Schema Induction in a Structure-Sensitive Connectionist Model

9511504 Hummel这项研究将开发一个计算机系统来模拟人们如何从例子中学习图式。模式是描述一般情况、事件、规则或关系的知识结构。例如，餐馆模式可以指定在餐馆中发生的事件的类型，以及这些事件中的参与者(例如，厨师、服务员和顾客等)之间的关系；家庭关系模式可以指定定义各种家庭成员的关系的类型，诸如姐妹、兄弟、父母、叔叔等。模式是非常一般的，因为它指的是许多特定的情况，并且也是高度结构化的，因为它指定了对象、事件之间的关系，甚至是其他关系之间的关系。心理学家和计算机科学家早就认识到图式作为推理的基础的效用：如果一个人可以将一个对象或情景识别为一般图式的一个实例，那么他就可以使用该图式来推理特定的对象或情景。例如，家庭关系模式告诉我们，妈妈的兄弟Bob是我们的叔叔，如果Bob有孩子，那么他们将是我们的堂兄弟。由于图式的存在，没有必要单独学习每个人的家庭关系。尽管模式的通用性和结构化特性使它们成为非常有用的知识形式，但它们也使它们很难建模：传统的计算机建模符号方法擅长表示结构化信息，但它们难以灵活地将特定实例与一般事实匹配；相比之下，连接主义模型擅长将实例与一般类别灵活匹配，但它们很难表示结构化信息(如规则和关系)。也许出于这个原因，还没有人开发出基于图式的学习和推理的正式和通用模型。Hummel和Holyoak开发了一个类比的计算机模型，该模型将连接主义体系结构与表示和学习关系结构的能力相结合。该模型以灵活、通用的方式表示结构化信息的能力使其成为模拟人类图式学习和使用的理想工具。本研究将把该模型的方法应用于结构的表征，以解决表征、学习和使用图式的问题。结果将是一个模型，它可以(A)从特定的例子中学习一般的模式，(B)将新的实例与学习的模式匹配，以及(C)使用模式来对新的例子进行归纳推理。该模型将是第一个基于人类图式推理的形式化理论，并将大大有助于我们对人类推理是一个广泛的领域的理解。它也将是第一个使用模式灵活地对一般知识领域进行推理的工作计算机系统。***

9511504 HUMMEL This research will develop a computer system to model how people learn schemas from examples. Schemas are knowledge structures that describe general situations, events, rules, or relationships. For example, a Restaurant Schema might specify the kinds of events that take place in restaurants, and the relationships between the actors in those events (e.g., chefs, waiters, and customers, etc.); a Family Relation Schema might specify the kinds of relationships that define various family members, such as sisters, brothers, parents, uncles, etc. A schema is very general, in that it refers to many specific situations, and also highly structured, in that it specifies the relationships between objects, events, or even between other relationships. Psychologists and computer scientists have long recognized the utility of schemas as a basis for reasoning: If one can recognize an object or situation as an instance of a general schema, then one can use the schema to reason about the specific object or situation. For example, the Family Relation Schema tells us to expect that Mom's brother, Bob, is our uncle, and that if Bob has children, then they will be our cousins. Due to the schema, it is not necessary to learn the family relations for every individual separately. Although the generality and structured nature of schemas make them extremely useful forms of knowledge, they also make them very difficult to model: Traditional symbolic approaches to computer modeling are good at representing structured information, but they have difficulty flexibly matching specific instances to general facts; by contrast, connectionist models are good at flexibly matching instances to general categories, but they have great difficulty representing structured information (such as rules and relationships). Perhaps for this reason, no one has ever developed a formal and general model of schema-based learning and reasoning. Hummel and Holyoak have developed a computer mo del of analogy that combines a connectionist architecture with the capacity to represent and learn relational structures. The model's capacity to represent structured information in a flexible, general manner makes it an ideal vehicle for simulating human schema learning and use. The research will apply the model's approach to the representation of structure to the problems of representing, learning, and using schemas. The result will be a model that can (a) learn general schemas from specific examples, (b) match new instances to learned schemas, and (c) use the schemas to make inductive inferences about the new examples. The model will be the first formal theory of human schema-based reasoning, and will contribute substantially to our understanding or human reasoning is a wide variety of domains. It will also be the first working computer system to use schemas to reason flexibly about general knowledge domains. ***