Both learning and reasoning are important aspects of intelligence. However they are rarely integrated within a single agent. Here it is suggested that imprecise learning and crisp reasoning may be coherently combined via the cognitive context. The identification of the current context is done using an imprecise learning mechanism, whilst the contents of a context are crisp models that may be usefully reasoned about. This also helps deal with situations of logical under- and over-determination because the scope of the context can be adjusted to include more or less knowledge into the reasoning process. An example model is exhibited where an agent learns and acts in an artificial stock market. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Edmonds, B., & Norling, E. (2007). Integrating learning and inference in multi-agent systems using cognitive context. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4442 LNAI, pp. 142–155). Springer Verlag. https://doi.org/10.1007/978-3-540-76539-4_11
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