Algorithms and Conditional Lower Bounds for Planning Problems

We consider planning problems for graphs, Markov decision processes (MDPs), and games on graphs. While graphs represent the most basic planning model, MDPs represent interaction with nature and games on graphs represent interaction with an adversarial environment. We consider two planning problems where there are k different target sets, and the problems are as follows: (a) the coverage problem asks whether there is a plan for each individual target set, and (b) the sequential target reachability problem asks whether the targets can be reached in sequence. For the coverage problem, we present a linear-time algorithm for graphs and quadratic conditional lower bound for MDPs and games on graphs. For the sequential target problem, we present a linear-time algorithm for graphs, a sub-quadratic algorithm for MDPs, and a quadratic conditional lower bound for games on graphs. Our results with conditional lower bounds establish (i) model-separation results showing that for the coverage problem MDPs and games on graphs are harder than graphs and for the sequential reachability problem games on graphs are harder than MDPs and graphs; (ii) objective-separation results showing that for MDPs the coverage problem is harder than the sequential target problem.Comment: Accepted at ICAPS'1

Near-Linear Time Algorithms for Streett Objectives in Graphs and MDPs

The fundamental model-checking problem, given as input a model and a specification, asks for the algorithmic verification of whether the model satisfies the specification. Two classical models for reactive systems are graphs and Markov decision processes (MDPs). A basic specification formalism in the verification of reactive systems is the strong fairness (aka Streett) objective, where given different types of requests and corresponding grants, the requirement is that for each type, if the request event happens infinitely often, then the corresponding grant event must also happen infinitely often. All omega-regular objectives can be expressed as Streett objectives and hence they are canonical in verification. Consider graphs/MDPs with n vertices, m edges, and a Streett objectives with k pairs, and let b denote the size of the description of the Streett objective for the sets of requests and grants. The current best-known algorithm for the problem requires time O(min(n^2, m sqrt{m log n}) + b log n). In this work we present randomized near-linear time algorithms, with expected running time O~(m + b), where the O~ notation hides poly-log factors. Our randomized algorithms are near-linear in the size of the input, and hence optimal up to poly-log factors

Faster Algorithms for Mean-Payoff Parity Games

Graph games provide the foundation for modeling and synthesis of reactive processes. Such games are played over graphs where the vertices are controlled by two adversarial players. We consider graph games where the objective of the first player is the conjunction of a qualitative objective (specified as a parity condition) and a quantitative objective (specified as a meanpayoff condition). There are two variants of the problem, namely, the threshold problem where the quantitative goal is to ensure that the mean-payoff value is above a threshold, and the value problem where the quantitative goal is to ensure the optimal mean-payoff value; in both cases ensuring the qualitative parity objective. The previous best-known algorithms for game graphs with n vertices, m edges, parity objectives with d priorities, and maximal absolute reward value W for mean-payoff objectives, are as follows: O(nd+1 . m . w) for the threshold problem, and O(nd+2 · m · W) for the value problem. Our main contributions are faster algorithms, and the running times of our algorithms are as follows: O(nd-1 · m ·W) for the threshold problem, and O(nd · m · W · log(n · W)) for the value problem. For mean-payoff parity objectives with two priorities, our algorithms match the best-known bounds of the algorithms for mean-payoff games (without conjunction with parity objectives). Our results are relevant in synthesis of reactive systems with both functional requirement (given as a qualitative objective) and performance requirement (given as a quantitative objective)

LIPIcs

Graph games provide the foundation for modeling and synthesis of reactive processes. Such games are played over graphs where the vertices are controlled by two adversarial players. We consider graph games where the objective of the first player is the conjunction of a qualitative objective (specified as a parity condition) and a quantitative objective (specified as a meanpayoff condition). There are two variants of the problem, namely, the threshold problem where the quantitative goal is to ensure that the mean-payoff value is above a threshold, and the value problem where the quantitative goal is to ensure the optimal mean-payoff value; in both cases ensuring the qualitative parity objective. The previous best-known algorithms for game graphs with n vertices, m edges, parity objectives with d priorities, and maximal absolute reward value W for mean-payoff objectives, are as follows: O(nd+1 . m . w) for the threshold problem, and O(nd+2 · m · W) for the value problem. Our main contributions are faster algorithms, and the running times of our algorithms are as follows: O(nd-1 · m ·W) for the threshold problem, and O(nd · m · W · log(n · W)) for the value problem. For mean-payoff parity objectives with two priorities, our algorithms match the best-known bounds of the algorithms for mean-payoff games (without conjunction with parity objectives). Our results are relevant in synthesis of reactive systems with both functional requirement (given as a qualitative objective) and performance requirement (given as a quantitative objective)

