Proceedings of the Annual Meeting of the Cognitive Science Society

Symbolic AI systems based on logical or frame languages can easily perform inferencesthat are still beyond the capability of most connectionist networks. This paper presents a strategy for implementing in connectionist networks the basic mechanisms of variable binding, dynamic frzune allocation and equality that underlie many of the types of inferences commonly handled by frame systems, including inheritance, subsumption and abductive inference. The paper describes a scheme for translating frame definitions in a simple frame language into objective functions whose minima correspond to partial deductive closures of the legaJ inferences. The resulting constrained optimization problem can be viewed as a specification for a connectionist network.