This is a basic implementation of a fuzzy logic system in C#. The code defines classes for FuzzySet, LinguisticVariable, FuzzyRule, FuzzyRuleSet, and FuzzyInferenceSystem.
To use this implementation in your C# project, follow these steps:
- Copy the code into your project.
- Create
LinguisticVariable
,FuzzySet
, andFuzzyRule
objects to define your fuzzy logic system. - Add the
FuzzySet
objects to theLinguisticVariable
using theAddFuzzySet
method. - Add the
FuzzyRule
objects to aFuzzyRuleSet
using theAddRule
method. - Create a
FuzzyInferenceSystem
object using theFuzzyRuleSet
. - Call the
Infer
method of theFuzzyInferenceSystem
object to get the output value based on the input values.
Here's an example usage of this implementation within the context of a game:
using UnityEngine;
using System.Collections.Generic;
using FuzzyLogic;
public class FuzzyExample : MonoBehaviour
{
public void Start()
{
// Create the linguistic variables
LinguisticVariable distance = new LinguisticVariable("Distance");
LinguisticVariable speed = new LinguisticVariable("Speed");
LinguisticVariable acceleration = new LinguisticVariable("Acceleration");
// Create the fuzzy sets for each linguistic variable
FuzzySet near = new TriangularFuzzySet("Near", 0, 0, 10);
FuzzySet far = new TriangularFuzzySet("Far", 5, 10, 15);
FuzzySet slow = new TriangularFuzzySet("Slow", 0, 0, 5);
FuzzySet fast = new TriangularFuzzySet("Fast", 5, 10, 15);
FuzzySet negative = new TriangularFuzzySet("Negative", -15, -10, -5);
FuzzySet positive = new TriangularFuzzySet("Positive", 5, 10, 15);
// Add the fuzzy sets to their corresponding linguistic variables
distance.AddFuzzySet(near);
distance.AddFuzzySet(far);
speed.AddFuzzySet(slow);
speed.AddFuzzySet(fast);
acceleration.AddFuzzySet(negative);
acceleration.AddFuzzySet(positive);
// Create the fuzzy rules
FuzzyRule rule1 = new FuzzyRule();
rule1.Antecedents.Add(distance, near);
rule1.Antecedents.Add(speed, slow);
rule1.Consequents.Add(acceleration, negative);
FuzzyRule rule2 = new FuzzyRule();
rule2.Antecedents.Add(distance, far);
rule2.Antecedents.Add(speed, fast);
rule2.Consequents.Add(acceleration, positive);
// Add the fuzzy rules to a rule set
FuzzyRuleSet ruleSet = new FuzzyRuleSet();
ruleSet.AddRule(rule1);
ruleSet.AddRule(rule2);
// Create the fuzzy inference system
FuzzyInferenceSystem fis = new FuzzyInferenceSystem();
// Define the input values
Dictionary<LinguisticVariable, float> inputs = new Dictionary<LinguisticVariable, float>();
inputs.Add(distance, 6);
inputs.Add(speed, 9);
// Infer the output value
float output = fis.Infer(inputs, acceleration, ruleSet);
// Display the output in console
print(output);
}
}
In this example, the fuzzy logic system is used to determine the acceleration of an object in a game based on its distance from a target and its current speed. Two fuzzy rules are defined based on the inputs and outputs, and the Infer
method is called to get the acceleration value. You can modify the fuzzy logic system definition and rules to suit your specific game requirements.
An abstract class that represents a fuzzy set. It has a Name
property and an abstract GetMembership
method that calculates the membership degree of a value in the set.
A class that represents a linguistic variable. It has a Name
property and a list of FuzzySet
objects that belong to it. You can add FuzzySet
objects to the variable using the AddFuzzySet
method.
A class that represents a fuzzy rule. It has two dictionaries: Antecedents
and Consequents
. The Antecedents
dictionary maps LinguisticVariable
objects to their corresponding FuzzySet
objects, while the Consequents
dictionary maps LinguisticVariable
objects to their corresponding FuzzySet
objects.
A class that represents a fuzzy rule set. It has a list of FuzzyRule
objects. You can add FuzzyRule
objects to the rule set using the AddRule
method.
A class that represents a fuzzy inference system. It has a FuzzyRuleSet
object. You can create a FuzzyInferenceSystem
object by passing a FuzzyRuleSet
object to its constructor. It has an Infer
method that takes a dictionary of input values and a LinguisticVariable
object representing the output variable. The Infer
method returns a float value representing the output.
This fuzzy logic system implementation can be used in games to make decisions based on fuzzy inputs. You can use it to create AI agents that make decisions based on a combination of factors like distance, speed, health, and more. By modifying the fuzzy sets and rules, you can fine-tune the behavior of the agents to suit your game requirements.