Combining Event Calculus and Description Logic Reasoning via Logic Programming

Baumgartner, Peter

doi:10.1007/978-3-030-86205-3_6

Cited by 2 publications

(2 citation statements)

References 33 publications

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“…Bragaglia et al [41] developed a "reactive" version of the EC for dynamically extending a narrative represented so far. Baumgartner [28] has similar motivation but also incorporates description logic reasoning for added expressivity. The approach has been applied, among others, for anomaly detection in a food supply chain [29] and for maritime surveillance [197].…”

Section: Logic Programming and Event Calculusmentioning

confidence: 99%

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Baumgartner

Smith

Rana

et al. 2022

Preprint

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Data-driven decision making is becoming an integral part of manufacturing companies. Data is collected and commonly used to improve efficiency and produce high quality items for the customers. IoT-based and other forms of object tracking are an emerging tool for collecting movement data of objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over space and time. Movement data can provide valuable insights like process bottlenecks, resource utilization, effective working time etc. that can be used for decision making and improving efficiency. Turning movement data into valuable information for industrial management and decision making requires analysis methods. We refer to this process as movement analytics. The purpose of this document is to review the current state of work for movement analytics both in manufacturing and more broadly. We survey relevant work from both a theoretical perspective and an application perspective. From the theoretical perspective, we put an emphasis on useful methods from two research areas: machine learning, and logic-based knowledge representation. We also review their combinations in view of movement analytics, and we discuss promising areas for futuredevelopment and application. Furthermore, we touch on constraint optimization. From an application perspective, we review applications of these methods to movement analytics in a general sense and across various industries. We also describe currently available commercial off-the-shelf products for tracking in manufacturing, and we overview main concepts of digital twins and their applications.

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Section: Logic Programming and Event Calculusmentioning

confidence: 99%

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Baumgartner

Smith

Rana

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…As future work, it seems promising to combine these orthogonal techniques. The first author introduced SBTP in an earlier non-probabilistic Fusemate system [2,4] and combined it with description logic reasoning and the event calculus [3]. In this paper we adapt SBTP to a probabilistic setting and integrate query guidance into it.…”

Section: Introductionmentioning

confidence: 99%

Bottom-Up Stratified Probabilistic Logic Programming with Fusemate

Baumgartner,

Tartaglia

2023

Electron. Proc. Theor. Comput. Sci.

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This paper introduces the Fusemate probabilistic logic programming system. Fusemate's inference engine comprises a grounding component and a variable elimination method for probabilistic inference. Fusemate differs from most other systems by grounding the program in a bottom-up way instead of the common top-down way. While bottom-up grounding is attractive for a number of reasons, e.g., for dynamically creating distributions of varying support sizes, it makes it harder to control the amount of ground clauses generated. We address this problem by interleaving grounding with a query-guided relevance test which prunes rules whose bodies are inconsistent with the query. We present our method in detail and demonstrate it with examples that involve "time", such as (hidden) Markov models. Our experiments demonstrate competitive or better performance compared to a state-of-the art probabilistic logic programming system, in particular for high branching problems. Bottom-Up Stratified Probabilistic Logic Programming with FusemateBottom-up grounding. We propose a method for grounding the program in a bottom-up way as an alternative to the top-down approach described above. Our grounding is defined for a certain form of program stratification called SBTP, defined in Section 2 below. The result ground program is obtained via fixpoint computation along this stratification order. In each step of the fixpoint computation the program rules of the proper stratification level are grounded thereby also eliminating occurrences of default negation. This is advantageous for on-the-fly removal of redundant ground rules, which can be informed with the result program lower than the stratum of the current step. See below for details.The default negation operator is expressive and allows for negation over (implicitly) existentially quantified conjunctions of atoms. As an example consider this program (Fusemate uses ProbLog syntax):

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Combining Event Calculus and Description Logic Reasoning via Logic Programming

Cited by 2 publications

References 33 publications

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Bottom-Up Stratified Probabilistic Logic Programming with Fusemate

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