An instance of the clp(x) scheme which allows to deal with temporal reasoning problems

In many applications oftemporal reasoning is necessary to express metric and symbolic temporal constraints among temporal objects whether they are points or intervals. In order to cope with these requirements different formalisms have been issued., those that allow to express symbolic temporal constraints by one hand, and others involvingmetric temporal constraints. AJthough this formalism are suitable to represent just sorne kind of problems, in many cases, it is necessary to handle and represent in the same framework both metric and symbolic constraints among temporal objects, whether they are point or interval. . Starting from the previous schemes, different formalisms to integrate metric and symbolic temporal constraints have been issued. A common limitation of these proposals is that none of them allows to represent disjunctive constraints involving a metric component and a symbolic one. This type of eonstraints arises for example in scheduling problems, where an activity must be performed beforé or after another activity, but considering the setting time of the used resources [lbáñez,92b]. Besides in.many planning applications, the formulation ofthe problem itself, must be expressed as logic formulas with a periQd of time associated. Therefore, a temporal reasoning system oriented to planning should be able to express both the logic and the temporal part in a same frame. Unfortunately, none of the approaches to integrate symbolic and tnetric temporal constraints allows to express the logic part of the problem. The main aim of this paper is to de.fine a temporal tool which allows to express and unify metric and symbolic temporal constraints among temporal objects (intervals and points). The temporal model proposed in this paper is based on intervals. However, as opposed to other formalisms, the duration of the intervals may be zero, and therefore temporal points are incIuded. In other words, the concept of temporal interval used in the literature (where the duration is strictly greater than zero), is generalized. Starting from the temporal model, a new operational framework oriented to the resolution oí the problems rather than focused to the representation oftemporal reasoning problems is defined. TIte proposed . operational frarn.ework was designed as a new instance oí the CLP(X) scheme [lbáñez,93] in which the computational domain is formed from temporal objects. Conceptually, the variables of the CLP(Temp) language have associated a finite set of pairs of value5 representing temporal intervals.Eje: 3er Workshop sobre Aspectos teóricos de la inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

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

Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey

Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems, and epidemiology. Representing complex networks as structures changing over time allow network models to leverage not only structural but also temporal patterns. However, as dynamic network literature stems from diverse fields and makes use of inconsistent terminology, it is challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a lot of attention in recent years for their ability to perform well on a range of network science tasks, such as link prediction and node classification. Despite the popularity of graph neural networks and the proven benefits of dynamic network models, there has been little focus on graph neural networks for dynamic networks. To address the challenges resulting from the fact that this research crosses diverse fields as well as to survey dynamic graph neural networks, this work is split into two main parts. First, to address the ambiguity of the dynamic network terminology we establish a foundation of dynamic networks with consistent, detailed terminology and notation. Second, we present a comprehensive survey of dynamic graph neural network models using the proposed terminologyComment: 28 pages, 9 figures, 8 table

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

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