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Abstract: To perform rational decision-making, autonomous agents need considerable computational resources. In multi-agent settings, when other agents are present in the environment, these demands are even more severe. We investigate ways in which the agent's knowledge and the results of deliberative decision-making can be compiled to reduce the complexity of decision-making procedures and to save time in urgent situations. We use machine learning algorithms to compile decision-theoretic deliberations... (Update)
Context of citations to this paper: More
.... modelling method has also been used for representing uncertain knowledge and communicative behaviour in coordinated defence campaigns [6]. Our multi agent flight simulations have involved either closed loop or pilot in the loop simulations in the Air Operations Simulation...
...simplified in a 26 number of ways. For lack of space, we briefly list some of the most intuitive methods (see [27] and the more recent [58] for more details) First, the dynamic programming solution of the recursive model structure takes advantage of the property of overlapping...
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BibTeX entry: (Update)
Noh, S. and Gmytrasiewicz, P. J. Towards Flexible MultiAgent Decision-Making Under Time Pressure Proc.. of the International Joint Conference on Artificial Intelligence, pp 492-498, (1999) http://citeseer.ist.psu.edu/184682.html More
@inproceedings{ noh99towards,
author = "Sanguk Noh and Piotr J. Gmytrasiewicz",
title = "Towards Flexible Multi-Agent Decision-Making Under Time Pressure",
booktitle = "{IJCAI}",
pages = "492-499",
year = "1999",
url = "citeseer.ist.psu.edu/184682.html" }
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13 Models of bounded rationality (context) - Zilberstein - 1995
12 Execution architectures and compilation (context) - Russell - 1989
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2 Symbolic decision theory and autonomous systems (context) - Fox, Krause - 1991
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