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» AI planning: solutions for real world problems
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PUK
2003
13 years 9 months ago
Accelerating Heuristic Search in Spatial Domains
This paper exploits the spatial representation of state space problem graphs to preprocess and enhance heuristic search engines. It combines classical AI exploration with computati...
Stefan Edelkamp, Shahid Jabbar, Thomas Willhalm
AAAI
1997
13 years 9 months ago
Model Minimization in Markov Decision Processes
Many stochastic planning problems can be represented using Markov Decision Processes (MDPs). A difficulty with using these MDP representations is that the common algorithms for so...
Thomas Dean, Robert Givan
EUSFLAT
2001
13 years 9 months ago
Fuzzy robustness analysis
This paper proposes a confluence between soft OR and soft computing methods, by means of an application of fuzzy logic ideas to robustness analysis. Both methods try to add flexib...
Luiz Fernando Loureiro Legey, Heloisa Firmo Kazay
EVOW
2006
Springer
13 years 11 months ago
The Trade Off Between Diversity and Quality for Multi-objective Workforce Scheduling
In this paper we investigate and compare multi-objective and weighted single objective approaches to a real world workforce scheduling problem. For this difficult problem we consid...
Peter I. Cowling, Nic Colledge, Keshav P. Dahal, S...
ICDM
2009
IEEE
149views Data Mining» more  ICDM 2009»
14 years 2 months ago
Accelerated Gradient Method for Multi-task Sparse Learning Problem
—Many real world learning problems can be recast as multi-task learning problems which utilize correlations among different tasks to obtain better generalization performance than...
Xi Chen, Weike Pan, James T. Kwok, Jaime G. Carbon...