AI planning is the task of finding a sequence of actions for a declaratively described system to reach some goal, while (optionally) optimizing some measures. In its classical version, the initial state is fully known and actions have deterministic, known effects. Besides its conceptual simplicity, classical planning is PSPACE-complete, both for satisficing and optimal planning and, consequently, a plethora of heuristic planners do exist, coping with the inherent complexity of the problem in different ways. Nevertheless, SAT based exact methods are competitive with heuristic methods for classical planning in many cases. Several declarative languages have been proposed for classical planning, such as STRIPS or PDDL, with PDDL being the de facto standard that most planners support, at least to some extent. PDDL formulations contain first-order formulas that are usually grounded in order to solve the planning problem, either with heuristic or exact methods, sometimes leading to a blow-up that makes the resulting problem instance intractable. With this in mind, in this paper we present a study on the translation of PDDL formulations to lifted representations in SMT, with the aim of obtaining more compact and, at the same time, efficient formulations for classical planning. We show that SMT formulations often outperform SAT based ones on hard-to-ground instances.

In this paper, we will show how to handle a new normal form called terse normal form (TNF), which is crucial to the development of a novel tableau method that solves realizability and synthesis for specifications expressed in a safety fragment of LTL. The construction of these tableaux is based on the conversion of LTL formulas into TNF, which is one of the most computationally expensive parts of the method. We will explain how to efficiently extract the relevant information required by the tableaux without having to compute the entire TNF of a safety formula. We present a correct algorithm for carrying out this task as well as its implementation.

We present a sound and complete algorithm for the detection of independent variables in linear temporal logic formulae. These formulae are often used to specify reactive systems. The algorithm is based on the use of model checkers.