Temporal representation and reasoning in artificial intelligence: A review

Pani, Ashis K.; Bhattacharjee, G. P.

doi:10.1016/s0895-7177(01)00049-8

Cited by 44 publications

(21 citation statements)

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“…Temporal imperfection has been studied in depth too and several researches are dedicated to (Pani and Bhattacharjee, 2001). One of our priority is to handle temporal imperfection; we plan to use a time model like MADS (Parent et al, 2006), OWL-Time (Hobbs and Pan, 2006), ISO standards (ISO, 2002), and event ontology mentioned above.…”

Section: Imperfectionmentioning

confidence: 99%

Virtual Leodium: From an Historical 3d City Scale Model to an Archaeological Information System

Pfeiffer

Carré

Delfosse

et al. 2013

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Virtual Leodium is an interdisciplinary project aiming to develop an archaeological information system based on a city scale model. The first part of the paper describes current project's achievements; the general methodology and the workflow of the project, namely the production and the modelling of archaeological data; the prototype functions of the ad hoc developed archaeological information system. The second part of the paper presents our new Virtual Leodium archaeological information modelling approach, which aims at consider, in a more comprehensive way, the complexity of archaeological information.

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Section: Imperfectionmentioning

confidence: 99%

Virtual Leodium: From an Historical 3d City Scale Model to an Archaeological Information System

Pfeiffer

Carré

Delfosse

et al. 2013

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…This section introduces the notation and basic definitions necessary for a detailed description of the objective of TSET À Miner, an algorithm proposed for mining frequent event-based sequences Each transaction is assigned with a time-stamped value represented in the system based on absolute dates [34]. To simplify the calculation, the temporal attribute of the first record, regarded as time origin, is subtracted from each of the temporal attributes, transforming the dataset into another equivalent one.…”

Section: Problem Definitionmentioning

confidence: 99%

A tree structure for event-based sequence mining

Guil

Marín

2012

Knowledge-Based Systems

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“…The goal of temporal reasoning is to formalize the notion of time and provide the temporal knowledge [15]. The fundamental temporal events and the relations between them are identified with time points and intervals.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…Some temporal imprecision methods are also investigated in e.g. [15]. Using the temporal constraint information, a fault can be diagnosed with specific algorithms, see [21,22].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Power System Fault Reasoning and Diagnosis Based on the Improved Temporal Constraint Network

Cui

Shi

Wang

2016

IEEE Trans. Power Delivery

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Temporal information is fundamental in model-based fault diagnosis (MBD), and the alarm processing problem is to interpret the alarm sequences to infer the type and time of fault event occurrences. There can be connected cycles or feedback loops in a real power system, but the fault reasoning methods for such cases are seldom considered in the literature. This paper provides an analytic model based on the improved temporal constraint network (ITCN). The reasoning method is dependent on time point and time distance information, with which the fault motivators (or actuators) and fault responders (or victims) can be identified. The system fault event reasoning and diagnosis are formulated as an optimization problem with the fault hypotheses (FHs) being tested. The calculation process consists of three steps: establishment of the objective function, determination of the fault propagation paths, and determination of the expected states under a given hypothesis. Finally, the proposed method is demonstrated via a power system example, which is verified to be successful on performance assessment and fault diagnosis.

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Temporal representation and reasoning in artificial intelligence: A review

Cited by 44 publications

References 50 publications

Virtual Leodium: From an Historical 3d City Scale Model to an Archaeological Information System

Virtual Leodium: From an Historical 3d City Scale Model to an Archaeological Information System

A tree structure for event-based sequence mining

Power System Fault Reasoning and Diagnosis Based on the Improved Temporal Constraint Network

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