ABSTRACT
Symbolic AI systems based on logical or frame languages can easily perform inferences that 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 frame 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 legal inferences. The resulting constrained optimization problem can be viewed as a specification for a connectionist network.
