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deploy to nuget and update docs
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Diff for: ‎.gitignore

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**/obj/
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**/bin/
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.vs/
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build/
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# Mobile Tools for Java (J2ME)
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.mtj.tmp/

Diff for: ‎README.md

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# cs-expert-system-shell
2-
C# implementation of an expert system shell
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C# implementation of an expert system shell, targeting .Net Core 1.1
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[![Build Status](https://travis-ci.org/chen0040/cs-expert-system-shell.svg?branch=master)](https://travis-ci.org/chen0040/cs-expert-system-shell)
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# Install
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Run the following command to install:
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Install-Package cs-expert-system-shell
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# Usage
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The sample code below shows how to create a rule engine and initialize it with a set of rules:
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```cs
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using chen0040.ExpertSystem;
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public RuleInferenceEngine getInferenceEngine()
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{
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RuleInferenceEngine rie = new RuleInferenceEngine();
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Rule rule = new Rule("Bicycle");
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rule.AddAntecedent(new IsClause("vehicleType", "cycle"));
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rule.AddAntecedent(new IsClause("num_wheels", "2"));
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rule.AddAntecedent(new IsClause("motor", "no"));
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rule.setConsequent(new IsClause("vehicle", "Bicycle"));
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rie.AddRule(rule);
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rule = new Rule("Tricycle");
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rule.AddAntecedent(new IsClause("vehicleType", "cycle"));
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rule.AddAntecedent(new IsClause("num_wheels", "3"));
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rule.AddAntecedent(new IsClause("motor", "no"));
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rule.setConsequent(new IsClause("vehicle", "Tricycle"));
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rie.AddRule(rule);
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rule = new Rule("Motorcycle");
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rule.AddAntecedent(new IsClause("vehicleType", "cycle"));
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rule.AddAntecedent(new IsClause("num_wheels", "2"));
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rule.AddAntecedent(new IsClause("motor", "yes"));
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rule.setConsequent(new IsClause("vehicle", "Motorcycle"));
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rie.AddRule(rule);
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rule = new Rule("SportsCar");
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rule.AddAntecedent(new IsClause("vehicleType", "automobile"));
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rule.AddAntecedent(new IsClause("size", "medium"));
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rule.AddAntecedent(new IsClause("num_doors", "2"));
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rule.setConsequent(new IsClause("vehicle", "Sports_Car"));
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rie.AddRule(rule);
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rule = new Rule("Sedan");
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rule.AddAntecedent(new IsClause("vehicleType", "automobile"));
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rule.AddAntecedent(new IsClause("size", "medium"));
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rule.AddAntecedent(new IsClause("num_doors", "4"));
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rule.setConsequent(new IsClause("vehicle", "Sedan"));
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rie.AddRule(rule);
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rule = new Rule("MiniVan");
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rule.AddAntecedent(new IsClause("vehicleType", "automobile"));
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rule.AddAntecedent(new IsClause("size", "medium"));
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rule.AddAntecedent(new IsClause("num_doors", "3"));
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rule.setConsequent(new IsClause("vehicle", "MiniVan"));
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rie.AddRule(rule);
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rule = new Rule("SUV");
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rule.AddAntecedent(new IsClause("vehicleType", "automobile"));
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rule.AddAntecedent(new IsClause("size", "large"));
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rule.AddAntecedent(new IsClause("num_doors", "4"));
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rule.setConsequent(new IsClause("vehicle", "SUV"));
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rie.AddRule(rule);
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rule = new Rule("Cycle");
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rule.AddAntecedent(new LessClause("num_wheels", "4"));
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rule.setConsequent(new IsClause("vehicleType", "cycle"));
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rie.AddRule(rule);
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rule = new Rule("Automobile");
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rule.AddAntecedent(new IsClause("num_wheels", "4"));
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rule.AddAntecedent(new IsClause("motor", "yes"));
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rule.setConsequent(new IsClause("vehicleType", "automobile"));
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rie.AddRule(rule);
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return rie;
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}
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```
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The sample code below shows how to use forward chaining in the rule engine to derive more facts from the known facts using rules:
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```cs
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RuleInferenceEngine rie = getInferenceEngine();
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rie.AddFact(new IsClause("num_wheels", "4"));
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rie.AddFact(new IsClause("motor", "yes"));
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rie.AddFact(new IsClause("num_doors", "3"));
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rie.AddFact(new IsClause("size", "medium"));
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console.WriteLine("before inference");
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console.WriteLine("{0}", rie.Facts);
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console.WriteLine("");
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rie.Infer(); //forward chain
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console.WriteLine("after inference");
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console.WriteLine("{0}", rie.Facts);
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console.WriteLine("");
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```
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The sample code below shows how to use the backward chaining to reach conclusion for a target variable given a set of known facts:
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```cs
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RuleInferenceEngine rie = getInferenceEngine();
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rie.AddFact(new IsClause("num_wheels", "4"));
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rie.AddFact(new IsClause("motor", "yes"));
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rie.AddFact(new IsClause("num_doors", "3"));
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rie.AddFact(new IsClause("size", "medium"));
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console.WriteLine("Infer: vehicle");
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List<Clause> unproved_conditions = new List<Clause>();
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Clause conclusion = rie.Infer("vehicle", unproved_conditions);
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console.WriteLine("Conclusion: " + conclusion);
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Assert.Equal(conclusion.Value, "MiniVan");
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```
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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:
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```cs
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RuleInferenceEngine rie = getInferenceEngine();
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console.WriteLine("Infer with All Facts Cleared:");
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rie.ClearFacts();
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List<Clause> unproved_conditions = new List<Clause>();
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Clause conclusion = null;
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while (conclusion == null)
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{
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conclusion = rie.Infer("vehicle", unproved_conditions);
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if (conclusion == null)
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{
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if (unproved_conditions.Count == 0)
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{
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break;
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}
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Clause c = unproved_conditions[0];
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console.WriteLine("ask: " + c + "?");
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unproved_conditions.Clear();
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console.WriteLine("What is " + c.Variable + "?");
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String value = Console.ReadLine();
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rie.AddFact(new IsClause(c.Variable, value));
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}
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}
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console.WriteLine("Conclusion: " + conclusion);
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console.WriteLine("Memory: ");
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console.WriteLine("{0}", rie.Facts);
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```

Diff for: ‎deploy.ps1

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dotnet pack cs-expert-system-shell/cs-expert-system-shell.csproj /p:PackageVersion=1.0.1 --configuration release --include-source --output ../build
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nuget push build/cs-expert-system-shell.1.0.1.nupkg -Source https://www.nuget.org/api/v2/package

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