Logic programs (LPs) and argumentation frameworks (AFs) are two declarative knowledge representation (KR) formalisms used for different reasoning tasks. The purpose of this study is interlinking two different reasoning components. To this end, we introduce two frameworks: LPAF and AFLP. The former enables to use the result of argumentation in AF for reasoning in LP, while the latter enables to use the result of reasoning in LP for arguing in AF. These frameworks are extended to bidirectional frameworks in which AF and LP can exchange information with each other. We also investigate their connection to several general KR frameworks from the literature.
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A Simple Extension of Answer Set Programs to Embrace Neural Networks (Extended Abstract)
The integration of low-level perception with high-level reasoning is one of the oldest problems in Artificial Intelligence. Today, the topic is revisited with the recent rise of deep neural networks. However, it is still not clear how complex and high-level reasoning, such as default reasoning, ontology reasoning, and causal reasoning, can be successfully computed by these approaches. The latter subject has been well-studied in the area of knowledge representation (KR), but many KR formalisms, including answer set programming (ASP), are logic-oriented and do not incorporate high-dimensional feature space as in deep learning, which limits the applicability of KR in many practical applications.
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- NSF-PAR ID:
- 10295440
- Editor(s):
- Ricca, Francesco et
- Date Published:
- Journal Name:
- Electronic proceedings in theoretical computer science
- ISSN:
- 2075-2180
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.4204/EPTCS.325
