Structural measures for games and process control in the branch learning model

Process control problems can be modeled as closed recursive games. Learning strategies for such games is equivalent to the concept of learning infinite recursive branches for recursive trees. We use this branch learning model to measure the difficulty of learning and synthesizing process controllers. We also measure the difference between several process learning criteria, and their difference to controller synthesis. As measure we use the information content (i.e. the Turing degree) of the oracle which a machine need to get the desired power. The investigated learning criteria are finite, EX-, BC-, Weak BC- and online learning. Finite, EX- and BC-style learning are well known from inductive inference, while weak BC- and online learning came up with the new notion of branch (i.e. process) learning. For all considered criteria - including synthesis - we also solve the questions of their trivial degrees, their omniscient degrees and with some restrictions their inference degrees. While most of the results about finite, EX- and BC-style branch learning can be derived from inductive inference, new techniques had to be developed for online learning, weak BC-style learning and synthesis, and for the comparisons of all process learning criteria with the power of controller synthesis

Therapy Plan Generation as Program Synthesis

. There has been developed and implemented an algorithm for the automatic synthesis of therapy plans for complex dynamic systems. This algorithm is the core of some control synthesis module which is embedded in a larger knowledge-based system for control, diagnosis and therapy. There are several applications. The planning algorithm may be understood as an inductive program synthesis procedure. Its fundamentals are introduced and its key ideas are sketched. The dichotomy between executability and consistency is investigated. 1 Motivation and Introduction The main intention of the present paper is to establish a new link between two areas of research: Program Synthesis and Therapy Planning. Thus, the authors wish to advance both areas of research they are active in. For program synthesis, the intended integration may result in new and exciting problems characterized by particular constraints not investigated in the classical approaches, so far. Our approach may widen the view at automat..