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» Learning Heuristic Functions from Relaxed Plans
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KDD
2004
ACM
237views Data Mining» more  KDD 2004»
15 years 10 months ago
Bayesian Model-Averaging in Unsupervised Learning From Microarray Data
Unsupervised identification of patterns in microarray data has been a productive approach to uncovering relationships between genes and the biological process in which they are in...
Mario Medvedovic, Junhai Guo
ECAI
2004
Springer
15 years 3 months ago
IPSS: A Hybrid Reasoner for Planning and Scheduling
In this paper we describe IPSS (Integrated Planning and Scheduling System), a domain independent solver that integrates an AI heuristic planner, that synthesizes courses of actions...
María Dolores Rodríguez-Moreno, Ange...
JAIR
2000
152views more  JAIR 2000»
14 years 9 months ago
Value-Function Approximations for Partially Observable Markov Decision Processes
Partially observable Markov decision processes (POMDPs) provide an elegant mathematical framework for modeling complex decision and planning problems in stochastic domains in whic...
Milos Hauskrecht
MP
1998
109views more  MP 1998»
14 years 9 months ago
Rounding algorithms for covering problems
In the last 25 years approximation algorithms for discrete optimization problems have been in the center of research in the fields of mathematical programming and computer science...
Dimitris Bertsimas, Rakesh V. Vohra
ENC
2004
IEEE
15 years 1 months ago
Distributed Learning in Intentional BDI Multi-Agent Systems
Despite the relevance of the belief-desire-intention (BDI) model of rational agency, little work has been done to deal with its two main limitations: the lack of learning competen...
Alejandro Guerra-Hernández, Amal El Fallah-...