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Tech Report kmi-95-07 Abstract


The Naive Psychology Manifesto
Techreport ID: kmi-95-07
Date: 1995
Author(s): Stuart Watt
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This paper argues that artificial intelligence has failed to address the whole problem of common sense, and that this is the cause of a recent stagnation in the field. The big gap is in common sense---or naive---psychology, our natural human ability to see one another as minds rather than as bodies. This is especially important to artificial intelligence because AI must eventually enable us humans to see computers not as grey boxes, but as minds. The paper proposes that AI study exactly this---what is going on in people's heads that makes them see others as having minds. To illustrate this, it describes three models for common sense psychology; each of which illustrates some aspect of the human ability to see people as minds. The paper concludes by drawing some conclusions about where and how AI can adapt to deal with these issues and to provide a new and stronger foundation for future research.

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A modified version of this paper has been submitted to Informatica.
 
KMi Publications Event | SSSW 2013, The 10th Summer School on Ontology Engineering and the Semantic Web Journal | 25 years of knowledge acquisition
 

Social Software is...


Social Software
Social Software can be thought of as "software which extends, or derives added value from, human social behaviour - message boards, musical taste-sharing, photo-sharing, instant messaging, mailing lists, social networking."

Interacting with other people not only forms the core of human social and psychological experience, but also lies at the centre of what makes the internet such a rich, powerful and exciting collection of knowledge media. We are especially interested in what happens when such interactions take place on a very large scale -- not only because we work regularly with tens of thousands of distance learners at the Open University, but also because it is evident that being part of a crowd in real life possesses a certain 'buzz' of its own, and poses a natural challenge. Different nuances emerge in different user contexts, so we choose to investigate the contexts of work, learning and play to better understand the trade-offs involved in designing effective large-scale social software for multiple purposes.