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Gal A. Kaminka. Curing Robot Autism: A Challenge. In Proceedings
of the Twelfth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-13), 2013.
Winner:
Best challenge paper award.
Almost all robots are autistic; very few humans are. Out of the box, robots generally do not behave correctly insocial settings (involving humans, or other agents). Most researchers treat this challenge behaviorally, bysuperficially tacking task- and domain- specific social behavior onto functioning individual robots. These rulesare built once, and applied once. In contrast, I posit that we can build better socially-capable robots by relyingon general social intelligence building blocks, built into the brains of robots, rather than grafted on per mission:built once, applied everywhere. I challenge the autonomous agents community to synthesize the computationalbuilding blocks underlying social intelligence, and to apply them in concrete robot and agent systems. I argue thatour field is in a unique position to do this, in that our community intersects with computer science, behavioral andsocial sciences, robotics, and neuro-science. Thus we can bring to bear a breadth of knowledge and understandingwhich cannot be matched in other related fields. To lend credibility for our ability to carry out this challenge, Iwill demonstrate that we have carried out similar tasks in the past (though at a smaller scale). I conclude with a sample of some open questions for research, raised by this challenge.
@InProceedings{aamas13challenge,
author = {Gal A. Kaminka},
title = {Curing Robot Autism: A Challenge},
booktitle = AAMAS-13,
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year = {2013},
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abstract = {
Almost all robots are autistic; very few humans are. Out of the box, robots generally do not behave correctly in
social settings (involving humans, or other agents). Most researchers treat this challenge \textit{behaviorally}, by
superficially tacking task- and domain- specific social behavior onto functioning individual robots. These rules
are built once, and applied once. In contrast, I posit that we can build better socially-capable robots by relying
on general social intelligence building blocks, built into the brains of robots, rather than grafted on per mission:
\textit{built once, applied everywhere}. I challenge the autonomous agents community to synthesize the computational
building blocks underlying social intelligence, and to apply them in concrete robot and agent systems. I argue that
our field is in a unique position to do this, in that our community intersects with computer science, behavioral and
social sciences, robotics, and neuro-science. Thus we can bring to bear a breadth of knowledge and understanding
which cannot be matched in other related fields. To lend credibility for our ability to carry out this challenge, I
will demonstrate that we have carried out similar tasks in the past (though at a smaller scale). I conclude with a sample of some open questions for research, raised by this challenge.
},
wwwnote = {{\bf Winner: Best challenge paper award.}},
OPTkeywords = {},
}
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