FIRE: Infrastructure for experience-based systems with common sense

Kenneth D Forbus, Thomas R Hinrichs, Johan De Kleer, Jeffrey Usher

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


We believe that the flexibility and robustness of common sense reasoning comes from analogical reasoning, learning, and generalization operating over massive amounts of experience. Million-fact knowledge bases are a good starting point, but are likely to be orders of magnitude smaller, in terms of ground facts, than will be needed to achieve human-like common sense reasoning. This paper describes the FIRE reasoning engine which we have built to experiment with this approach. We discuss its knowledge base organization, including coarse-coding via mentions and a persistent TMS to achieve efficient retrieval while respecting the logical environment formed by contexts and their relationships in the KB. We describe its stratified reasoning organization, which supports both reflexive reasoning (Ask, Query) and deliberative reasoning (Solve, HTN planner). Analogical reasoning, learning, and generalization are supported as part of reflexive reasoning. To show the utility of these ideas, we describe how they are used in the Companion cognitive architecture, which has been used in a variety of reasoning and learning experiments.

Original languageEnglish (US)
Title of host publicationCommonsense Knowledge - Papers from the AAAI Fall Symposium, Technical Report
PublisherAI Access Foundation
Number of pages6
ISBN (Print)9781577354840
StatePublished - 2010
Event2010 AAAI Fall Symposium - Arlington, VA, United States
Duration: Nov 11 2010Nov 13 2010

Publication series

NameAAAI Fall Symposium - Technical Report


Other2010 AAAI Fall Symposium
Country/TerritoryUnited States
CityArlington, VA

ASJC Scopus subject areas

  • Engineering(all)


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