Freitext[edit]

Hybrid Answer Set Programming: Opportunities and Challenges[edit]

Hybrid Answer Set Programming: Opportunities and
Challenges
Thomas Eiter
TU Wien, Wien, Austria




1. Abstract
In the recent years, the interest in combining symbolic and sub-symbolic AI approaches has
been rapidly increasing. In particular neuro-symbolic AI, in which the two approaches have
been combined in a number of different ways, is in the center of attention. A natural question in
this context is how answer set programs, one of the main non-monotonic rule-based formalisms
in use today, may fit into this endeavor.
   Several authors have considered how to combine answer set programs with subsymbolic AI,
specifically with (deep) neural networks, at varying levels of integration in order to facilitate
semantics-enhanced applications of AI that build on subsymbolic AI such as scene classification,
object tracking, or visual question answering. In this talk, we shall consider hybrid answer
set programming approaches and explore opportunities and challenges for them. Notably,
combining answer set programs with alternative inference approaches is not novel and has
been extensively studied e.g. for logic-based ontologies. We shall also revisit lessons learnt from
such work for the ongoing work on hybrid answer set programming.




NMR 2022: 20th International Workshop on Non-Monotonic Reasoning, August 07–09, 2022, Haifa, Israel
" eiter@kr.tuwien.ac.at (T. Eiter)
~ https://informatics.tuwien.ac.at/people/thomas-eiter (T. Eiter)
� 0000-0001-6003-6345 (T. Eiter)
                                    © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
 CEUR
 Workshop
 Proceedings
               http://ceur-ws.org
               ISSN 1613-0073
                                    CEUR Workshop Proceedings (CEUR-WS.org)




                                                                                                        1
�

Hybrid Answer Set Programming: Opportunities and Challenges[edit]

Hybrid Answer Set Programming: Opportunities and
Challenges
Thomas Eiter
TU Wien, Wien, Austria




1. Abstract
In the recent years, the interest in combining symbolic and sub-symbolic AI approaches has
been rapidly increasing. In particular neuro-symbolic AI, in which the two approaches have
been combined in a number of different ways, is in the center of attention. A natural question in
this context is how answer set programs, one of the main non-monotonic rule-based formalisms
in use today, may fit into this endeavor.
   Several authors have considered how to combine answer set programs with subsymbolic AI,
specifically with (deep) neural networks, at varying levels of integration in order to facilitate
semantics-enhanced applications of AI that build on subsymbolic AI such as scene classification,
object tracking, or visual question answering. In this talk, we shall consider hybrid answer
set programming approaches and explore opportunities and challenges for them. Notably,
combining answer set programs with alternative inference approaches is not novel and has
been extensively studied e.g. for logic-based ontologies. We shall also revisit lessons learnt from
such work for the ongoing work on hybrid answer set programming.




NMR 2022: 20th International Workshop on Non-Monotonic Reasoning, August 07–09, 2022, Haifa, Israel
" eiter@kr.tuwien.ac.at (T. Eiter)
~ https://informatics.tuwien.ac.at/people/thomas-eiter (T. Eiter)
� 0000-0001-6003-6345 (T. Eiter)
                                    © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
 CEUR
 Workshop
 Proceedings
               http://ceur-ws.org
               ISSN 1613-0073
                                    CEUR Workshop Proceedings (CEUR-WS.org)




                                                                                                        1
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