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pro vyhledávání: '"Nau, Dana"'
Hierarchical Task Network (HTN) planning usually requires a domain engineer to provide manual input about how to decompose a planning problem. Even HTN-MAKER, a well-known method-learning algorithm, requires a domain engineer to annotate the tasks wi
Externí odkaz:
http://arxiv.org/abs/2404.06325
Publikováno v:
Artificial Intelligence, Elsevier, 2021, 299, pp.103523
In AI research, synthesizing a plan of action has typically used descriptive models of the actions that abstractly specify what might happen as a result of an action, and are tailored for efficiently computing state transitions. However, executing th
Externí odkaz:
http://arxiv.org/abs/2010.01909
We present new planning and learning algorithms for RAE, the Refinement Acting Engine. RAE uses hierarchical operational models to perform tasks in dynamically changing environments. Our planning procedure, UPOM, does a UCT-like search in the space o
Externí odkaz:
http://arxiv.org/abs/2003.03932
Humans interact with each other on a daily basis by developing and maintaining various social norms and it is critical to form a deeper understanding of how such norms develop, how they change, and how fast they change. In this work, we develop an ev
Externí odkaz:
http://arxiv.org/abs/1804.07406
Human societies around the world interact with each other by developing and maintaining social norms, and it is critically important to understand how such norms emerge and change. In this work, we define an evolutionary game-theoretic model to study
Externí odkaz:
http://arxiv.org/abs/1704.04720
Akademický článek
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Publikováno v:
In Artificial Intelligence October 2021 299
We discuss how to use evolutionary game theory (EGT) as a framework for studying how cultural dynamics and structural properties can influence the evolution of norms and behaviors within a society. We provide a brief tutorial on how EGT works, and di
Externí odkaz:
http://arxiv.org/abs/1606.02570
Autor:
Gelfand, Michele J *, Jackson, Joshua Conrad, Pan, Xinyue, Nau, Dana, Pieper, Dylan, Denison, Emmy, Dagher, Munqith, Van Lange, Paul A M, Chiu, Chi-Yue, Wang, Mo **
Publikováno v:
In The Lancet Planetary Health March 2021 5(3):e135-e144
In the field of Artificial Intelligence, traditional approaches to choosing moves in games involve the we of the minimax algorithm. However, recent research results indicate that minimizing may not always be the best approach. In this paper we summar
Externí odkaz:
http://arxiv.org/abs/1304.3445