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Commonsense reasoning is also referred to as mundane reasoning. Commonsense reasoning is a term used by ethnomethodologists, derived from Alfred Schutz (1899-1959), referring to the practical or everyday reasoning used by members of society to create and sustain a sense of social reality as being objective, factual, predictable and external to themselves. Since the objectivity of the world as a practical accomplishment is the focus of ethnomethodology this kind of reasoning is a primary topic of investigation. Sociology and common sense both assume that there is an objective world that exists independently of the knower and that is accessible to competent perceivers. This assumption forms the basis of mundane reasoning. A device using Artificial Intelligence or AI that exhibits commonsense reasoning will be capable of predicting results and drawing conclusions that are similar to innate ability to reason about people's behavior and intentions, and humans' natural understanding of the physical world. commonsense knowledge is the set of background information that an individual is intended to know or assume and the ability to use it when appropriate.
Visual Commonsense Reasoning is a new task and large-scale dataset for cognition-level visual understanding. At just one glance we can effortlessly imagine the world beyond the pixels. Google has a Visual Commonsense Reasoning group, which discusses the dataset, ask/answer questions, and share things that you find. an important and challenging problem in the field of computer vision. Visual Commonsense Reasoning is important because the ability to reason about commonsense, plan and act accordingly, represents the distinct competence that tells human apart from other animals.
Learning in Order to Reason: The Approach.
SOFSEM: Theory and Practice of Informatics (1996), D. Roth.
Abstract: Any theory aimed at understanding commonsense reasoning, the process that humans use to cope with the mundane but complex aspects of the world in evaluating everyday situations, should account for its flexibility, its adaptability, and the speed with which it is performed.
Visual common-sense for scene understanding using perception, semantic parsing and reasoning - Somak Aditya, Yiannis Aloimonos, Chitta Baral, Cornelia Fermuller and Yezhou Yang. Abstract: In this paper we explore the use of visual commonsense knowledge and other kinds of knowledge(such as domain knowledge, background knowledge, linguistic knowledge for scene understanding. In particular, we combine visual processing with techniques from natural language understanding (especially semantic parsing), common-sense reasoning and knowledge representation and reasoning to improve visual perception to reason about ﬁner aspects of activities.
An architecture of
diversity for commonsense reasoning, IBM Systems Journal, vol. 41(3).
Mccarthy, J., Marvin, M., Sloman, A., Gong, L., Lau, T., Morgenstern, L., Mueller, E.T.,
Riecken, D., Singh, M. and Singh, P.
Abstract: This paper discusses commonsense reasoning and what makes it difficult for computers. The paper contends that commonsense reasoning is too hard a problem to solve using any single artificial intelligence technique. A multilevel architecture is proposed that consists of diverse reasoning and representation techniques that collaborate and reflect in order to allow the best techniques to be used for the many situations that arise in commonsense reasoning. Story understanding, specifically, understanding and answering questions about progressively harder children's texts, is presented as a task for evaluating and scaling up a commonsense reasoning system.
Reasoning and Commonsense Knowledge in Artificial Intelligence.
Ernest Davis, Dept. of Computer Science, New York University.
Gary Marcus, Dept. of Psychology, New York University.
Abstract Since the earliest days of artificial intelligence, it has been recognized that commonsense reasoning is one of the central challenges in the field. However, progress in this area has on the whole been frustratingly slow. In this review paper, we discuss why commonsense reasoning is needed to achieve human-level performance in tasks like natural language processing, vision, and robotics, why the problem is so difficult, and why progress has been slow.
A Simple Method for
Abstract: Commonsense reasoning is a long-standing challenge for deep learning. For example, it is difﬁcult to use neural networks to tackle the Winograd Schema dataset. In this paper, we present a simple method for commonsense reasoning with neural networks, using unsupervised learning. Key to our method is the use of language models, trained on a massive amount of unlableddata, to score multiple choice questions posed by commonsense reasoning tests. Analysis shows that our system successfully discovers important features of the context that decide the correct answer, indicating a good grasp of commonsense knowledge.
Commonsense Reasoning by
Erik T Mueller.
Central to the idea of Artificial Intelligence is getting computers to understand simple facts about people and everyday lifewhat we call Common Sense. Amid the technical discussions about inference algorithms and knowledge representation, a larger question arises: What have we actually learned in the past 30 years about how to put Commonsense knowledge in computers? Look no further than Erik Mueller's Commonsense Reasoning for a deep and insightful survey of the state of the art in this topic. The strength of this book is that it uses a uniform representation formalism, the event calculus, to solve a variety of commonsense reasoning problems.