Abstract
Causal reasoning occupies a central position in human reasoning. In many ways, causality is granular. This is true for: perception, commonsense reasoning as well as for mathematical and scientifc theory. At a very fine-grained level, the physical world itself may be made up out of granules. Knowledge of at least some causal effects is imprecise. Perhaps, complete knowledge of all possible factors might lead to a crisp description of whether an effect will occur. However, in the commonsense world, it is unlikely that all possible factors can be known. Commonsense understanding of the world deals with imprecision, uncertainty and imperfect knowledge. In commonsense, every day reasoning, we use approaches that do not require complete knowledge. Even if the precise elements of the complex are unknown, people recognize that a complex collection of elements can cause a particular effect. They may not know what events are in the complex; or, what constraints and laws the complex is subject to. Sometimes, the details underlying an event can be known to a fine level of detail, sometimes not. Usually, commonsense reasoning is more successful in reasoning about a few large-grain sized events than many fine-grained events. Perhaps, a satisficing solution would be to develop large-grained solutions and then only go to the finer-grain when the impreciseness of the large-grain is unsatisfactory. An algorithmic way of handling causal imprecision is needed. Perhaps fuzzy Markov models might be used to build complexes. It may be more feasible to work on a larger-grained size. This may reduce the need to learn extensive hidden Markov models, which in computationally expensive.
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J. Mazlack, L. Granular Nested Causal Complexes. In: Ruan, D., Chen, G., E. Kerre, E., Wets, G. (eds) Intelligent Data Mining. Studies in Computational Intelligence, vol 5. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11004011_2
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DOI: https://doi.org/10.1007/11004011_2
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