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A Connectionist System for Rule Based Reasoning With Multi-Place Predicates and Variables

机译:具有多位置谓词和变量的基于规则的推理的连接系统

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摘要

McCarthy has observed that the representational power of most connectionist systems is restricted to unary predicates applied to a fixed object. More recently, Fodor and Pylyshyn have made a sweeping claim that connectionist systems cannot incorporate systematicity and compositionality. These comments suggest that representing structured knowledge in a connectionist network and using this knowledge in a systematic way is considered difficult if not impossible. The work reported in this paper demonstrates that a connectionist system can not only represent structured knowledge and display systematic behavior, but it can also do so with extreme efficiency. The paper describes a connectionist system that can represent knowledge expressed as rules and facts involving multi-place predicates (i.e., n-ary relations), and draw limited, but sound, inferences based on this knowledge. The system is extremely efficient - in fact, optimal, as it draws conclusions in time proportional to the length of the proof. It is observed that representing and reasoning with structured knowledge requires a solution to the variable binding problem. A solution to this problem using a multi-phase clock is proposed. The solution allows the system to maintain and propagate an arbitrary number of variable bindings during the reasoning process. The work also identifies constraints on the structure of inferential dependencies and the nature of quantification in individual rules that are required for efficient reasoning. These constraints may eventually help in modelling the remarkable human ability of performing certain inferences with extreme efficiency.
机译:麦卡锡(McCarthy)观察到,大多数连接主义系统的表示能力仅限于应用于固定对象的一元谓词。最近,Fodor和Pylyshyn提出了一个笼统的主张,即连接主义系统不能纳入系统性和组成性。这些评论表明,即使不是不可能,在连接主义网络中表示结构化知识并以系统的方式使用此知识也很困难。本文报道的工作表明,连接主义系统不仅可以表示结构化知识并显示系统行为,而且还可以以极高的效率做到这一点。该论文描述了一种连接主义系统,该系统可以表示表示为涉及多处谓词的规则和事实的知识(即n元关系),并基于此知识得出有限但合理的推论。该系统效率极高-实际上是最佳的,因为它在时间上得出与证明长度成比例的结论。可以看出,用结构化知识表示和推理需要解决变量绑定问题。提出了一种使用多相时钟的解决方案。该解决方案允许系统在推理过程中维护和传播任意数量的变量绑定。这项工作还确定了推理依赖性结构的约束以及有效推理所需的单个规则中的量化性质。这些约束条件最终可能有助于建模人类以非凡的效率执行某些推理的非凡能力。

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