Teaching machines about everyday life

Push Singh, Barbara Barry, Hugo Liu

Research output: Contribution to journalArticlepeer-review


In order to build software that can deeply understand people and our problems, we require computational tools that give machines the capacity to learn and reason about everyday life. We describe three commonsense knowledge bases that take unconventional approaches to representing, acquiring, and reasoning with large quantities of commonsense knowledge. Each adopts a different approach - ConcepNet is a large-scale semantic network, LifeNet is a probabilistic graphical model, and StotyNet is a database of stoty-scripts. We describe the evolution, architecture and operation of these three systems, and conclude with a discussion of how we might combine them into an integrated commonsense reasoning system.

Original languageEnglish (US)
Pages (from-to)227-240
Number of pages14
JournalBT Technology Journal
Issue number4
StatePublished - Oct 2004

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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