Symbolic Decision Theory and Autonomous Systems
  The ability to reason under uncertainty and with incomplete information is a
fundamental requirement of decision support technology. In this paper we argue
that the concentration on theoretical techniques for the evaluation and
selection of decision options has distracted attention from many of the wider
issues in decision making. Although numerical methods of reasoning under
uncertainty have strong theoretical foundations, they are representationally
weak and only deal with a small part of the decision process. Knowledge based
systems, on the other hand, offer greater flexibility but have not been
accompanied by a clear decision theory. We describe here work which is under
way towards providing a theoretical framework for symbolic decision procedures.
A central proposal is an extended form of inference which we call
argumentation; reasoning for and against decision options from generalised
domain theories. The approach has been successfully used in several decision
support applications, but it is argued that a comprehensive decision theory
must cover autonomous decision making, where the agent can formulate questions
as well as take decisions. A major theoretical challenge for this theory is to
capture the idea of reflection to permit decision agents to reason about their
goals, what they believe and why, and what they need to know or do in order to
achieve their goals.
