@inproceedings{oai:niigata-u.repo.nii.ac.jp:00030878,
 author = {Gotou, Yoshiaki and Hagiwara, Takeshi and Sawamura, Hajime},
 book = {7th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy'11), 7th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy'11)},
 month = {Jul},
 note = {Argumentation is a leading principle both foundationally and functionally for agent-oriented computing where reasoning accompanied by communication plays an essential role in agent interaction. We constructed a simple but versatile neural network for neural network argumentation, so that it can decide which argumentation semantics (admissible, stable, semistable, preferred, complete, and grounded semantics) a given set of arguments falls into, and compute argumentation semantics via checking. In this paper, we are concerned with the opposite direction from neural network computation to symbolic argumentation/dialogue. We deal with the question how various argumentation semantics can have dialectical proof theories, and describe a possible answer to it by extracting or generating symbolic dialogues from the neural network computation under various argumentation semantics.},
 pages = {28--33},
 title = {Extracting Argumentative Dialogues from the Neural Network that Computes the Dungean Argumentation Semantics},
 volume = {7},
 year = {2011}
}