Reasoning for Smart Space Application: Comparing Three Reasoning Engines CLIPS, Jess and Win-prolog

Gilman, Ekaterina; Sánchez, Inmaculada; Saloranta, Timo; Riekki, Jukka

doi:10.1109/cit.2010.240

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…This system assigns comments to factors based on their performance indicators, as well as weather information. A rule-based system was selected as it provides a formal way to encode the expert knowledge, as well enough expressive power for end users and maintenance advantages for developers (Bikakis and Antoniou, 2010;Gilman et al, 2010). Driving coach uses SWI-Prolog system for these purposes.…”

Section: Object-level Functionalitymentioning

confidence: 99%

Personalised assistance for fuel-efficient driving

Gilman

Keskinarkaus

Tamminen

et al. 2015

Transportation Research Part C: Emerging Technologies

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a b s t r a c tRecent advances in technology are changing the way how everyday activities are performed. Technologies in the traffic domain provide diverse instruments of gathering and analysing data for more fuel-efficient, safe, and convenient travelling for both drivers and passengers. In this article, we propose a reference architecture for a context-aware driving assistant system. Moreover, we exemplify this architecture with a real prototype of a driving assistance system called Driving coach. This prototype collects, fuses and analyses diverse information, like digital map, weather, traffic situation, as well as vehicle information to provide drivers in-depth information regarding their previous trip along with personalised hints to improve their fuel-efficient driving in the future. The Driving coach system monitors its own performance, as well as driver feedback to correct itself to serve the driver more appropriately.

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Section: Object-level Functionalitymentioning

confidence: 99%

Personalised assistance for fuel-efficient driving

Gilman

Keskinarkaus

Tamminen

et al. 2015

Transportation Research Part C: Emerging Technologies

Self Cite

View full text Add to dashboard Cite

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“…Am ong other ad aptation ap proaches, the ru le-based approach is com m on [30], [43], [50]. Ru les provid e ad vantages of m od u larity, expressivity, and system m aintenance su pport [56]. Proprietary solu tions are d em onstrated by som e system s [44], [47], [49].…”

Section: State-of-the-art Analysismentioning

confidence: 99%

Towards User Support in Ubiquitous Learning Systems

Gilman

Milara

Cortés

et al. 2015

IEEE Trans. Learning Technol.

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New technologies enable novel types of learning activities that differ radically from traditional approach of visiting lectures and doing homework assignments. Namely, these technologies support transforming our everyday environments into learning environments. This concept is referred to in the literature as ubiquitous learning. Enriching ubiquitous learning systems with adaptive functionality facilitates personalization of learning activities by adapting them to learners' progress and situation. In this article, we identify needs of four user roles in ubiquitous learning systems, i.e. learner, instructor, developer, and researcher. We analyze the state of the art in ubiquitous learning and find that roles other than learners have not received much attention in the literature. Finally, we propose supporting different needs identified for four user roles by adding meta-level functionality to ubiquitous learning systems. This proposal adds self-introspective capabilities to such systems to serve their users better.

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Reasoning in Air Traffic Control Using Prolog

Liu

et al. 2012

Advances in Intelligent and Soft Computing

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Reasoning for Smart Space Application: Comparing Three Reasoning Engines CLIPS, Jess and Win-prolog

Cited by 4 publications

References 9 publications

Personalised assistance for fuel-efficient driving

Personalised assistance for fuel-efficient driving

Towards User Support in Ubiquitous Learning Systems

Reasoning in Air Traffic Control Using Prolog

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