Defeasible Decisions: What the Proposal is and isn't
This work offers a philosophical reframing of decision theory that could impact researchers in AI planning and decision-making, though it appears incremental as details remain undeveloped.
The author proposes a defeasible reasoning framework for decision analysis that differs from classical Bayesian approaches, aiming to integrate planning with decision theory and address the meta-decision regress problem.
In two recent papers, I have proposed a description of decision analysis that differs from the Bayesian picture painted by Savage, Jeffrey and other classic authors. Response to this view has been either overly enthusiastic or unduly pessimistic. In this paper I try to place the idea in its proper place, which must be somewhere in between. Looking at decision analysis as defeasible reasoning produces a framework in which planning and decision theory can be integrated, but work on the details has barely begun. It also produces a framework in which the meta-decision regress can be stopped in a reasonable way, but it does not allow us to ignore meta-level decisions. The heuristics for producing arguments that I have presented are only supposed to be suggestive; but they are not open to the egregious errors about which some have worried. And though the idea is familiar to those who have studied heuristic search, it is somewhat richer because the control of dialectic is more interesting than the deepening of search.