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» Modelling Uncertainty in Agent Programming
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82
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GECCO
2004
Springer
113views Optimization» more  GECCO 2004»
15 years 5 months ago
Implications of Epigenetic Learning Via Modification of Histones on Performance of Genetic Programming
Extending the notion of inheritable genotype in genetic programming (GP) from the common model of DNA into chromatin (DNA and histones), we propose an approach of embedding in GP a...
Ivan Tanev, Kikuo Yuta
148
Voted
AAMAS
2005
Springer
15 years 12 days ago
Cooperative Multi-Agent Learning: The State of the Art
Cooperative multi-agent systems are ones in which several agents attempt, through their interaction, to jointly solve tasks or to maximize utility. Due to the interactions among t...
Liviu Panait, Sean Luke
ATAL
2006
Springer
15 years 4 months ago
Verifying space and time requirements for resource-bounded agents
The effective reasoning capability of an agent can be defined as its capability to infer, within a given space and time bound, facts that are logical consequences of its knowledge...
Natasha Alechina, Mark Jago, Piergiorgio Bertoli, ...
109
Voted
ICRA
1993
IEEE
131views Robotics» more  ICRA 1993»
15 years 4 months ago
Exploration Strategies for Mobile Robots
The problem of programming a robot t o carry out a systematic exploration of its environment using realistic sensors is considered in this paper. The robot is modelled as a single...
Camillo J. Taylor, David J. Kriegman
106
Voted
CP
2007
Springer
15 years 6 months ago
Boosting Probabilistic Choice Operators
Probabilistic Choice Operators (PCOs) are convenient tools to model uncertainty in CP. They are useful to implement randomized algorithms and stochastic processes in the concurrent...
Matthieu Petit, Arnaud Gotlieb