70,795 research outputs found
Practical Reasoning in DatalogMTL
DatalogMTL is an extension of Datalog with metric temporal operators that has
found an increasing number of applications in recent years. Reasoning in
DatalogMTL is, however, of high computational complexity, which makes reasoning
in modern data-intensive applications challenging. In this paper we present a
practical reasoning algorithm for the full DatalogMTL language, which we have
implemented in a system called MeTeoR. Our approach effectively combines an
optimised (but generally non-terminating) materialisation (a.k.a. forward
chaining) procedure, which provides scalable behaviour, with an automata-based
component that guarantees termination and completeness. To ensure favourable
scalability of the materialisation component, we propose a novel semina\"ive
materialisation procedure for DatalogMTL enjoying the non-repetition property,
which ensures that each specific rule application will be considered at most
once throughout the entire execution of the algorithm. Moreover, our
materialisation procedure is enhanced with additional optimisations which
further reduce the number of redundant computations performed during
materialisation by disregarding rules as soon as it is certain that they cannot
derive new facts in subsequent materialisation steps. Our extensive evaluation
supports the practicality of our approach.Comment: Under consideration in Theory and Practice of Logic Programming
(TPLP). arXiv admin note: text overlap with arXiv:2208.0710
PDDL2.1: An extension of PDDL for expressing temporal planning domains
In recent years research in the planning community has moved increasingly towards application of planners to realistic problems involving both time and many types of resources. For example, interest in planning demonstrated by the space research community has inspired work in observation scheduling, planetary rover ex ploration and spacecraft control domains. Other temporal and resource-intensive domains including logistics planning, plant control and manufacturing have also helped to focus the community on the modelling and reasoning issues that must be confronted to make planning technology meet the challenges of application. The International Planning Competitions have acted as an important motivating force behind the progress that has been made in planning since 1998. The third competition (held in 2002) set the planning community the challenge of handling time and numeric resources. This necessitated the development of a modelling language capable of expressing temporal and numeric properties of planning domains. In this paper we describe the language, PDDL2.1, that was used in the competition. We describe the syntax of the language, its formal semantics and the validation of concurrent plans. We observe that PDDL2.1 has considerable modelling power --- exceeding the capabilities of current planning technology --- and presents a number of important challenges to the research community
Temporal Data Modeling and Reasoning for Information Systems
Temporal knowledge representation and reasoning is a major research field in Artificial
Intelligence, in Database Systems, and in Web and Semantic Web research. The ability to
model and process time and calendar data is essential for many applications like appointment
scheduling, planning, Web services, temporal and active database systems, adaptive
Web applications, and mobile computing applications. This article aims at three complementary
goals. First, to provide with a general background in temporal data modeling
and reasoning approaches. Second, to serve as an orientation guide for further specific
reading. Third, to point to new application fields and research perspectives on temporal
knowledge representation and reasoning in the Web and Semantic Web
Multiple relaxations in temporal planning
CRIKEY is a planner that separates out the scheduling from the classical parts of temporal planning. This can be seen as a relaxation of the temporal information during the classical planning phase. Relaxations in planning are used to guide the search. However, the quality of the relaxation greatly affects the performance of the planner, and in some cases can lead the search into a dead end. This can happen whilst separating out the planning and scheduling problems, leading to the production of an unschedulable plan. CRIKEY can detect these cases and change the relaxation accordingly
On Relaxing Metric Information in Linear Temporal Logic
Metric LTL formulas rely on the next operator to encode time distances,
whereas qualitative LTL formulas use only the until operator. This paper shows
how to transform any metric LTL formula M into a qualitative formula Q, such
that Q is satisfiable if and only if M is satisfiable over words with
variability bounded with respect to the largest distances used in M (i.e.,
occurrences of next), but the size of Q is independent of such distances.
Besides the theoretical interest, this result can help simplify the
verification of systems with time-granularity heterogeneity, where large
distances are required to express the coarse-grain dynamics in terms of
fine-grain time units.Comment: Minor change
Real-time and Probabilistic Temporal Logics: An Overview
Over the last two decades, there has been an extensive study on logical
formalisms for specifying and verifying real-time systems. Temporal logics have
been an important research subject within this direction. Although numerous
logics have been introduced for the formal specification of real-time and
complex systems, an up to date comprehensive analysis of these logics does not
exist in the literature. In this paper we analyse real-time and probabilistic
temporal logics which have been widely used in this field. We extrapolate the
notions of decidability, axiomatizability, expressiveness, model checking, etc.
for each logic analysed. We also provide a comparison of features of the
temporal logics discussed
Approximate reasoning for real-time probabilistic processes
We develop a pseudo-metric analogue of bisimulation for generalized
semi-Markov processes. The kernel of this pseudo-metric corresponds to
bisimulation; thus we have extended bisimulation for continuous-time
probabilistic processes to a much broader class of distributions than
exponential distributions. This pseudo-metric gives a useful handle on
approximate reasoning in the presence of numerical information -- such as
probabilities and time -- in the model. We give a fixed point characterization
of the pseudo-metric. This makes available coinductive reasoning principles for
reasoning about distances. We demonstrate that our approach is insensitive to
potentially ad hoc articulations of distance by showing that it is intrinsic to
an underlying uniformity. We provide a logical characterization of this
uniformity using a real-valued modal logic. We show that several quantitative
properties of interest are continuous with respect to the pseudo-metric. Thus,
if two processes are metrically close, then observable quantitative properties
of interest are indeed close.Comment: Preliminary version appeared in QEST 0
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