This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning under uncertainty, sensor-based planning, visibility, decision-theoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and kinodynamic planning.

from Ltl formulas to deterministic !-automata. First, we show that the longest distance (length of the longest simple path) of the generator is also an important parameter, by giving an O(d log n)-space procedure to solve a Buchi game on a graph with n vertices and longest distance d. Then, for the Ltl

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implementation. It is well-known that ω-automata that stem from LTL formulas have special properties. In this paper, we exploit such a property in a new determinization procedure for these automata. Our procedure avoids the use of complicated tree structures as used in Safra’s procedure and it generates symbolic

non-deterministic Büchi automaton. Then a determinization procedure such as of Safra is performed yielding a deterministic ω-automaton. We present a direct translation of the (F,G)-fragment of LTL into deterministic ω-automata with no determinization procedure involved. Since our approach is tailored

optimizations are computed in time polynomial in the size of the automaton on which they are computed. We have applied our implementation to several examples and show a significant improvement over the straightforward implementation. Although our examples are still small, this work constitutes the first

universal co-Büchi word automaton. We show that such an observer can be made deterministic and that this deterministic observer has a nice structure which can be exploited by an incremental algorithm that manipulates antichains of game positions. We have implemented this new algorithm and our first results

Abstract. The translation of LTL formulas to nondeterministic automata involves an exponential blow-up, and so does the translation of nondeterministic automata to deterministic ones. This yields a 2 2O(n) upper bound for the translation of LTL to deterministic automata. A lower bound

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Recently a new method directly translating linear temporal logic (LTL) formulas to deterministic (generalized) Rabin automata was pro-posed by Javier Esparza and Jan Kret́ınsky ́ [1]. Compared to the existing approaches of constructing a non-deterministic Buechi-automaton in the first step