Generating Decision Structures and Causal Explanations for Decision
  Making
  This paper examines two related problems that are central to developing an
autonomous decision-making agent, such as a robot. Both problems require
generating structured representafions from a database of unstructured
declarative knowledge that includes many facts and rules that are irrelevant in
the problem context. The first problem is how to generate a well structured
decision problem from such a database. The second problem is how to generate,
from the same database, a well-structured explanation of why some possible
world occurred. In this paper it is shown that the problem of generating the
appropriate decision structure or explanation is intractable without
introducing further constraints on the knowledge in the database. The paper
proposes that the problem search space can be constrained by adding knowledge
to the database about causal relafions between events. In order to determine
the causal knowledge that would be most useful, causal theories for
deterministic and indeterministic universes are proposed. A program that uses
some of these causal constraints has been used to generate explanations about
faulty plans. The program shows the expected increase in efficiency as the
causal constraints are introduced.
