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Title:
The potential of logic programming as a computational tool to model morality
Publication date:
May 2015
Citation:
lnct15-ofai
Abstract:
We investigate the potential of logic programming (LP) to computationally model three morality aspects, viz., dual-process model, moral justification, and intention in moral permissibility. The research aims at developing an LP-based system with features needed in modeling these above mentioned morality aspects. We have currently co-developed two essential ingredients of the LP system, i.e., abduction and updating, benefiting from LP tabling features. They serve as the basis for our whole system, into which other reasoning facets will be integrated, to model the surmised morality aspects. We exemplify two applications pertaining moral updating and moral reasoning under uncertainty and detail their implementation. Moreover, we touch upon the potential of our ongoing studies of LP based cognitive features for the emergence of computational morality, in populations of agents enabled with the capacity for intention recognition, commitment and apology.
Book chapter
Authors:
Ari Saptawijaya
,
Luís Moniz Pereira
Editors:
Robert Trappl
Book title:
A Construction Manual for Robots’ Ethical Systems: Requirements, Methods, Implementations
Series:
Cognitive Technologies
Publisher:
Springer
Address:
-
Volume:
-
Pages:
-
ISBN:
-
ISSN:
-
Note:
-
Url address:
http://centria.di.fct.unl.pt/~lmp/publications/online-papers/ofai_book.pdf
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Plain text:
Ari Saptawijaya and Luís Moniz Pereira, The potential of logic programming as a computational tool to model morality, in: Robert Trappl (eds), A Construction Manual for Robots’ Ethical Systems: Requirements, Methods, Implementations, Cognitive Technologies, Springer, (http://centria.di.fct.unl.pt/~lmp/publications/online-papers/ofai_book.pdf), May 2015.
HTML:
<a href="/people/members/view.php?code=3296a2fcb759ac3f30bc313a41f955bc" class="author">Ari Saptawijaya</a> and <a href="/people/members/view.php?code=6175f826202ff877fba2ad77784cb9cb" class="author">Luís Moniz Pereira</a>, <b>The potential of logic programming as a computational tool to model morality</b>, in: Robert Trappl (eds), <u>A Construction Manual for Robots’ Ethical Systems: Requirements, Methods, Implementations</u>, Cognitive Technologies, <a href="http://www.springer.com" title="Link to external entity..." target="_blank" class="publisher">Springer</a>, (<a href="http://centria.di.fct.unl.pt/~lmp/publications/online-papers/ofai_book.pdf" target="_blank">url</a>), May 2015.
BibTeX:
@incollection {lnct15-ofai, author = {Ari Saptawijaya and Lu\'{\i}s Moniz Pereira}, editor = {Robert Trappl}, title = {The potential of logic programming as a computational tool to model morality}, booktitle = {A Construction Manual for Robots’ Ethical Systems: Requirements, Methods, Implementations}, series = {Cognitive Technologies}, publisher = {Springer}, url = {http://centria.di.fct.unl.pt/~lmp/publications/online-papers/ofai_book.pdf}, abstract = {We investigate the potential of logic programming (LP) to computationally model three morality aspects, viz., dual-process model, moral justification, and intention in moral permissibility. The research aims at developing an LP-based system with features needed in modeling these above mentioned morality aspects. We have currently co-developed two essential ingredients of the LP system, i.e., abduction and updating, benefiting from LP tabling features. They serve as the basis for our whole system, into which other reasoning facets will be integrated, to model the surmised morality aspects. We exemplify two applications pertaining moral updating and moral reasoning under uncertainty and detail their implementation. Moreover, we touch upon the potential of our ongoing studies of LP based cognitive features for the emergence of computational morality, in populations of agents enabled with the capacity for intention recognition, commitment and apology.}, keywords = {abduction, updates, argumentation, reactive behavior, deliberative reasoning, morality, emergence}, month = {May}, year = {2015}, }
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