Conformant Planning as a Case Study of Incremental QBF Solving

We consider planning with uncertainty in the initial state as a case study of incremental quantified Boolean formula (QBF) solving. We report on experiments with a workflow to incrementally encode a planning instance into a sequence of QBFs. To solve this sequence of incrementally constructed QBFs, we use our general-purpose incremental QBF solver DepQBF. Since the generated QBFs have many clauses and variables in common, our approach avoids redundancy both in the encoding phase and in the solving phase. Experimental results show that incremental QBF solving outperforms non-incremental QBF solving. Our results are the first empirical study of incremental QBF solving in the context of planning and motivate its use in other application domains.Comment: added reference to extended journal article; revision (camera-ready, to appear in the proceedings of AISC 2014, volume 8884 of LNAI, Springer

SAT-Based Methods for Circuit Synthesis

Reactive synthesis supports designers by automatically constructing correct hardware from declarative specifications. Synthesis algorithms usually compute a strategy, and then construct a circuit that implements it. In this work, we study SAT- and QBF-based methods for the second step, i.e., computing circuits from strategies. This includes methods based on QBF-certification, interpolation, and computational learning. We present optimizations, efficient implementations, and experimental results for synthesis from safety specifications, where we outperform BDDs both regarding execution time and circuit size. This is an extended version of [2], with an additional appendix.Comment: Extended version of a paper at FMCAD'1

Complexity Classifications for logic-based Argumentation

We consider logic-based argumentation in which an argument is a pair (Fi,al), where the support Fi is a minimal consistent set of formulae taken from a given knowledge base (usually denoted by De) that entails the claim al (a formula). We study the complexity of three central problems in argumentation: the existence of a support Fi ss De, the validity of a support and the relevance problem (given psi is there a support Fi such that psi ss Fi?). When arguments are given in the full language of propositional logic these problems are computationally costly tasks, the validity problem is DP-complete, the others are SigP2-complete. We study these problems in Schaefer's famous framework where the considered propositional formulae are in generalized conjunctive normal form. This means that formulae are conjunctions of constraints build upon a fixed finite set of Boolean relations Ga (the constraint language). We show that according to the properties of this language Ga, deciding whether there exists a support for a claim in a given knowledge base is either polynomial, NP-complete, coNP-complete or SigP2-complete. We present a dichotomous classification, P or DP-complete, for the verification problem and a trichotomous classification for the relevance problem into either polynomial, NP-complete, or SigP2-complete. These last two classifications are obtained by means of algebraic tools

On Quantifier Shifting for Quantified Boolean Formulas

Since most currently available solvers for quantified Boolean formulas (QBFs) process only input formulas in prenex normal form, suitable translations are required for handling arbitrary formulas. In this paper, we propose a normal form translation incorporating a certain anti-prenexing step in order to obtain QBFs possessing quantifier prefixes such that the number of alternating quantifiers is never greater than the number of alternations obtained by using nondeterministic normal form translations based on usual quantifier shifting rules. Furthermore, our algorithm is deterministic. We show that anti-prenexing is beneficial in some cases for QBF-solvers which are able to process arbitrary QBFs, like BDD-based solvers. We illustrate this point by discussing some experimental results in this direction.

Application of artificial intelligence in Geodesy – A review of theoretical foundations and practical examples

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Artificial Intelligence (AI) is one of the key technologies in many of today's novel applications. It is used to add knowledge and reasoning to systems. This paper illustrates a review of AI methods including examples of their practical application in Geodesy like data analysis, deformation analysis, navigation, network adjustment, and optimization of complex measurement procedures. We focus on three examples, namely, a geo-risk assessment system supported by a knowledge-base, an intelligent dead reckoning personal navigator, and evolutionary strategies for the determination of Earth gravity field parameters. Some of the authors are members of IAG Sub-Commission 4.2 – Working Group 4.2.3, which has the main goal to study and report on the application of AI in Engineering Geodesy