Quasipolynomial Set-Based Symbolic Algorithms for Parity Games

Solving parity games, which are equivalent to modal $\mu$-calculus model checking, is a central algorithmic problem in formal methods. Besides the standard computation model with the explicit representation of games, another important theoretical model of computation is that of set-based symbolic algorithms. Set-based symbolic algorithms use basic set operations and one-step predecessor operations on the implicit description of games, rather than the explicit representation. The significance of symbolic algorithms is that they provide scalable algorithms for large finite-state systems, as well as for infinite-state systems with finite quotient. Consider parity games on graphs with $n$ vertices and parity conditions with $d$ priorities. While there is a rich literature of explicit algorithms for parity games, the main results for set-based symbolic algorithms are as follows: (a) an algorithm that requires $O(n^d)$ symbolic operations and $O(d)$ symbolic space; and (b) an improved algorithm that requires $O(n^{d/3+1})$ symbolic operations and $O(n)$ symbolic space. Our contributions are as follows: (1) We present a black-box set-based symbolic algorithm based on the explicit progress measure algorithm. Two important consequences of our algorithm are as follows: (a) a set-based symbolic algorithm for parity games that requires quasi-polynomially many symbolic operations and $O(n)$ symbolic space; and (b) any future improvement in progress measure based explicit algorithms imply an efficiency improvement in our set-based symbolic algorithm for parity games. (2) We present a set-based symbolic algorithm that requires quasi-polynomially many symbolic operations and $O(d \cdot \log n)$ symbolic space. Moreover, for the important special case of $d \leq \log n$, our algorithm requires only polynomially many symbolic operations and poly-logarithmic symbolic space.Comment: Published at LPAR-22 in 201

Faster Algorithms for Bounded Liveness in Graphs and Game Graphs

Graphs and games on graphs are fundamental models for the analysis of reactive systems, in particular, for model-checking and the synthesis of reactive systems. The class of ω-regular languages provides a robust specification formalism for the desired properties of reactive systems. In the classical infinitary formulation of the liveness part of an ω-regular specification, a "good" event must happen eventually without any bound between the good events. A stronger notion of liveness is bounded liveness, which requires that good events happen within d transitions. Given a graph or a game graph with n vertices, m edges, and a bounded liveness objective, the previous best-known algorithmic bounds are as follows: (i) O(dm) for graphs, which in the worst-case is O(n³); and (ii) O(n² d²) for games on graphs. Our main contributions improve these long-standing algorithmic bounds. For graphs we present: (i) a randomized algorithm with one-sided error with running time O(n^{2.5} log n) for the bounded liveness objectives; and (ii) a deterministic linear-time algorithm for the complement of bounded liveness objectives. For games on graphs, we present an O(n² d) time algorithm for the bounded liveness objectives

Geometry and field theory in multi-fractional spacetime

We construct a theory of fields living on continuous geometries with fractional Hausdorff and spectral dimensions, focussing on a flat background analogous to Minkowski spacetime. After reviewing the properties of fractional spaces with fixed dimension, presented in a companion paper, we generalize to a multi-fractional scenario inspired by multi-fractal geometry, where the dimension changes with the scale. This is related to the renormalization group properties of fractional field theories, illustrated by the example of a scalar field. Depending on the symmetries of the Lagrangian, one can define two models. In one of them, the effective dimension flows from 2 in the ultraviolet (UV) and geometry constrains the infrared limit to be four-dimensional. At the UV critical value, the model is rendered power-counting renormalizable. However, this is not the most fundamental regime. Compelling arguments of fractal geometry require an extension of the fractional action measure to complex order. In doing so, we obtain a hierarchy of scales characterizing different geometric regimes. At very small scales, discrete symmetries emerge and the notion of a continuous spacetime begins to blur, until one reaches a fundamental scale and an ultra-microscopic fractal structure. This fine hierarchy of geometries has implications for non-commutative theories and discrete quantum gravity. In the latter case, the present model can be viewed as a top-down realization of a quantum-discrete to classical-continuum transition.Comment: 1+82 pages, 1 figure, 2 tables. v2-3: discussions clarified and improved (especially section 4.5), typos corrected, references added; v4: further typos correcte

