Dynamic Domain Sizes in Temporal Probabilistic Relational Models.

Nils Finke; Tanya Braun; Marcel Gehrke; Ralf Möller

doi:10.32473/flairs.v34i1.128466

Dynamic Domain Sizes in Temporal Probabilistic Relational Models.

Nils Finke, Tanya Braun, Marcel Gehrke, Ralf Möller

Abstract

Probabilistic dynamic relational models (PDRMs) allow for an expressive, yet sparse and efficient representation of uncertain temporal (dynamic) and relational information with a fixed (static) set of domain objects (entities). While for different points in time, information about objects may differ, the set of objects under consideration is the same for all time points in standard PDRMs. Motivated by examples from a logistics application, in this paper we extend the theory of PDRMs with dynamically changing sets of domain objects. The paper introduces the semantics of so-called PD2RMs and analyses model management as well as query answering problems and algorithms.

Original language	English
Title of host publication	FLAIRS Conference
Publication date	2021
DOIs	https://doi.org/10.32473/flairs.v34i1.128466
Publication status	Published - 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 4 Quality Education
SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities
SDG 12 Responsible Consumption and Production
SDG 14 Life Below Water
SDG 15 Life on Land

Research Areas and Centers

Centers: Center for Artificial Intelligence Luebeck (ZKIL)
Research Area: Intelligent Systems

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10.32473/flairs.v34i1.128466

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title = "Dynamic Domain Sizes in Temporal Probabilistic Relational Models.",

abstract = "Probabilistic dynamic relational models (PDRMs) allow for an expressive, yet sparse and efficient representation of uncertain temporal (dynamic) and relational information with a fixed (static) set of domain objects (entities). While for different points in time, information about objects may differ, the set of objects under consideration is the same for all time points in standard PDRMs. Motivated by examples from a logistics application, in this paper we extend the theory of PDRMs with dynamically changing sets of domain objects. The paper introduces the semantics of so-called PD2RMs and analyses model management as well as query answering problems and algorithms.",

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AU - Gehrke, Marcel

AU - Möller, Ralf

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AB - Probabilistic dynamic relational models (PDRMs) allow for an expressive, yet sparse and efficient representation of uncertain temporal (dynamic) and relational information with a fixed (static) set of domain objects (entities). While for different points in time, information about objects may differ, the set of objects under consideration is the same for all time points in standard PDRMs. Motivated by examples from a logistics application, in this paper we extend the theory of PDRMs with dynamically changing sets of domain objects. The paper introduces the semantics of so-called PD2RMs and analyses model management as well as query answering problems and algorithms.

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DO - 10.32473/flairs.v34i1.128466

M3 - Conference contribution

BT - FLAIRS Conference

ER -

Dynamic Domain Sizes in Temporal Probabilistic Relational Models.

Abstract

UN SDGs

Research Areas and Centers

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