Abstract
The last few decades have seen remarkable advances in Artificial Intelligence, with some form of intelligent system now embedded in a wide range of devices and software, from mobile phones to internet search engines. However, there are many aspects of intelligent behaviour that these systems still cannot replicate. For example, intelligent systems still cannot negotiate a contract online and they cannot promote a certain viewpoint or construct an informal argument. They cannot devise a political slogan or catchphrase. Indeed, the extent to which intelligent agents can take part in any but the most semantically simple dialogues is very limited. These tasks require, amongst other things, an ability to be imprecise or vague on appropriate occasions and in order to achieve certain goals. Furthermore, they require that intelligent systems be able to evolve their own semantic structures and to adapt conceptual models according to context and their current tasks and goals. In an age when intelligent systems must increasingly find patterns and structure in rapidly evolving data rich environments, new methodologies for embedding flexibility and representing imprecision and uncertainty in concept definitions can open the way to a new generation of uniquely robust and adaptable systems.
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Lawry, J. (2013). Flexible Concepts Are Fuzzy Concepts. In: Seising, R., Trillas, E., Moraga, C., Termini, S. (eds) On Fuzziness. Studies in Fuzziness and Soft Computing, vol 298. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35641-4_51
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DOI: https://doi.org/10.1007/978-3-642-35641-4_51
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