which arecalled the reasonable and the forced ordering. Both orderings are defined forsimple STRIPS operators as well as for more complex ADL operators supportingnegation and conditional effects. The complexity of these orderings isinvestigated and their practical relevance is discussed. Secondly, twodifferent methods to compute reasonable goal orderings are developed. One ofthem is based on planning graphs, while the other investigates the set ofactions directly. Finally, it is shown how the ordering relations, which havebeen derived for a given set of goals G, can be used to compute a so-calledgoal agenda that divides G into an ordered set of subgoals. Any planner canthen, in principle, use the goal agenda to plan for increasing sets ofsubgoals. This can lead to an exponential complexity reduction, as the solutionto a complex planning problem is found by solving easier subproblems. Sinceonly a polynomial overhead is caused by the goal agenda computation, apotential exists to dramatically speed up planning algorithms as we demonstratein the empirical evaluation, where we use this method in the IPP planner.

On Reasonable and Forced Goal Orderings and their Use in an Agenda-Driven Planning Algorithm