Fuzzy Associative Matrix

A fuzzy associative matrix expresses fuzzy logic rules in matrix form. These rules usually take two variables as input, mapping cleanly to a two-dimensional matrix, although theoretically a matrix of any number of dimensions is possible. Let's suppose we want to write fuzzy logic rules for a video game monster. We decide to start with two variables: hit points (HP) and firepower (FP). We might start with this:
HP/FP !! Very low HP !! Low HP !! Medium HP !! High HP !! Very high HP
Very weak FP Retreat! Retreat! Defend Defend Attack
Weak FP Retreat! Defend Defend Attack Attack
Medium FP Retreat! Defend Attack Attack Full attack!
High FP Retreat! Defend Attack Attack Full attack!
Very high FP Defend Attack Attack Full attack! Full attack!
This translates to:
  IF MonsterHP IS VeryLowHP AND MonsterFP IS VeryWeakFP THEN Retreat  IF MonsterHP IS LowHP AND MonsterFP IS VeryWeakFP THEN Retreat  IF MonsterHP IS MediumHP AND MonsterFP is VeryWeakFP THEN Defend 
...and so on. It is important to note that multiple rules can fire at once, and very often, they will. The distinction between "very low" and "low" is probably fuzzy at many points, and at such points, both rules will fire. If it is more "very low" than it is low, then the "very low" rule will generate a stronger response. The program will evaluate all the rules that fire and use an appropriate defuzzification method to generate its actual response. An implementation of this system might use either the matrix or the explicit IF/THEN form. The matrix makes it easy to visualize the system, but it also makes it impossible to add a third variable just for one rule, so it is less flexible. There is no inherent pattern in the matrix. It appears as if the rules were just made up, and indeed they were. This is both a strength and a weakness of fuzzy logic in general. It is often impractical or impossible to find an exact set of rules or formulae for dealing with a specific situation. For a sufficiently complex game, a mathematician would not be able to study the system and figure out a mathematically accurate set of rules. However, this weakness is intrinsic to the realities of the situation, not of fuzzy logic itself. The strength of the system is that even if one of the rules is wrong, even greatly wrong, other rules that are correct are likely to fire as well and they may compensate for the error. This does not mean a fuzzy system should be sloppy. Depending on the system, it might get away with being sloppy, but it will underperform. While the rules are fairly arbitrary, they should be chosen carefully. If possible, an expert should decide on the rules, and the sets and rules should be tested vigorously and refined as needed. In this way, a fuzzy system is like an expert system. (Fuzzy logic is used in many true expert systems, as well.)

See also

Combs method

 

<< PreviousWord BrowserNext >>
cleanness
hversu noregr byggdist
diisopropyltryptamine
youri djorkaeff
solar system by size
saint thomas of villanueva
regan
australian industrial relations commission
matsuri
clarence earl gideon
florida parishes
the devil's playground
structuration theory
list of assets owned by bertelsmann
gorilla grodd
julian c. dixon
haiduk peak
advanced soaring concepts
patrologia latina
dalanzadgad
hiroyuki imaishi
halbert and nancy robinson center for young scholars
mexican orangetip
mount girouard
xb 28 dragon
wisconsin v. yoder
australian labour law
mount inglismaldie
state cessions
southern cross ten
advanced soaring concepts falcon
kingsborough community college
lothriel
oku
steve trevor
mount peechee
procurator general of the ussr
bob hartman
advanced soaring concepts spirit
auloniad
someone like you
ian alistair mackenzie
history of nuevo len
mount charles stewart