Information enhancement - A tool for approximate representation of optimal strategies from influence diagrams

There are three phases in the life of a decision problem, specification, solution, and representation of solution. The specification and solution phases are off-line, while the represention of solution often shall serve an on-line situation with rather tough constraints on time and space. One of the advantages of influence diagrams (IDs) is that for small decision problems, the distinction between phases does not confront the decision maker with a problem; when the problem has been properly specified, the solution algorithms are so efficient that the ID can also be used as an on-line representation of the solution. If the solution algorithm cannot meet the on-line requirements, you will construct an alternative structure for representing the optimal strategy, for example a look-up table or a strategy tree. We report on ongoing work with situations where the solution algorithm is too space and time consuming, and where the policy functions for the decisions have so large domains that they cannot be represented directly in a strategy tree. The approach is to have separate ID representations for each decision variable. In each representation the actual information is fully exploited, however the representation of policies for future decisions are approximations. We call the approximation information abstraction. It consists in introducing a dummy structure connecting the past with the decision. We study how to specify, implement and learn information abstraction.