Komplexität von Well-Designed SPARQL

Abweichender Titel nach Übersetzung der Verfasserin/des VerfassersZusammenfassung in deutscher SpracheSPARQL Protocol and RDF Query Language (SPARQL) ist eine standardisierte Möglichkeit um Resource Description Framework (RDF) Daten aus dem Internet abzufragen. Ein Feature von SPARQL 1.0 ist der GRAPH Operator, der es erlaubt, mehrere lokale RDF Graphen in einer einzigen SPARQL Query zu verwenden. Mit der Zeit tauchten immer mehr SPARQL Schnittstellen im Internet auf und um jene auch ansprechen zu können, wurde der SERVICE Operator in der SPARQL 1.1 Federated Query extension eingeführt. Mit dem SERVICE Operator kann man, ähnlich dem GRAPH Operator, mehrere SPARQL Endpoints in einer Query benützen. Sowohl der GRAPH Operator als auch der SERVICE Operator werfen neue Probleme in der Komplexitätsanalyse von SPARQL auf. Die beiden Operatoren wurden bis jetzt noch nicht analysiert. Weil die Evaluierung von allgemeinen SPARQL Queries PSPACE-complete ist, beschränken wir unsere Beobachtungen auf well-designed SPARQL, wo das Evaluierungsproblem coNP-complete ist. Wenn man das well-designed SPARQL Fragment mit SERVICE und GRAPH erweitert, erhält man ein neues SPARQL Fragment welches eine gute Basis für unsere Komplexitätsanalyse darstellt. Wenn man allgemeines SPARQL um die beiden Operatoren erweitern würde, könnte es sein, dass die Komplexität der beiden Operatoren von diesem Fragment überschattet würden. Wir werden zeigen, dass die Komplexität des Evaluierungsproblems im well-designed Fragment, welches mit den Operatoren GRAPH und SERVICE erweitert wurde, coNP-complete ist. Wenn man den SERVICE operator in der Praxis benutzt, löst dieser neue Schwierigkeiten aus, die mit der Einführung mehrerer Notationen gelöst wurden. Der Unterschied dieser Notationen wurde bis jetzt noch nicht wirklich erforscht. Nachdem wir die Notationen eingeführt haben, werden wir sie diskutieren. Wir werden auch das Fragment weakly well-designed SPARQL behandeln. Es ist ein auf well-designed SPARQL basierendes Fragment, welches allerdings mächtiger ist, weil es einige Bedingungen des well-designed SPARQL Fragments schwächt.The SPARQL Protocol and RDF Query Language (SPARQL) presents a standardized way to query the growing amount of Resource Description Framework (RDF) data on the internet. A feature of SPARQL 1.0 is the GRAPH operator which is able to query multiple local RDF graphs in only a single query. To access the growing amounts of SPARQL endpoints available on the internet, the SERVICE operator was introduced in the SPARQL 1.1 Federated Query extension. The SERVICE operator is used to query several SPARQL endpoints in only a single query, similar to the GRAPH operator. Both the SERVICE and the GRAPH operator pose new problems in the complexity analysis of SPARQL, as the two operators have not yet been analyzed. Because the evaluation of general SPARQL patterns is PSPACE-complete we restrict our considerations to well-designed SPARQL, where the evaluation problem is coNP-complete. Extending the well-designed fragment of SPARQL with the SERVICE and GRAPH operators yields a new SPARQL fragment which is a good basis for a complexity analysis, as general SPARQLwouldcloudthecomplexityoftheSERVICEandGRAPHoperators. Itisshown that the evaluation problem in this fragment is coNP-complete. Using the SERVICE operator in practice elicits di-culties which were resolved by introducing various notions. Thesubtledi-erencebetweenthosenotionshasnotyetbeenfullyexplored. Afterde-ning the notions the di-erences between them will be discussed. Building upon well-designed SPARQL we will also cover weakly well-designed SPARQL which renders a powerful fragment by relaxing constraints of well-designed SPARQL.9

Algorithms and conditional lower bounds for planning problems

We consider planning problems for graphs, Markov decision processes (MDPs), and games on graphs. While graphs represent the most basic planning model, MDPs represent interaction with nature and games on graphs represent interaction with an adversarial environment. We consider two planning problems where there are k different target sets, and the problems are as follows: (a) the coverage problem asks whether there is a plan for each individual target set; and (b) the sequential target reachability problem asks whether the targets can be reached in sequence. For the coverage problem, we present a linear-time algorithm for graphs, and quadratic conditional lower bound for MDPs and games on graphs. For the sequential target problem, we present a linear-time algorithm for graphs, a sub-quadratic algorithm for MDPs, and a quadratic conditional lower bound for games on graphs. Our results with conditional lower bounds establish (i) model-separation results showing that for the coverage problem MDPs and games on graphs are harder than graphs and for the sequential reachability problem games on graphs are harder than MDPs and graphs; and (ii) objective-separation results showing that for MDPs the coverage problem is harder than the sequential target problem