If we are to understand how we can build machines capable of broad purpose learning and reasoning, we must first aim to build systems that can represent, acquire, and reason about the kinds of commonsense knowledge that we humans have about the world. This endeavor suggests steps such as identifying the kinds of knowledge people commonly have about the world, constructing suitable knowledge representations, and exploring the mechanisms that people use to make judgments about the everyday world. In this work, I contribute to these goals by proposing an architecture for a system that can learn commonsense knowledge about the properties and behavior of objects in the world. The architecture described here augments previous machine learning systems in four ways: (1) it relies on a seven dimensional notion of context, built from information recently given to the system, to learn and reason about objects' properties; (2) it has multiple methods that it can use to reason about objects, so that when one method fails, it can fall back on others; (3) it illustrates the usefulness of reasoning about objects by thinking about their similarity to other, better known objects, and by inferring properties of objects from the categories that they belong to; and (4) it represents an attempt to build an autonomous learner and reasoner, that sets its own goals for learning about the world and deduces new facts by reflecting on its acquired knowledge. This thesis describes this architecture, as well as a first implementation, that can learn from sentences such as ``A blue bird flew to the tree'' and ``The small bird flew to the cage'' that birds can fly. One of the main contributions of this work lies in suggesting a further set of salient ideas about how we can build broader purpose commonsense artificial learners and reasoners.

If we are to understand how we can build machines capable of broadpurpose learning and reasoning, we must first aim to build systemsthat can represent, acquire, and reason about the kinds of commonsenseknowledge that we humans have about the world. This endeavor suggestssteps such as identifying the kinds of knowledge people commonly haveabout the world, constructing suitable knowledge representations, andexploring the mechanisms that people use to make judgments about theeveryday world. In this work, I contribute to these goals by proposingan architecture for a system that can learn commonsense knowledgeabout the properties and behavior of objects in the world. Thearchitecture described here augments previous machine learning systemsin four ways: (1) it relies on a seven dimensional notion of context,built from information recently given to the system, to learn andreason about objects' properties; (2) it has multiple methods that itcan use to reason about objects, so that when one method fails, it canfall back on others; (3) it illustrates the usefulness of reasoningabout objects by thinking about their similarity to other, betterknown objects, and by inferring properties of objects from thecategories that they belong to; and (4) it represents an attempt tobuild an autonomous learner and reasoner, that sets its own goals forlearning about the world and deduces new facts by reflecting on itsacquired knowledge. This thesis describes this architecture, as wellas a first implementation, that can learn from sentences such as ``Ablue bird flew to the tree'' and ``The small bird flew to the cage''that birds can fly. One of the main contributions of thiswork lies in suggesting a further set of salient ideas about how wecan build broader purpose commonsense artificial learners andreasoners.