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JMLR
2006
169views more  JMLR 2006»
14 years 9 months ago
Bayesian Network Learning with Parameter Constraints
The task of learning models for many real-world problems requires incorporating domain knowledge into learning algorithms, to enable accurate learning from a realistic volume of t...
Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat ...
92
Voted
ECRTS
2004
IEEE
15 years 1 months ago
On Energy-Constrained Real-Time Scheduling
In this paper, we explore the feasibility and performance optimization problems for real-time systems that must remain functional during an operation/mission with a fixed, initial...
Tarek A. AlEnawy, Hakan Aydin
JFPLC
2003
14 years 11 months ago
Une approche hybride pour l'optimisation multi-objectif sous contraintes
ABSTRACT. We present PICPA, a new algorithm for tackling constrained continuous multiobjective problems. The algorithm combines constraint propagation techniques and evolutionary c...
Vincent Barichard, Jin-Kao Hao
AAAI
2011
13 years 9 months ago
Combining Learned Discrete and Continuous Action Models
Action modeling is an important skill for agents that must perform tasks in novel domains. Previous work on action modeling has focused on learning STRIPS operators in discrete, r...
Joseph Z. Xu, John E. Laird
79
Voted
HM
2010
Springer
157views Optimization» more  HM 2010»
14 years 10 months ago
Hybrid Heuristics for Dynamic Resource-Constrained Project Scheduling Problem
Dynamic Resource-Constrained Project Scheduling Problem (DRCPSP) is a scheduling problem that works with an uncommon kind of resources: the Dynamic Resources. They increase and dec...
André Renato Villela da Silva, Luiz Satoru ...