The Tractability of Model-checking for LTL: The Good, the Bad, and the Ugly Fragments

Bauland, Michael; Mundhenk, Martin; Schneider, Thomas; Schnoor, Henning; Schnoor, Ilka; Vollmer, Heribert

doi:10.1016/j.entcs.2009.02.041

Cited by 20 publications

(42 citation statements)

References 13 publications

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“…For example, we might want to stipulate the a 7 For example, suppose that there is a set Si of initial states for every system S, T designates the system's reachability or, synonymously, temporal accessibility relation, and T * designates the reflexive transitive closure of T. Then, S is induced by S in the sense that S := { s | there is si ∈ Si such that si T * s }. 8 for all s ∈ S, there is s ∈ S s.t. Doxastic accessibility D a ⊆ E a can be defined from E a by weakening the reflexivity of E a to seriality as appropriate for the considered application.…”

Section: Definition 1 (Framework) Letmentioning

confidence: 99%

“…Table 2. Examples of agent correctness -algorithm, game-rule, and protocol compliance -good book-keeping in audit and control of transactions -fairness in the scheduling of legitimately requested services -liveness in the sense of absence of crash, e.g., that the agent, say a, has always eventually responded to a ping from some other agent, say b, which can be expressed with a Linear-Temporal Logic [28,27,70,8] formula as follows:…”

Section: Motivationmentioning

confidence: 99%

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Computer-aided decision-making with trust relations and trust domains (cryptographic applications)

Kramer

Goré

Okamoto

2012

Journal of Logic and Computation

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Abstract. We propose generic declarative definitions of individual and collective trust relations between interacting agents and agent collections, and trust domains of trust-related agents in distributed systems. Our definitions yield (1) (in)compatibility, implicational, and transitivity results for trust relationships, including a Datalog-implementability result for their logical structure; (2) computational complexity results for deciding potential and actual trust relationships and membership in trust domains; (3) a positive (negative) compositionality result for strong (weak) trust domains; (4) a computational design pattern for building up strong trust domains; and (5) a negative scalability result for trust domains in general. We instantiate our generic trust concepts in five major cryptographic applications of trust, namely: Access Control, Trusted Third Parties, the Web of Trust, Public-Key Infrastructures, and Identity-Based Cryptography. We also show that accountability induces trust. Our defining principle for weak and strong trust (domains) is (common) belief in and (common) knowledge of agent correctness, respectively.

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Section: Definition 1 (Framework) Letmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Computer-aided decision-making with trust relations and trust domains (cryptographic applications)

Kramer

Goré

Okamoto

2012

Journal of Logic and Computation

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“…These results provide a (partial) account for the polytomous complexity classifications of problems parametrized by the set of available Boolean connectives: the complexity of the satisfiability problem was, for instance, shown to be NP-complete if x y can be composed from the available Boolean connectives, and solvable in logspace in all other cases [13]. Further results include a variety of problems in propositional logic [5,19], modal logics [1], temporal logics [2][3][4]16], their hybrid variants [14,15], and nonmonotonic logics [6,11,23].…”

Section: Introductionmentioning

confidence: 99%

On the applicability of Postʼs lattice

Thomas

2012

Information Processing Letters

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“…Several approaches explore the computational properties of model checking and several algorithmic subclasses have been identified that are characterized by which (modal) operators are employed [29]. Model checking of manipulation tasks specified in the LTL-like logic QSL benefits from two characteristics:…”

Section: Reasoning With Qslmentioning

confidence: 99%

Leveraging Qualitative Reasoning to Learning Manipulation Tasks

Wolter

Kirsch

2015

Robotics

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Learning and planning are powerful AI methods that exhibit complementary strengths. While planning allows goal-directed actions to be computed when a reliable forward model is known, learning allows such models to be obtained autonomously. In this paper we describe how both methods can be combined using an expressive qualitative knowledge representation. We argue that the crucial step in this integration is to employ a representation based on a well-defined semantics. This article proposes the qualitative spatial logic QSL, a representation that combines qualitative abstraction with linear temporal logic, allowing us to represent relevant information about the learning task, possible actions, and their consequences. Doing so, we empower reasoning processes to enhance learning performance beyond the positive effects of learning in abstract state spaces. Proof-of-concept experiments in two simulation environments show that this approach can help to improve learning-based robotics by quicker convergence and leads to more reliable action planning.Keywords: qualitative spatial reasoning; robot learning; AI robotics MotivationRobotic applications have evolved considerably, yet they still lack the efficiency, flexibility and adaptability that is needed to improve our everyday life as versatile service robots. Planning and learning Robotics 2015, 4 254 are the two major paradigms employed to make a robot act intelligently. Planning is a form of symbolic reasoning applied on various levels of abstraction. Planning is effective whenever reliable forward models of the robot and its environment are available. Learning is a powerful method to generate such models from data. However, it requires carefully handcrafted state space representations to converge to high-quality models in reasonable time. Neither planning nor learning on their own suffice to meet the demands of versatile service robots. By integrating both paradigms one can benefit from their respective strengths. So far, a true integration of learning and planning has not been achieved that allows a robot to acquire new skills efficiently in a truly autonomous way.Our work is motivated by this basic question of how to integrate learning and planning, leveraging their individual strengths to produce reliable and versatile robot behavior. We propose a qualitative reasoning framework as the glue between those two paradigms. Qualitative reasoning lifts information, e.g., obtained by perception, to a knowledge level, enabling us to integrate it with common sense knowledge and to use available reasoning techniques [1][2][3]. The qualitative representation serves two purposes: (1) to make a planning or action selection process with learned models more efficient; and (2) to control selection of learning samples in order to make a learning process more reliable and to obtain high-quality models with little manual design effort. We present two experimental studies in simulation for a specific robot task: to throw an object to a specific destination. The results ind...

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The Tractability of Model-checking for LTL: The Good, the Bad, and the Ugly Fragments

Cited by 20 publications

References 13 publications

Computer-aided decision-making with trust relations and trust domains (cryptographic applications)

Computer-aided decision-making with trust relations and trust domains (cryptographic applications)

On the applicability of Postʼs lattice

Leveraging Qualitative Reasoning to Learning Manipulation Tasks

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