63 search results - page 6 / 13 » Learning Symbolic Models of Stochastic Domains |
TSMC
2010
14 years 4 months ago
2010
Traditional planning assumes reachability goals and/or full observability. In this paper, we propose a novel solution for safety and reachability planning with partial observabilit...
SYNASC
2005
IEEE
15 years 3 months ago
2005
IEEE
The paper presents a new reinforcement learning mechanism for spiking neural networks. The algorithm is derived for networks of stochastic integrate-and-fire neurons, but it can ...
ATAL
2008
Springer
14 years 11 months ago
2008
Springer
Due to computational intractability, large scale coordination algorithms are necessarily heuristic and hence require tuning for particular environments. In domains where character...
103
click to vote
AI
2006
Springer
14 years 9 months ago
2006
Springer
In this paper we describe a machine learning approach for acquiring a model of a robot behaviour from raw sensor data. We are interested in automating the acquisition of behaviour...
ICCBR
2007
Springer
15 years 3 months ago
2007
Springer
The aim of this paper is to analyze how the generalizations built by a CBR method can be used as local approximations of a concept. From this point of view, these local approximati...