Causal Inversion: Applying Kinematic Principles to Mechanism Design

Contents

Most work in intelligent design systems suffers from the limitation that the space of all possible structures that the system is capable of generating is represented in a fixed symbolic language which serves as a support for search. In this research, we investigate techniques for reasoning about a nonenumerable space of possible structures, for which such representations cannot be constructed. As an example, we address the problem of designing part shapes for higher kinematic pairs in fixed-axis mechanisms. When a fixed symbolic language is impossible, symbolic operators for reasoning must be devised during the problem-solving process. We introduce the technique of cansal inversion to obtain symbolic operators which manipulate the shapes of objects in a goal-directed way. In this technique, a causal analysis of kinematic function is in~erted so that functional features are translated to their corresponding shape fcatures. These shape features allow symbolic reasoning about shape modifications, and make knowledge-based design systems possible in this non-enumerable domain.