Rock Street, San Francisco

Introduction

This
is document contains a report on a Fuzzy Logic based system. The system which
was created using Matlab regulating a gaming weapon system, the values were
store in a Microsoft excel file. The Fuzzy logic system I have created is
linked to a game called Fornite developed by Epic games. It is essentially a
battle royal involving 100 players at a time, either individuals or up to
four-man squads, attempting to be the last man or team standing as they hunt
other players and avoid being killed themselves (Microsoft.com,
2017). The players start with no equipment hence why I thought the
weapon system would be interesting to research and develop. Figure 1 shows how
Fornite uses colour to distinguish between the weapon rarities. The system I
have developed determines the rarity of a weapon corresponding to the damage,
accuracy and range.  In this report, it
covers the research and testing that has been performed on the system and an
overall view of how the system works.

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Fuzzy Logic

Fuzzy logic was first established by Lukasiewicz in the
1930s and in 1965 the proposal of Fuzzy set theory was introduced by Lofi Zadeh
(Dumitras and Moschytz, 2007).  After the 1960’s Fuzzy logic techniques was
used in engineering applications, through the concept of fuzzy sets.  Furthermore in 1968, Fuzzy logic continued to
find more importance across multiple fields as it integrates its subjectivity
and imprecision into the model formulation and solution. It gains even more
importance when used in a model system that are difficult to define precisely,
its ability to quantitatively and qualitatively model problems involving
vagueness and imprecision has made its use more interesting (Sabri, Aljunid and Salim, 2017).

Fuzzy logic deals successfully with models that have
fuzziness or vagueness, unlike other methods, such as classical theory.  It is different as it allows values to be more
accurate than Boolean values. Boolean values allow only false (0) or True
(1).  With Fuzzy Logic, the variables in
between 0 and 1 are taken into account. For example; a player is shot, the user
getting shot or not is where Boolean is used however if the player was behind
cover, the percentage of him getting hit would vary, and take less damage.

As Fuzzy logic is simple to use and to understand, with the
flexibility it offers, hence why it offers several reasons to use it.  In the modern times, Fuzzy system is used and
combined with many industries to enable an easier experience for everyone that
is involved. It has been used for both research/analysis and practical across a
platform of industries such as computing, business and engineering.

Literature Review

Fuzzy logic is a superset of conventional logic that been
drawn-out to the partial concept between the Boolean value. Some facts are not
binary, which means they may belong to more than one category, this means that
it allows an input to have multiple degree of truth or false rather the general
Boolean true or false. Fuzzy
uses a set of input, which then it fuzzify the point on the graph where the
values are made less strict (crisp). The data is run through the list of rules
to determine the combined data on the output value (Ross, Booker and Parkinson, 2002).his followed by the data been
defuzzified to get a fixed value, which states the output of the membership to
determine the variable.

Fuzzy
logic is becoming a vital part for game development. There are many games,
which have used Fuzzy logic and made it essential for the game to run smoothly.

For example, the Star Wars game ‘Knights Of The Old Republic’ is a simple
example of how Fuzzy logic is used. It uses the Light side/Dark side scale to
determine the player’s outcome. The player’s decision across the game gathers
information so that the player gradually moves from one end of the scale to the
other as seen in figure 2 below.

The influences from the player reacts to the character’s ability and
trait they can acquire.it is seen as binary at first however the main point of
what makes it fuzzy is the way the player’s action across the entire game
contributes to it. The game bases the binary choices and turns them into one
fuzzy value creating a clear frame of reference for the players and designer
alike (Quora, 2017).

Another example of how Fuzzy logic is used in gaming is the
game The Sims, which is one of the most played games in history. The characters
are given different outputs (their mood) which ranges from 0-100, which
reflects on how they can interact with other characters or gain certain traits(Ieeexplore.ieee.org, 2017).

For example having a low hygiene would mean it would be harder to interact with
other characters in the game. This system with the help of fuzzy logic improves
the games method to a more realistic approach rather than just a Boolean output
of happy or sad. Although the system created isn’t directly linked to gaming AI,
it is just one of the branches of the broader field of artificial
intellingence.