Julie A. Dickerson , Bart Kosko
Fuzzy Engineering , Prentice Hall, 1997
Virtual Worlds
ð Fuzzy cognitive maps (FCMs) can structure virtual
worlds that change with time. An FCM links causal
events, actors, values, goals, and trends in a fuzzy
feedback dynamical system. An FCM lists the fuzzy
rules or causal flow paths that relate events.
ð Complex FCMs can endow virtual worlds with new
or chaotic equilibrium behavior. Simple FCMs give
virtual worlds with periodic behavior. They map input
states to limit-cycle equilibria. An FCM limit cycle
repeats a sequence of events or a chain of actions and
responses. Limit cycles can control the steady-state
rhythms and patterns in a virtual world.
2
Nested FCMs
ð In nested FCMs each causal concept can control
its own FCM or fuzzy function approximator. This
gives levels of fuzzy systems that can choose goals
and causal webs as well as move objects and guide
actors in the webs. FCM matrices sum to give a
combined FCM virtual world for any number of
knowledge sources.
ð In complex FCMs the user can choose the
dynamical structure of the virtual world from a
spectrum that ranges from mildly to. wildly
nonlinear.
3
Virtual world is a dynamical system
ð It changes with time as the user or an actor moves
through it. In the simplest case only the user moves in
the virtual world. In general both the user and the
virtual world change and they change each other.
ð Actors cause events to happen as they move in a virtual
world. They add new patterns of cause and effect and
respond to old ones. The virtual world changes their
behavior and can change its own web of cause of effect.
ð This feedback causality between actors and their
virtual world makes up a complex dynamical system
that can model events, actors, actions, and data as they
unfold in time.
4
Fuzzy dynamical worlds
ð Virtual worlds are fuzzy as well as their feedback:
ð Events occur and concepts hold only to some degree.
ð Events cause one another to some degree.
ð In this sense virtual worlds are fuzzy causal worlds.
They are fuzzy dynamical systems.
ð In a virtual world the concept nodes can stand for
events, actions, values, moods, goals, or trends. The
causal edges state fuzzy rules or causal flows between
concepts. In a predator-prey world survival threat
increases prey runaway. The degree of runaway grows
or falls as the degree of threat grows or falls.
5
Fuzzy cognitive maps can structure
virtual worlds
6
Embedded fuzzy system
Embedded fuzzy systems drive lower-level
fuzzy systems for animation, sounds, and
other virtual world outputs
Here a shark finds a school of fish. The shark attacks and
7
the fish flee.
FCM system
ð The FCM system turns each picture into a matrix of
fuzzy rule weights. The system weights and adds the
FCM matrices to combine any number of causal
pictures.
ð More FCMs tend to sum to a better picture of the
causal web with rich tangles of feedback and fuzzy
edges even if each expert gives binary (present or
absent) edges.
8
FCM system
ð This makes it easy to add or delete actors or to change
the background of a virtual world or to combine virtual
worlds that are disjoint or overlap.
ð We can also let an FCM node control its own FCM to
give a nested FCM in a hierarchy of virtual worlds.
9
Positive and negative edge rules
Directed edges stand for fuzzy rules or the partial causal
flow between the concepts. The sign (+ or ) of an edge
stands for causal increase or decrease. The positive edge
rule states that a survival threat increases runaway. It is a
positive causal connection.
The runaway response grows or falls as the threat grows or falls.
10
FCM with five concept nodes
11
FCM with five concept nodes
12
Calculation of state vectors
State vectors Cn cycle through the FCM adjacency
matrix E: C1 -> E -> C2 -> E -> C3 &
The system nonlinearly transforms the weighted
input to each node Ci:
13
Signal function
Sigmoid function
With large C > 0 approximates a binary threshold function
14
Cycle
1
Simple threshold FMCs quickly converge to stable limit
cycles or fixed points. In this case binary thresholding
yields the four-step limit cycle.
15
Three bivalent FMCs
16
Augmented FCMs
FCM matrices additively combine to form new FCMs.
This allows combination of FCMs for different actors or
environments in the virtual world. The new (augmented)
FCM includes the union of the causal concepts for all the
actors and the environment in the virtual world.
If an FCM does not include a concept then those rows
and columns are all zero. The sum of the augmented
(zero-padded) FCM matrices for each actor forms the
virtual world:
=
17
Augmented FCM
18
Augmented FCM
The FCM sum helps knowledge acquisition. Any number of
experts can describe their FCM virtual world views and one
can weight and combine them. The strong law of large
numbers [13] ensures that the knowledge estimate F improves
with the expert sample size n if we view the experts as
independent (unique) random knowledge sources with finite
variance (bounded uncertainty) and identical distribution
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(same problem-domain focus).
Nested FCMs
ð The FCM can combine simple actions to model
intelligent behavior. Each node in turn can control
its own simple FCM in a nested FCM.
ð Complex actions such as walking emerge from
networks of simple reflexes. Nested simple FCMs can
mimic this process as a net of finite state machines
with binary limit cycles.
ð FCM nesting extends to any number of fuzzy sets for
the inputs. A concept can divide into smaller fuzzy sets
or subconcepts. The edges or rules link the sets. This
leads to a discrete multivalued output for each node.
20
Nested FCMs can divide a concept
into subconcepts
21
Subconcepts map to other concepts
22
Virtual Undersea World
A simple FCM for a virtual dolphin. It lists a causal web of
goals and actions in the life of a dolphin.
23
Virtual dolphin
24
Modelling survival threat
We can model the effect of a survival threat on the dolphin
FCM as a sustained input to D9. This means D9 = 1 for all time:
Then:
The arrow stands for a threshold operation with 1 /2 as the
threshold value. C1 keeps D9 on since we want to study the
effect of a sustained threat. C1 shows that when threatened
the dolphins cluster in a herd and flee the threat.
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The negative rules the ninth row of ED show that a threat to
survival turns off other actions. The FCM in converges to the
limit cycle C1 -> C2 -> C3 -> C4 -> C5 ->C1 & while the
threat lasts:
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