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» Learning to Optimize Plan Execution in Information Agents
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IAT
2010
IEEE
13 years 5 months ago
Selecting Operator Queries Using Expected Myopic Gain
When its human operator cannot continuously supervise (much less teleoperate) an agent, the agent should be able to recognize its limitations and ask for help when it risks making...
Robert Cohn, Michael Maxim, Edmund H. Durfee, Sati...
AGENTS
2000
Springer
13 years 12 months ago
Scalability of a Transactional Infrastructure for Multi-Agent Systems
Abstract. One of the reasons for attending to agent technology is the evergrowing complexity of information systems and the increasing difficulty to foresee and plan for all potent...
Khaled Nagi
SIGMOD
2002
ACM
119views Database» more  SIGMOD 2002»
14 years 7 months ago
Rate-based query optimization for streaming information sources
Relational query optimizers have traditionally relied upon table cardinalities when estimating the cost of the query plans they consider. While this approach has been and continue...
Stratis Viglas, Jeffrey F. Naughton
ICEBE
2005
IEEE
96views Business» more  ICEBE 2005»
14 years 1 months ago
Adding Physical Optimization to Cost Models in Information Mediators
Optimization of execution plans in information mediators is a critical task, specially when sources are remote and semistructured, as in the case of transactional web sites. Lack ...
Justo Hidalgo, Alberto Pan, José Losada, Ma...
CIA
2006
Springer
13 years 11 months ago
Learning to Negotiate Optimally in Non-stationary Environments
Abstract. We adopt the Markov chain framework to model bilateral negotiations among agents in dynamic environments and use Bayesian learning to enable them to learn an optimal stra...
Vidya Narayanan, Nicholas R. Jennings