Companion cognitive systems: Design goals and lessons learned so far

Kenneth D Forbus, Matthew Klenk, Thomas R Hinrichs

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


The Companion cognitive architecture supports experiments in achieving human-level intelligence. The seven key features of cognitive architecture include analogical processing, extensive conceptual knowledge, flexible reasoning, and coarse-grained distributed implementation, broad learning at multiple levels, continuous operation, and natural interaction. The model for analogical matching is the Structure-Mapping Engine (SME) that computes mappings using algorithm, operating in polynomial time. The model for similarity-based retrieval is many are called/few are chosen (MAC/FAC) that takes as input a probe and a case library. Another design goal for Companions is to emulate the parallelism that's evident in human behavior. Companions are implemented as distributed systems that allocate individual nodes of a cluster computer to semi-independent, asynchronous processes (agents). Agents communicate internally using the Knowledge Query and Manipulation Language (KQML) with callbacks to support asynchronous queries and subscriptions to events.

Original languageEnglish (US)
Pages (from-to)36-46
Number of pages11
JournalIEEE Intelligent Systems
Issue number4
StatePublished - 2009

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Artificial Intelligence


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