We present a declarative language, PP, for the high-level specification of preferences between possible solutions (or trajectories) of a planning problem. This novel language allows users to elegantly express non-trivial, multi-dimensional preferences and priorities over such preferences. The semantics of PP allows the identification of most preferred trajectories for a given goal. We also provide an answer set programming implementation of planning problems with PP preferences.
introduced a framework for planning with action costs using logic programming @cite_3 . The focus of their proposal is to express certain classes of quantitative preferences. Each action is assigned an integer cost, and plans with the minimal cost are considered to be optimal. Costs can be either static or relative to the time step in which the action is executed. @cite_3 also presents the encoding of different preferences, such as shortest plan and the cheapest plan. Our approach also emphasizes the use of logic programming, but differs in several aspects. Here, we develop a for preference representation. Our language can express the preferences discussed in @cite_3 , but it is more high-level and flexible than the action costs approach. The approach in @cite_3 also does not allow the use of fully general dynamic preferences. On the other hand, while we only consider planning with complete information, @cite_3 deal with planning in the presence of incomplete information and non-deterministic actions.
@cite_3
1633032608
Recently, planning based on answer set programming has been proposed as an approach towards realizing declarative planning systems. In this paper, we present the language κc, which extends the declarative planning language κ by action costs. κc provides the notion of admissible and optimal plans, which are plans whose overall action costs are within a given limit resp. minimum over all plans (i.e., cheapest plans). As we demonstrate, this novel language allows for expressing some nontrivial planning tasks in a declarative way. Furthermore, it can be utilized for representing planning problems under other optimality criteria, such as computing "shortest" plans (with the least number of steps), and refinement combinations of cheapest and fastest plans. We study complexity aspects of the language κc and provide a transformation to logic programs, such that planning problems are solved via answer set programming. Furthermore, we report experimental results on selected problems. Our experience is encouraging that answer set planning may be a valuable approach to expressive planning systems in which intricate planning problems can be naturally specified and solved.