The international planning competition (IPC) is an important driver for planning research.
The general goals of the IPC include pushing the state of the art in planning technology by …

Planning with preferences involves not only finding a plan that achieves the goal, it requires
finding a preferred plan that achieves the goal, where preferences over plans are specified …

Automated Planning is an old area of AI that focuses on the development of techniques for
finding a plan that achieves a given goal from a given set of initial states as quickly as …

We present a heuristic search approach to solve partial satisfaction planning (PSP)
problems. In these problems, goals are modeled as soft constraints with utility values, and …

In this paper, we address the problem of specifying and computing preferred plans using
rich, qualitative, user preferences. We propose a logical language for specifying preferences …

Temporally extended goals are critical to the specification of many real-world planning
problems. Such goals are typically specified in the subset of Linear Temporal Logic (LTL) …

This paper describes a method for planning with rich qualitative, temporally extended
preferences (QTEPs) using lookahead heuristics inspired by those employed in state-of-the …

Planning as Satisfiability is one of the most well-known and effective techniques for classical
planning: satplan has been the winning system in the deterministic track for optimal planners …

Planning algorithms are often applied by intelligent agents for achieving their goals. For the
plan creation, this kind of algorithm uses only an initial state definition, a set of actions, and a …

The field of AI Planning has undergone significant growth since the advent of the STRIPS
planner in 1971, fueled by the need to tackle an expanding array of complex problem …