@inBook{
 title = {A Framework for Integrating Heterogenous Learning Agents},
 type = {inBook},
 year = {1988},
 identifiers = {[object Object]},
 pages = {746-764},
 publisher = {Springer-Verlag},
 city = {Waltham, MA},
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 created = {2009-08-18T04:22:20.000Z},
 file_attached = {false},
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 last_modified = {2017-10-14T18:03:59.023Z},
 read = {false},
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 abstract = {Machine Learning is a rapidly growing subfield of Artificial Intelligence, and a large variety of learning algorithms have been reported in the literature. However, no one algorithm provides a totally satisfactory solution to a wide range of problems. This chapter describes our domain-independent Integrated Learning System (ILS), and one application, which learns how to control a telecommunications network. Our approach includes a framework for combining various learning paradigms, integrating different reasoning techniques, and coordinating distributed cooperating problem-solvers. The current implementation has five learning paradigms (agents) that cooperate to improve problem-solving performance. ILS also includes a central controller, called The Learning Coordinator (TLC), which manages control flow and communication between the agents. The agents provide TLC with advice. TLC chooses which suggestion to adopt and performs the appropriate actions. At intervals, the agents can inspect the results of the TLC's actions and use this feedback to learn, improving the value of their future advice.},
 bibtype = {inBook},
 author = {Silver, Bernard and Vittal, John and Frawley, William and Iba, Glenn and Fawcett, Tom and Dusseault, Susan and Doleac, John},
 book = {Second Generation Expert Systems}
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