On Github: my Fuzzy Associative Memory (FAM) package

I’ve just released to Github my working fuzzy logic module, Fuzzy Associative Memory.

A Fuzzy Associative Memory (FAM for short) is a Fuzzy Logic tool for decision making. It uses Fuzzy Sets to establish a set of rules that are linguistic in nature; examples might include:

  • “If the room is a bit warm, turn the fan up a little bit”
  • “If the orc’s hit points are a little low, retreat from the enemy”
  • “If the ship is off course by a little bit, correct just a little to the right”
  • “If the bird is much slower than the flock, speed it up a lot”

As you can see, the rules are deliberately vague and use qualifiers like “a little” and “a lot”. This is the nature of fuzzy sets; they capture such human fuzziness in a way that extracts highly natural behavior from the fuzzy rules.

It has a wide range of applications:

  • Industrial control, such as governing a fan to keep a room at the “just right” temperature
  • Game AI, such as giving human-like behavior capabilities to NPCs
  • Prediction systems

Status

This is working, functional software. It currently supports:

  • Triangular fuzzy sets for input/output
  • Larsen Implication (scaling)
  • Atomic antecedent propositions (if A then Z)

To do:

  • Trapezoidal (and other shapes) for fuzzy sets
  • Hedges (‘very’ and ‘fairly’)
  • Mamdani Implication (clipping)
  • Composite antecedent propositions (if A or B, then Z)
  • Additional examples

Examples

The bin directory contains the following examples:

  • hvac_system_example illustrates how a FAM could govern an HVAC fan unit to maintain a constant, comfortable temperature

Get it here!

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