| It is well-known that the capability of reasoning with causation is very
important and even crucial for many real-world applications. The causal
reasoning problem we used in this case study is related to commonsense knowledge
about "balance" and "fall". A testing instance is shown in Figure 1. There
is a board putting on the edge of a table, with one end sticking-out. A red
bottle is put on the sticking-out end of the board, and a green bottle is on the
other end. If the green bottle was moved while the red one was on the stick-out
end of the board, the red bottle would drop to the ground. The task is to pick
up the green bottle under a default presupposition of avoiding anything falling.
To accomplish the task, one must take some measure first against the unwanted
side-effect of action "catch the green bottle". We input into Ke Jia some
causal knowledge which is manually programmed and told Ke Jia that the red
bottle is on the sticking-out end of the board and the green one is on the other
end. Ke Jia produced a plan in which the red bottle is moved to a safety place
first and then the green bottle is caught. The inference for generating the plan
costs no more than 0.5 second in the experiments. The result demonstrates that
Ke Jia can make use of build-in causal knowledge to accomplish causal reasoning
easily.
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