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Title:
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DEVELOPMENT AND INHIBITION OF LEARNING ABILITIES IN AGENTS AND INTELLIGENT SYSTEMS |
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Author(s):
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Alexander Poddiakov |
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ISBN:
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978-972-8924-39-3 |
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Editors:
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António Palma dos Reis, Katherine Blashki and Yingcai Xiao (series editors:Piet Kommers, Pedro Isaías and Nian-Shing Chen) |
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Year:
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2007 |
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Edition:
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Single |
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Keywords:
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artificial intelligence, intelligent systems, learning, competition, stimulation and inhibition of learning abilities |
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Type:
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Reflection Paper |
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First Page:
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235 |
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Last Page:
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238 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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In human social life, "the ability to learn faster than your competitors" is "only sustainable competitive advantage" (de
Geus). This statement may concern not only humans, but also artificial intelligence systems, learning ability of which is
considered a most important feature by most researchers. Yet a general law of competition is that a participant of
competition can gain competitive advantages by two ways: (a) increase of its own potential, and (b) premeditated
decrease of competitors' potential. So, paradoxically, possible directions of artificial intelligence systems development
can be design of systems that are able to: (a) counteract other systems' learning, decrease their learning abilities and
conduct their "Trojan horse" teaching; (b) learn and increase level of their leaning abilities and general "intellectual level"
in conditions of counteraction to their learning. In the paper, distinguishing between control of learning and control of
learning ability is introduced. An approach to construction of models of the learning ability control and of agents' mutual
teaching/learning is described. Effects of unpremeditated and premeditated Trojan horse teaching in agents' interactions
are discussed. The aim of future researches is design of competitive environments, in which struggle for higher levels of
learning abilities is presented explicitly as a key parameter and which provide with an opportunity to generate and select
the agents with maximal learning abilities. |
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