In this post I switch from talking about Blazor infrastructure to focusing on application logic. I will discuss rules-based processing, which is an approach to problem solving that deviates from the typical procedural programming model.<\/p>\n\n\n\n
Rules-based processing came out of AI research in the 1960s and 1970s, through work on Expert Systems<\/a>. The intent was to build a system that can deduce new facts from a set of existing facts by applying a series of rules. These rules would be specified by “experts” in a given field, thus the new system would have the appearance of being guided by these experts themselves.<\/p>\n\n\n\n The term “rules-based processing” may be a bit misleading. The “rules” here are not constraints. Rather, they are if-then<\/em> constructs that allow a set of facts to be progressively expanded. For example, from the rules for Crazy Eights<\/a>:<\/p>\n\n\n\n Many procedural programming languages support this type of logic through constructs such as if-then<\/em> and switch<\/em>. Rules-based systems differ from procedural languages in several ways:<\/p>\n\n\n\n These differences may cause surprising results for those more used to procedural programming.<\/p>\n\n\n\n Early rules-based systems performed a brute-force approach to applying rules based on fact changes by testing each rule for each change. These systems were severely limited in the number of rules they could process due to the limitations in available computer processing at the time. In the mid-1970s Charles L. Forgy created the Rete Algorithm<\/a>, which greatly improved the processing time for such a system. This algorithm uses a special structure that has indexing to help locate applicable conditions for rules associated with fact changes. A consequence of this is that rules must be compiled into this special structure at the beginning of processing, and that facts need to be specifically inserted into and updated within this structure.<\/p>\n\n\n\n There are many modern implementations of the Rete algorithm, including some built into commercial decision support systems. In this post we will use NRules<\/a>, which is an open-source tool built in C#.<\/p>\n\n\n\n Although the most common application for rules-base processing is in business decision making, I will instead show how the approach can be used to play the game of Tic-Tac-Toe. Hopefully the study of a fairly trivial game will allow the ideas to be presented clearly.<\/p>\n\n\n\n The game of Tic-Tac-Toe involves two players alternatively placing markers (typically “X” and “O”) in cells of a 3×3 grid until one player has three of their markers in a row – horizontally, vertically, or diagonally – or until there are no more empty cells. <\/p>\n\n\n\n Create a class library project:<\/p>\n\n\n\n I used .NET 10.0 (Preview) for my project. It should work equally well with .NET 9.<\/p>\n\n\n\n\n
A Simple Game<\/h2>\n\n\n\n
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