Plenary lecture 9: computationally recognizing causality in an imprecise world
Lawrence J. Mazlack · Annual Conference on Computers · 2010
Causal reasoning perceptions play an essential role in human decision-making. Recognizing and developing causal relationships is essential for reasoning; it forms the basis for acting intelligentially in the world. Causal knowledge provides a deep understanding of a system; and, the potential control over a system that comes from being able to predict action's consequences. Relationships with a known cause/effect relationship have a high decision value. Causality description must necessarily be imperfect as knowledge is imperfect and limited. Commonsense understanding of the world tells us that we have to deal with imprecision, uncertainty and imperfect knowledge. Consequently, knowledge of at least some causal effects is inherently imprecise. A difficulty is striking a good balance between precise formalism and commonsense imprecise reality. Causality is imprecisely granular in many ways. Causal complexes are groupings of smaller causal relations that make up a large grained causal object. Usually, commonsense reasoning is more successful in reasoning about a few large-grained events than many fine-grained events. However, the larger-grained causal objects are necessarily more imprecise as some of their constituent components. A satisficing solution might be to develop large-grained solutions and then only go to the finer-grain when the impreciseness of the large-grain is unsatisfactory.