The stabilization of environments

Kristian J. Hammond, Timothy M. Converse*, Joshua W. Grass

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


In planning and activity research there are two common approaches to matching agents with environments. Either the agent is designed with a specific environment in mind, or it is provided with learning capabilities so that it can adapt to the environment it is placed in. In this paper we look at a third and underexploited alternative: designing agents which adapt their environments to suit themselves. We call thisstabilization, and we present a taxonomy of types of stability that human beings typically both rely on and enforce. We also taxonomize the ways in which stabilization behaviors can be cued and learned. We illustrate these ideas with a program calledFixPoint, which improves its performance over time by stabilizing its environment.

Original languageEnglish (US)
Pages (from-to)305-327
Number of pages23
JournalArtificial Intelligence
Issue number1-2
StatePublished - Jan 1995

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence


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