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J. D. Montgomery and Alan K. Mackworth. Adaptive Synchronisation for a RoboCup Agent. In Proceedings of the International RoboCup Symposium, RoboCup 2002, pp. 128–143, Fukuoka, Japan, June 2002.
We describe an algorithm that adaptively synchronises an agent with its environment enabling maximal deliberation time and improved action success rates. The method balances its reliance upon noisy evidence with internal representations, making it robust to interaction faults caused by both communication and timing. The notion of action correctness is developed and used to analyse the new method as well as two special cases: Internal and External synchronisation. Action correctness is determined online by a novel action accounting procedure that determines the outcome of commanded actions. In conjunction, these elements provide online analysis of agent activity, action confirmation for model prediction, and a coarse measure of the agent’s coherence with the environment that is used to adapt its performance.
@InProceedings{IRoboCup02,
author = {J. D. Montgomery and Alan K. Mackworth},
title = {Adaptive Synchronisation for a RoboCup Agent},
year = {2002},
month = {June},
booktitle = {Proceedings of the International RoboCup Symposium, RoboCup 2002},
Editor = {G. Kaminka, P. Lima, P. Rojas},
address = {Fukuoka, Japan},
pages = {128--143},
abstract = {We describe an algorithm that adaptively synchronises an agent with its environment enabling maximal deliberation time and improved action success rates. The method balances its reliance upon noisy evidence with internal representations, making it robust to interaction faults caused by both communication and timing. The notion of action correctness is developed and used to analyse the new method as well as two special cases: Internal and External synchronisation. Action correctness is determined online by a novel action accounting procedure that determines the outcome of commanded actions. In conjunction, these elements provide online analysis of agent activity, action confirmation for model prediction, and a coarse measure of the agent’s coherence with the environment that is used to adapt its performance.},
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