Kruengkrai, Canasai (National Institute of Information and Communications Technology) | Torisawa, Kentaro (National Institute of Information and Communications Technology) | Hashimoto, Chikara (National Institute of Information and Communications Technology) | Kloetzer, Julien (National Institute of Information and Communications Technology) | Oh, Jong-Hoon (National Institute of Information and Communications Technology) | Tanaka, Masahiro (National Institute of Information and Communications Technology)
We propose a method for recognizing such event causalities as "smoke cigarettes" "die of lung cancer" using background knowledge taken from web texts as well as original sentences from which candidates for the causalities were extracted. We retrieve texts related to our event causality candidates from four billion web pages by three distinct methods, including a why-question answering system, and feed them to our multi-column convolutional neural networks. This allows us to identify the useful background knowledge scattered in web texts and effectively exploit the identified knowledge to recognize event causalities. We empirically show that the combination of our neural network architecture and background knowledge significantly improves average precision, while the previous state-of-the-art method gains just a small benefit from such background knowledge.
I've been struggling with this issue and was wondering if anyone could help. In my architecture for an intelligent system, I plan to use what is basically a planner. What I need, however, is for every action to have detailed preconditions and postconditions. The issue then becomes: how do I obtain this information? What I need is for my agent to automatically infer causality from observations.
"This paper shows how formal characterizations of causality and of the method of comparative statics, long used in econometrics, thermodynamics and other domains, can be applied to clarify and make rigorous the qualitative causal calculus recently proposed by de Kleer and Brown . The formalization shows exactly what assumptions are required to carry out causal analysis of a system of interdependent variables in equilibrium and to propagate disturbances through such a system. The intuitive concepts of causality captured by de Kleer and Brown provide a rough approximation to the standard analytic techniques that are used in the treatment of simultaneous algebraic and differential equations." Artificial Intelligence 29:63-72
The original Halpern-Pearl definition of causality was updated in the journal version of the paper to deal with some problems pointed out by Hopkins and Pearl. Here the definition is modified yet again, in a way that (a) leads to a simpler definition, (b) handles the problems pointed out by Hopkins and Pearl, and many others, (c) gives reasonable answers (that agree with those of the original and updated definition) in the standard problematic examples of causality, and (d) has lower complexity than either the original or updated definitions.