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Intentional change, intrinsic motivations, and goal generation.

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Title: Intentional change, intrinsic motivations, and goal generation.
Authors: Manzotti, Riccardo, Moderato, Paolo
Source: Behavioral & Brain Sciences. Aug2014, Vol. 37 Issue 4, p431-432. 2p.
Subjects: Motivation (Psychology), Social change, Machine learning, Artificial intelligence, Social sciences, Metacognition
Abstract: Wilson et al. draw our attention to the problem of a science of intentional change. We stress the connection between their approach and existing paradigms for learning and goal generation that have been developed in machine learning, artificial intelligence, and psychology. These paradigms outline the structural principles of a domain-general and teleologically open agent. [ABSTRACT FROM PUBLISHER]
Copyright of Behavioral & Brain Sciences is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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PubType: Academic Journal
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  Data: Intentional change, intrinsic motivations, and goal generation.
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  Data: <searchLink fieldCode="AR" term="%22Manzotti%2C+Riccardo%22">Manzotti, Riccardo</searchLink><br /><searchLink fieldCode="AR" term="%22Moderato%2C+Paolo%22">Moderato, Paolo</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Behavioral+%26+Brain+Sciences%22">Behavioral & Brain Sciences</searchLink>. Aug2014, Vol. 37 Issue 4, p431-432. 2p.
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  Data: <searchLink fieldCode="DE" term="%22Motivation+%28Psychology%29%22">Motivation (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Social+change%22">Social change</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Social+sciences%22">Social sciences</searchLink><br /><searchLink fieldCode="DE" term="%22Metacognition%22">Metacognition</searchLink>
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  Data: Wilson et al. draw our attention to the problem of a science of intentional change. We stress the connection between their approach and existing paradigms for learning and goal generation that have been developed in machine learning, artificial intelligence, and psychology. These paradigms outline the structural principles of a domain-general and teleologically open agent. [ABSTRACT FROM PUBLISHER]
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  Data: <i>Copyright of Behavioral & Brain Sciences is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=97674177
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              Text: Aug2014
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