Semantic Vector Spaces for Broadening Consideration of Consequences
  Reasoning systems with too simple a model of the world and human intent are
unable to consider potential negative side effects of their actions and modify
their plans to avoid them (e.g., avoiding potential errors). However,
hand-encoding the enormous and subtle body of facts that constitutes common
sense into a knowledge base has proved too difficult despite decades of work.
Distributed semantic vector spaces learned from large text corpora, on the
other hand, can learn representations that capture shades of meaning of
common-sense concepts and perform analogical and associational reasoning in
ways that knowledge bases are too rigid to perform, by encoding concepts and
the relations between them as geometric structures. These have, however, the
disadvantage of being unreliable, poorly understood, and biased in their view
of the world by the source material. This chapter will discuss how these
approaches may be combined in a way that combines the best properties of each
for understanding the world and human intentions in a richer way.
