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CONTEXT
2001
Springer
13 years 12 months ago
A Connectionist-Symbolic Approach to Modeling Agent Behavior: Neural Networks Grouped by Contexts
A recent report by the National Research Council (NRC) declares neural networks “hold the most promise for providing powerful learning models”. While some researchers have expe...
Amy E. Henninger, Avelino J. Gonzalez, Michael Geo...
AAAI
2006
13 years 8 months ago
Multi-Conditional Learning: Generative/Discriminative Training for Clustering and Classification
This paper presents multi-conditional learning (MCL), a training criterion based on a product of multiple conditional likelihoods. When combining the traditional conditional proba...
Andrew McCallum, Chris Pal, Gregory Druck, Xuerui ...
JOT
2010
144views more  JOT 2010»
13 years 5 months ago
A Tool for Specifying and Validating Agents' Interaction Protocols: From Agent UML to Maude
To achieve the multi-agent systems’ goals, agents interact to exchange information, to cooperate and to coordinate their tasks. Interaction is generally recognized as an importa...
Farid Mokhati, Brahim Sahraoui, Soufiane Bouzaher,...
ATAL
2008
Springer
13 years 9 months ago
The Use of Norms Violations to Model Agents Behavioral Variety
In multi-agent applications, normative systems are usually used to regulate the behavior of the agents. They provide an efficient means to ensure limited deviations from an expecte...
Benoit Lacroix, Philippe Mathieu, Andras Kemeny
ATAL
2007
Springer
14 years 1 months ago
Letting loose a SPIDER on a network of POMDPs: generating quality guaranteed policies
Distributed Partially Observable Markov Decision Problems (Distributed POMDPs) are a popular approach for modeling multi-agent systems acting in uncertain domains. Given the signi...
Pradeep Varakantham, Janusz Marecki, Yuichi Yabu, ...