Abstract
Concept formation is a form of abstraction that allows for knowledge transfer, generalization, and compact representation. Concepts are useful for the creation of a generally intelligent autonomous agent. If an autonomous agent is experiencing a changing world, then nearly every experience it has will be unique in that it will have at least slight differences from other experiences. Concepts allow an agent to generalize these experiences and other data. In some applications, the concepts that an agent uses are explicitly provided by a human programmer. A problem with this approach is that the agent encounters difficulties when it faces situations that the programmer had not anticipated. For this reason, it would be useful for the agent to automatically form concepts in an unsupervised setting. The agent should be able to depend as little as possible on representations tailored by humans, and therefore it should develop its own representations from raw uninterpreted data.
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Pickett, M. (2007). Using Analogy Discovery to Create Abstractions. In: Miguel, I., Ruml, W. (eds) Abstraction, Reformulation, and Approximation. SARA 2007. Lecture Notes in Computer Science(), vol 4612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73580-9_37
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DOI: https://doi.org/10.1007/978-3-540-73580-9_37
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