FPNES: fuzzy Petri net based expert system for bridges damage assessment

Liu, K.F.R.; Lee, J.; Chiang, Wei-Ling; Yang, Stephen J. H.

doi:10.1109/tai.1998.744858

Cited by 1 publication

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Approach [166,34,56,206,2,124,54,146,198,192,129,131,86,138,82,136,24,97,4,171,249,84,231,94,101,143,252,149,85,208,77,189,220,68,64,6,52,29,167,230,23,96,47,242,117,254,15,46,9,156,44,236,88,…”

Section: Selection Of Primary Studiesmentioning

confidence: 99%

A systematic review and taxonomy of explanations in decision support and recommender systems

Nunes

Jannach

2017

User Model User-Adap Inter

279

193

View full text Add to dashboard Cite

With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems.A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today's increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advicegiving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated.

show abstract