https://github.com/chen0040/cs-expert-system-shell
C# implementation of an expert system shell
https://github.com/chen0040/cs-expert-system-shell
expert-system expert-system-shell rule-engine rules
Last synced: about 2 months ago
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C# implementation of an expert system shell
- Host: GitHub
- URL: https://github.com/chen0040/cs-expert-system-shell
- Owner: chen0040
- License: mit
- Created: 2017-05-09T14:47:46.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-05-20T04:35:55.000Z (over 8 years ago)
- Last Synced: 2025-08-01T07:56:54.170Z (2 months ago)
- Topics: expert-system, expert-system-shell, rule-engine, rules
- Language: C#
- Size: 23.4 KB
- Stars: 39
- Watchers: 5
- Forks: 21
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# cs-expert-system-shell
C# implementation of an expert system shell, targeting .Net Core 1.1
[](https://travis-ci.org/chen0040/cs-expert-system-shell)
# Install
Run the following command to install:
```bash
Install-Package cs-expert-system-shell
```
# Usage
The sample code below shows how to create a rule engine and initialize it with a set of rules:
```cs
using chen0040.ExpertSystem;
public RuleInferenceEngine getInferenceEngine()
{
RuleInferenceEngine rie = new RuleInferenceEngine();
Rule rule = new Rule("Bicycle");
rule.AddAntecedent(new IsClause("vehicleType", "cycle"));
rule.AddAntecedent(new IsClause("num_wheels", "2"));
rule.AddAntecedent(new IsClause("motor", "no"));
rule.setConsequent(new IsClause("vehicle", "Bicycle"));
rie.AddRule(rule);
rule = new Rule("Tricycle");
rule.AddAntecedent(new IsClause("vehicleType", "cycle"));
rule.AddAntecedent(new IsClause("num_wheels", "3"));
rule.AddAntecedent(new IsClause("motor", "no"));
rule.setConsequent(new IsClause("vehicle", "Tricycle"));
rie.AddRule(rule);
rule = new Rule("Motorcycle");
rule.AddAntecedent(new IsClause("vehicleType", "cycle"));
rule.AddAntecedent(new IsClause("num_wheels", "2"));
rule.AddAntecedent(new IsClause("motor", "yes"));
rule.setConsequent(new IsClause("vehicle", "Motorcycle"));
rie.AddRule(rule);
rule = new Rule("SportsCar");
rule.AddAntecedent(new IsClause("vehicleType", "automobile"));
rule.AddAntecedent(new IsClause("size", "medium"));
rule.AddAntecedent(new IsClause("num_doors", "2"));
rule.setConsequent(new IsClause("vehicle", "Sports_Car"));
rie.AddRule(rule);
rule = new Rule("Sedan");
rule.AddAntecedent(new IsClause("vehicleType", "automobile"));
rule.AddAntecedent(new IsClause("size", "medium"));
rule.AddAntecedent(new IsClause("num_doors", "4"));
rule.setConsequent(new IsClause("vehicle", "Sedan"));
rie.AddRule(rule);
rule = new Rule("MiniVan");
rule.AddAntecedent(new IsClause("vehicleType", "automobile"));
rule.AddAntecedent(new IsClause("size", "medium"));
rule.AddAntecedent(new IsClause("num_doors", "3"));
rule.setConsequent(new IsClause("vehicle", "MiniVan"));
rie.AddRule(rule);
rule = new Rule("SUV");
rule.AddAntecedent(new IsClause("vehicleType", "automobile"));
rule.AddAntecedent(new IsClause("size", "large"));
rule.AddAntecedent(new IsClause("num_doors", "4"));
rule.setConsequent(new IsClause("vehicle", "SUV"));
rie.AddRule(rule);
rule = new Rule("Cycle");
rule.AddAntecedent(new LessClause("num_wheels", "4"));
rule.setConsequent(new IsClause("vehicleType", "cycle"));
rie.AddRule(rule);
rule = new Rule("Automobile");
rule.AddAntecedent(new IsClause("num_wheels", "4"));
rule.AddAntecedent(new IsClause("motor", "yes"));
rule.setConsequent(new IsClause("vehicleType", "automobile"));
rie.AddRule(rule);
return rie;
}
```
The sample code below shows how to use forward chaining in the rule engine to derive more facts from the known facts using rules:
```cs
RuleInferenceEngine rie = getInferenceEngine();
rie.AddFact(new IsClause("num_wheels", "4"));
rie.AddFact(new IsClause("motor", "yes"));
rie.AddFact(new IsClause("num_doors", "3"));
rie.AddFact(new IsClause("size", "medium"));
console.WriteLine("before inference");
console.WriteLine("{0}", rie.Facts);
console.WriteLine("");
rie.Infer(); //forward chain
console.WriteLine("after inference");
console.WriteLine("{0}", rie.Facts);
console.WriteLine("");
```
The sample code below shows how to use the backward chaining to reach conclusion for a target variable given a set of known facts:
```cs
RuleInferenceEngine rie = getInferenceEngine();
rie.AddFact(new IsClause("num_wheels", "4"));
rie.AddFact(new IsClause("motor", "yes"));
rie.AddFact(new IsClause("num_doors", "3"));
rie.AddFact(new IsClause("size", "medium"));
console.WriteLine("Infer: vehicle");
List unproved_conditions = new List();
Clause conclusion = rie.Infer("vehicle", unproved_conditions);
console.WriteLine("Conclusion: " + conclusion);
Assert.Equal(conclusion.Value, "MiniVan");
```
The sample code below shows how to use the rule engine to ask more questions when it fails to reach conclusion for the target variable given a limited set of known facts:
```cs
RuleInferenceEngine rie = getInferenceEngine();
console.WriteLine("Infer with All Facts Cleared:");
rie.ClearFacts();
List unproved_conditions = new List();
Clause conclusion = null;
while (conclusion == null)
{
conclusion = rie.Infer("vehicle", unproved_conditions);
if (conclusion == null)
{
if (unproved_conditions.Count == 0)
{
break;
}
Clause c = unproved_conditions[0];
console.WriteLine("ask: " + c + "?");
unproved_conditions.Clear();
console.WriteLine("What is " + c.Variable + "?");
String value = Console.ReadLine();
rie.AddFact(new IsClause(c.Variable, value));
}
}
console.WriteLine("Conclusion: " + conclusion);
console.WriteLine("Memory: ");
console.WriteLine("{0}", rie.Facts);
```