LTLf satisfiability checking

We consider here Linear Temporal Logic (LTL) formulas interpreted over \emph{finite} traces. We denote this logic by LTLf. The existing approach for LTLf satisfiability checking is based on a reduction to standard LTL satisfiability checking. We describe here a novel direct approach to LTLf satisfiability checking, where we take advantage of the difference in the semantics between LTL and LTLf. While LTL satisfiability checking requires finding a \emph{fair cycle} in an appropriate transition system, here we need to search only for a finite trace. This enables us to introduce specialized heuristics, where we also exploit recent progress in Boolean SAT solving. We have implemented our approach in a prototype tool and experiments show that our approach outperforms existing approaches

Pure-Past Linear Temporal and Dynamic Logic on Finite Traces

LTLf and LDLf are well-known logics on finite traces. We review PLTLf and PLDLf, their pure- past versions. These are interpreted backward from the end of the trace towards the beginning. Because of this, we can exploit a foundational result on reverse languages to get an exponential improvement, wrt LTLf /LDLf, in computing the corresponding DFA. This exponential improvement is reflected in several forms sequential decision making involving temporal specifications, such as planning and decision problems in non-deterministic and non-Markovian domains. Interestingly, PLTLf (resp. PLDLf ) has the same expressive power as LTLf (resp. LDLf ), but transforming a PLTLf (resp. PLDLf ) formula into its equivalent in LTLf (resp. LDLf ) is quite expensive. Hence, to take advantage of the exponential improvement, properties of interest must be directly expressed in PLTLf /PLTLf

FOND planning for pure-past linear temporal logic goals

Recently, Pure-Past Temporal Logic (PPLTL) has proven highly effective in specifying temporally extended goals in deterministic planning domains. In this paper, we show its effectiveness also for fully observable nondeterministic (FOND) planning, both for strong and strong-cyclic plans. We present a notably simple encoding of FOND planning for PPLTL goals into standard FOND planning for final-state goals. The encoding only introduces few fluents (at most linear in the PPLTL goal) without adding any spurious action and allows planners to lazily build the relevant part of the deterministic automaton for the goal formula on-the-fly during the search. We formally prove its correctness, implement it in a tool called Plan4Past, and experimentally show its practical effectiveness

A New Tableau-based Satisfiability Checker for Linear Temporal Logic

Tableaux-based methods were among the first techniques proposed for Linear Temporal Logic satisfiability checking. The earliest tableau for LTL by [21] worked by constructing a graph whose path represented possible models for the formula, and then searching for an actual model among those paths. Subsequent developments led to the tree-like tableau by [17], which works by building a structure similar to an actual search tree, which however still has back-edges and needs multiple passes to assess the existence of a model. This paper summarizes theworkdoneonanewtool for LTL satisfiability checking based on a novel tableau method. The new tableau construction, which is very simple and easy to explain, builds an actually tree-shaped structure and it only requires a single pass to decide whether to accept a given branch or not. The implementation has been compared in terms of speed and memory consumption with tools implementing both existing tableau methods and different satisfiability techniques, showing good results despite the simplicity of the underlying algorithm

A tool for declarative Trace Alignment via automated planning

We present a tool, called TraceAligner, for solving Trace Alignment by first compiling into Planning and then solving it with any available cost-optimal planner. TraceAligner can produce different variants of the output Planning instance, each offering different degrees of readability and solution efficiency. The Planning instance is expressed in PDDL, the Planning Domain Definition Language. The tool can be easily extended and coupled with any planner taking PDDL as input language. A thorough experimental analysis has shown that the approach dramatically outperforms existing ad-hoc tools, thus making TraceAligner the best-performing tool for Trace Alignment with declarative specifications