Add to ORKGAbstract. For many classical planning domains, the computationalcomplexity of non-optimal and optimal planning is known. However, little is known about the area in between the two extremes of findingsome plan and finding optimal plans. In this contribution, we provide a complete classification of the propositional domains from the firstfour International Planning Competitions with respect to the approximation classes PO, PTAS, APX, poly-APX, and NPO. 1 INTRODUCTION Considering the important role that benchmark domains such asL OGISTICS and SATELLITE play in evaluating the performance ofclassical planning algorithms, comparatively little is known abou
Planning as Satisfiability is one of the most well-known and effective technique for classical plann...
When designing state-of-the-art, domain-independent planning systems, many decisions have to be made...
We introduce a width parameter that bounds the complexity of classical planning problems and domains...
AbstractThe efficiency of AI planning systems is usually evaluated empirically. For the validity of ...
In a field of research about general reasoning mechanisms, it is essential to have appropriate bench...
There are two complementary ways to evaluate planning algorithms: performance on benchmark problems ...
Although even propositional STRIPS planning is a hard problem in general, many instances of the prob...
Benchmark suite for oversubscription planning. The benchmarks were created in a similar fashion to ...
Automated planning is known to be computationally hard in the general case. Propositional planning i...
Recent trends in planning research have led to empirical comparison becoming com-monplace. The eld h...
When designing state-of-the-art, domain-independent planning systems, many decisions have to be made...
In a field of research about general reasoning mechanisms, it is essential to have appropriate bench...
When designing state-of-the-art, domain-independent plan-ning systems, many decisions have to be mad...
These days, propositional planning can be considered a quite well-understood problem. Good algorith...
Algorithms are usually shown to be correct on paper, but bugs in their implementations can still lea...
Planning as Satisfiability is one of the most well-known and effective technique for classical plann...
When designing state-of-the-art, domain-independent planning systems, many decisions have to be made...
We introduce a width parameter that bounds the complexity of classical planning problems and domains...
AbstractThe efficiency of AI planning systems is usually evaluated empirically. For the validity of ...
In a field of research about general reasoning mechanisms, it is essential to have appropriate bench...
There are two complementary ways to evaluate planning algorithms: performance on benchmark problems ...
Although even propositional STRIPS planning is a hard problem in general, many instances of the prob...
Benchmark suite for oversubscription planning. The benchmarks were created in a similar fashion to ...
Automated planning is known to be computationally hard in the general case. Propositional planning i...
Recent trends in planning research have led to empirical comparison becoming com-monplace. The eld h...
When designing state-of-the-art, domain-independent planning systems, many decisions have to be made...
In a field of research about general reasoning mechanisms, it is essential to have appropriate bench...
When designing state-of-the-art, domain-independent plan-ning systems, many decisions have to be mad...
These days, propositional planning can be considered a quite well-understood problem. Good algorith...
Algorithms are usually shown to be correct on paper, but bugs in their implementations can still lea...
Planning as Satisfiability is one of the most well-known and effective technique for classical plann...
When designing state-of-the-art, domain-independent planning systems, many decisions have to be made...
We introduce a width parameter that bounds the complexity of classical planning problems and domains...