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 exploration 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.

Abstract. Time modeling is a crucial feature in many application domains. However, temporal information often is not crisp, but is uncertain, subjective and vague. This is particularly true when representing historical information, as historical accounts are inherently imprecise. Similarly, we conjecture that in the Semantic Web representing uncertain temporal information will be a common requirement. Hence, existing approaches for temporal modeling based on crisp representation of time cannot be applied to these advanced modeling tasks. To overcome these difficulties, in this paper we present fuzzy interval-based temporal model capable of representing imprecise temporal knowledge. Our approach naturally subsumes existing crisp temporal models, i.e. crisp temporal relationships are intuitively represented in our system. Apart from presenting the fuzzy temporal model, we discuss how this model is integrated with the ontology model to allow annotating ontology definitions with time specifications. 1

In this paper we propose a propositional temporal language based on fuzzy temporal constraints which turns out to be expressive enough for domains like many coming from medicine where knowledge is of propositional nature and an explicit handling of time, imprecision and uncertainty are required. The language is provided with a natural possibilistic semantics to account for the uncertainty issued by the fuzziness of temporal constraints. We also present an inference system based on specific rules dealing with the temporal constraints and a general fuzzy modus ponens rule whereby behaviour is shown to be sound. The analysis of the different choices as fuzzy operators leads us to identify the well-known Lukasiewicz implication as very appropriate to define the notion of possibilistic entailment, an essential element of our inference system.

Temporal Constraint Networks are a well-defined, natural and efficient formalism for representing temporal knowledge based on metric temporal constraints. They support the representation of both metric and some qualitative temporal relations and are provided with efficient algorithms based on CSP techniques. Recently, a generalization based on fuzzy sets has been proposed in order to cope with vagueness in temporal relations. In this paper we generalize some earlier definitions for Fuzzy Temporal Constraint Networks, we identify and define "interesting" queries in a fuzzy temporal constraint network, and explore a method for efficiently computing them in a specific case. Further analysis of some measures on possibility distributions turns out to be fundamental in order to precisely determine some of these queries. We discuss the advantages and shortcomings of various choices and propose specific alternatives which satisfactorily avoid the problems of previous proposals. The res...

Model-based diagnosis (MBD) tackles the problem of troubleshooting systems starting from a description of their structure and function (or behavior). Time is a fundamental dimension in MBD: the behavior of most systems is time-dependent in one way or another. Temporal MBD, however, is a difficult task and indeed many simplifying assumptions have been adopted in the various approaches in the literature. These assumptions concern different aspects such as the type and granularity of the temporal phenomena being modeled, the definition of diagnosis, the ontology for time being adopted. Unlike the atemporal case, moreover, there is no general "theory" of temporal MBD which can be used as a knowledge-level characterization of the problem. In this paper we present a general characterization of temporal model-based diagnosis. We distinguish between different temporal phenomena that can be taken into account in diagnosis and we introduce a modeling language which can capture all such phenomena...

In this paper, we focus on an application and extension of Artificial Intelligence temporal reasoning techniques in order to represent and reason with temporal constraints in clinical guidelines. Particular attention is dedicated to the treatment of repeated (periodic) events, which play a major role in clinical therapies. We also discuss some limitations of our current approach, highlighting possible future enhancements. The work in this paper has been developed in the GLARE project, meant to realize a prototype of a domain-independent manager of clinical guidelines. The GLARE system has been built in cooperation with Azienda Ospedaliera S. Giovanni Battista of Turin, and has been successfully tested on different clinical domains.

Abstract. In this paper, we describe EventNet, a toolkit for inferring temporal relations between Commonsense events. It comprises 10,000 nodes and 30,000 temporal links mined from the Openmind Commonsense Knowledge Base. It enables applications to deduce "obvious " (to people) temporal relations between commonly occurring events, for example: First, you wake up, then you can leave the house in the morning. The temporal relation might be one of cause and effect, of action/goal or prerequisite relations, or simply that they tend to follow each other in a commonly occurring “script”. In addition, the algorithm has some built-in heuristics to infer when its information is not enough. It then finds semantically similar nodes to dynamically search the knowledge base. EventNet has been used in projects such as an intelligent kitchen, and in intelligent interfaces for consumer electronics devices. 1

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This paper proposes a logic for causal based on event trees. Event trees provide a natural and familiar framework for probability and decision theory, but they lack the modularity that would make them convenient models for a logic. Our logic overcomes this deficiency by elaborating the idea of an event tree to that of an event space. This is a space containing situations or instantaneous events, which are related by relations of precedence, refinement, and impliation. 1

An extension of the Horn clause logic programming language (PROLOG), called Horn Temporal Reference Language (HTRL), suitable for temporal reasoning is presented in this paper. The syntax of the HTRL language is given and its informal semantics is briefly presented. An implementation through the transformation of an HTRL program to an equivalent Constraint Logic Program is also briefly presented. Keywords Temporal reasoning, logic programming, constraint logic programming, uncertainty. 1 Introduction The problem of representing time depended information and reasoning about time has become an issue of great concern for many researchers in Artificial Intelligence during the last few years [8, 10]. Temporal Logics[6] have found application in many domains, such as, planning, temporal deductive data bases, verification of concurrent systems, VLSI design, etc. Some work has also been reported in Constraint Logic Programming (CLP) and its application to Temporal Reasoning [2, 3, 5]. Man...