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» Learning Action Strategies for Planning Domains
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ICML
1990
IEEE
15 years 4 months ago
Explanations of Empirically Derived Reactive Plans
Given an adequate simulation model of the task environment and payoff function that measures the quality of partially successful plans, competition-based heuristics such as geneti...
Diana F. Gordon, John J. Grefenstette
90
Voted
IJCSA
2008
104views more  IJCSA 2008»
14 years 12 months ago
Artificial Intelligence and Bluetooth Techniques in a Multi-user M-learning Domain
In this paper we present a practical implementation of a multiuser technical laboratory that combines Artificial Intelligence (AI) and Bluetooth (BT) techniques. The objective is ...
Bonifacio Castaño, Angel Moreno, Melquiades...
117
Voted
ICML
2009
IEEE
16 years 1 months ago
Approximate inference for planning in stochastic relational worlds
Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...
Tobias Lang, Marc Toussaint
110
Voted
KER
2007
90views more  KER 2007»
15 years 7 days ago
PLTOOL: A knowledge engineering tool for planning and learning
AI planning solves the problem of generating a correct and efficient ordered set of instantiated activities, from a knowledge base of generic actions, which when executed will tra...
Susana Fernández, Daniel Borrajo, Raquel Fu...
ICCBR
2010
Springer
15 years 4 months ago
Case-Based Plan Diversity
The concept of diversity was successfully introduced for recommender-systems. By displaying results that are not only similar to a target problem but also diverse among themselves,...
Alexandra Coman, Héctor Muñoz-Avila