dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Prakken, Henri | |
dc.contributor.author | Houwelingen, Leon van | |
dc.date.accessioned | 2022-01-21T00:00:18Z | |
dc.date.available | 2022-01-21T00:00:18Z | |
dc.date.issued | 2022 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/386 | |
dc.description.abstract | Argumentation is a reasoning approach in Artificial Intelligence, which is approached
by extension-based methods as well as gradual approaches. In the
literature one is often vague about the type of argument strength that is studied
and it is mostly approached avoiding structured argumentation. Thereby, assumptions
are made on abstract level that do not always hold at the structural
level.
In this work we answer the question how a semantics for dialectical argument
strength in structured approaches to argumentation can be developed and evaluated.
To that end two new semantics will be proposed using ASPIC+, one
for argumentation frameworks with only attacks, one for argumentation frameworks
with only supports. Both of these semantics will be evaluated by the
postulates proposed in the literature as well as by postulates proposed in this
work. Existing semantics will also be evaluated by the new postulates. | |
dc.description.sponsorship | Utrecht University | |
dc.language.iso | EN | |
dc.subject | We answer the question how a semantics for dialectical argument strength in structured approaches to argumentation can be developed and evaluated. To that end two new semantics will be proposed using ASPIC+, one for argumentation frameworks with only attacks, one for argumentation frameworks with only supports. Both of these semantics will be evaluated by the postulates proposed in the literature as well as by postulates proposed in this work. Existing semantics will also be evaluated. | |
dc.title | Gradual Acceptability for Structured Argumentation in ASPIC+ | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | Gradual Semantics; ASPIC+ | |
dc.subject.courseuu | Artificial Intelligence | |
dc.thesis.id | 1891 | |