Learning Monotonicity

Monotonic learning is when an agent may not learn any knowledge that contradicts what it already knows. For example, it may not replace a statement with its negation. Thus, the knowledge base may only grow with new facts in a monotonic fashion. The advantages of monotonic learning are:

  1. greatly simplified truth-maintenance
  2. greater choice in learning strategies

Non-monotonic learning is when an agent may learn knowledge that contradicts what it already knows. So it may replace old knowledge with new if it believes there is sufficient reason to do so. The advantages of non-monotonic learning are:

  1. increased applicability to real domains,
  2. greater freedom in the order things are learned in

A related property is the consistency of the knowledge. If an architecture must maintain a consistent knowledge base then any learning strategy it uses must be monotonic.

Press this line for general discussion on learning.

Examples of monotonic learning architectures are:

  • Atlantis by E. Gat.
  • ERE by Drummond et al.
  • Homer by Vere & Bickmore.
  • Icarus by Langley.
  • Prodigy by Carbonell et al

    • Examples of non-monotonic learning architectures are:

  • Dynamic Control Architecture by B. Hayes-Roth.
  • Theo by T. Mitchell et al.

    • Examples of architectures that are compatable with non-monotonic learning but do not support it are:

  • MAX by D. Kuokka
  • SOAR by A. Newell et al.
  • Teton by VanLehn & Ball.

    • Other Properties.

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