Model transformations in simulation and planning: behavior preserving model simplifications

Abstract

Reasoning tasks such as simulation and planning involve deriving behavior of a system from a model of the system. The information needed to solve such problems can be represented as model behavior pairs (MBPs). The problem can be stated as one or more incomplete MBPs. The problem-solving method can be expressed as a sequence of MBP completions and comparisons. A language for representing and manipulating models, behaviors, and MBPs is presented. It is independent of any specific modeling domain. An important class of model transformation operators is the behavior-preserving model transformation operators. Because they preserve behavior, they can be used to simplify a model without compromising its value for problem solving. This sort of operator can speed up computations significantly. It can be used either to select an appropriate sub-model for a specific problem or to decompose a problem into a sequence of subproblems. A behavior-preserving pruning operator is presented and shown to work in three modeling domains: discrete event simulation (DES), planning, and qualitative physics (QP). The significance of this work lies in the domain independence of the language and operators. It provides a representation midway between the computer-oriented concepts of programming languages (and knowledge representation schemes) and the problem oriented concepts of the real world. The benefits that can result from such a representation are easy mapping of problem-to-solution method, easy communication between solution methods (when more than one reasoning technique is required to solve a problem) and efficient solution of problems.