Add to ORKGMost current partial-order planning systems are based on either the TWEAK or SNLP planning al-gorithms. Both planning algorithms are complete and correct. The SNLP algorithm distinguishes it-self from TWEAK in that it is also systematic, so it never generates redundant plans in its search space. This paper compares the two planning algorithms and shows that SNLP’S systematicity property does not imply that the planner is more efficient than TWEAK. To compare the two systems, we review the SNLP algorithm and describe how it can be easily transformed into TWEAK. Then we present a com-plexity analysis of each system and identify the fac-tors that determine the performance of the systems. Finally, we present results on a set of classic planning...
Recent trends in planning research have led to empirical comparison becoming com-monplace. The eld h...
In this document we will continue a line of research which focusses on reviving partial order planni...
When designing state-of-the-art, domain-independent planning systems, many decisions have to be made...
AbstractDespite the long history of classical planning, there has been very little comparative analy...
For many years, the intuitions underlying partial-order planning were largely taken for granted. Onl...
For many years, the intuitions underlying partial-order planning were largely taken for granted. Onl...
Planning has been an area of research in artificial intelligence for over four decades. It increases...
Although task reduction (HTN) planning historically preceded partial order (PO) planning, and is und...
We present a new method for partial order planning in the STRIPS/SNLP style. Our contribution center...
Despite the long history of classical planning, there has been very little comparative analysis of t...
Abstract. In this paper, we present FLAP, a partial-order planner that accurately applies the least-...
Automated planning is known to be computationally hard in the general case. Propositional planning i...
The principle of least commitment was embraced early in planning research. Hierarchical task network...
Recently, several researchers have demonstrated domains where partially-ordered planners outperform ...
a domain-independent planning algorithm that implements the family of heuristic search planners that...
Recent trends in planning research have led to empirical comparison becoming com-monplace. The eld h...
In this document we will continue a line of research which focusses on reviving partial order planni...
When designing state-of-the-art, domain-independent planning systems, many decisions have to be made...
AbstractDespite the long history of classical planning, there has been very little comparative analy...
For many years, the intuitions underlying partial-order planning were largely taken for granted. Onl...
For many years, the intuitions underlying partial-order planning were largely taken for granted. Onl...
Planning has been an area of research in artificial intelligence for over four decades. It increases...
Although task reduction (HTN) planning historically preceded partial order (PO) planning, and is und...
We present a new method for partial order planning in the STRIPS/SNLP style. Our contribution center...
Despite the long history of classical planning, there has been very little comparative analysis of t...
Abstract. In this paper, we present FLAP, a partial-order planner that accurately applies the least-...
Automated planning is known to be computationally hard in the general case. Propositional planning i...
The principle of least commitment was embraced early in planning research. Hierarchical task network...
Recently, several researchers have demonstrated domains where partially-ordered planners outperform ...
a domain-independent planning algorithm that implements the family of heuristic search planners that...
Recent trends in planning research have led to empirical comparison becoming com-monplace. The eld h...
In this document we will continue a line of research which focusses on reviving partial order planni...
When designing state-of-the-art, domain-independent planning systems, many decisions have to be made...