2014 IEEE 26th International Conference on Tools With Artificial Intelligence 2014
DOI: 10.1109/ictai.2014.120
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Arg Teach - A Learning Tool for Argumentation Theory

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Cited by 1 publication
(2 citation statements)
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“…In this section, we consider update relations which give rise to the preferred and grounded semantics, and which are motivated by algorithms for computing these semantics that have been described by Dauphin and Schulz [9]. The algorithmic update relation for the grounded semantics first identifies the arguments which are only being attacked by arguments which are already labeled {out}, labels them as {in} and any argument they attack as {out}, and then repeats this process until no arguments can be further labeled, at which point it will label all remaining arguments as {undec}.…”
Section: Case Analysis: An Algorithmic Approach For Combining Preferrmentioning
confidence: 99%
See 1 more Smart Citation
“…In this section, we consider update relations which give rise to the preferred and grounded semantics, and which are motivated by algorithms for computing these semantics that have been described by Dauphin and Schulz [9]. The algorithmic update relation for the grounded semantics first identifies the arguments which are only being attacked by arguments which are already labeled {out}, labels them as {in} and any argument they attack as {out}, and then repeats this process until no arguments can be further labeled, at which point it will label all remaining arguments as {undec}.…”
Section: Case Analysis: An Algorithmic Approach For Combining Preferrmentioning
confidence: 99%
“…Lab 9 can be reached using mfg stage mfg grnd by first applying mfg stage several times to reduce the epistemic labels on d and e to {out} and {in} respectively and then applying item 2 of Definition 21 with the interface I := {d}, the set S := {a, b, c} and the following parallel framework F 9 2 :…”
Section: Defining New Semantics Via Mergingmentioning
confidence: 99%