Windows Workflow Foundation (WF) in .NET 4

A new control flow activity called Flowchart has been added to make it possible for developers to use the Flowchart model to define a workflow. The Flowchart more closely resembles the concepts and thought processes that many analysts and developers go through when creating solutions or designing business processes. Therefore, it made sense to provide an activity to make it easy to model the conceptual thinking and planning that had already been done. The Flowchart enables concepts such as returning to previous steps and splitting logic based on a single condition, or a Switch / Case logic.


MSDN: Windows Workflow Foundation (WF) in .NET 4

.NET Windows Workflow Foundation (WF) WF Rule Engine using External WF Rules

Windows Workflow Foundation (WF) includes a flexible Rule Engine, allowing the Workflow developer to logically separate your application's business rules from the Workflow Assemblies. Rules allow you to model and implement the business requirements of your application into a set of declarative statements, which can be enforced at runtime.

A WF Rule is composed out of the following three separate parts:

1.A Condition which evaluates to a Boolean result.
2.A Then Action which is executed if the condition evaluates to true.
3.An optional Else Action which is executed if the condition evaluates to false.
The WF Visual Studio designers include a set of Rule Editor Dialogs. These editors allows the developer to create individual rules, and combine them together into a RuleSet. Alternatively, you can use any .NET language to create the a RuleSet through custom code.

Traditionally, WF Rules are used in the following two context:

1.Within a Declarative Rule Condition. Such a rule condition can be part of an IfElseBranchActivity, a WhileActivity, or any other activity which needs to evaluate a simple or compound boolean expression.
2.Within a Policy activity. A Policy activity evaluates a RuleSet, and applies the Then or Else actions of each Rule in the RuleSet, based upon the Rule's condition. The Rule Engine has the ability to apply forward chaining, which is a mechanism in which dependencies within rules are automatically detected, and Rules can be re-evaluated as a result of these dependencies.


Alternative Applications for the WF Rule Engin

Jess: The Rule Engine for the Java Platform

Jess is a rule engine and scripting environment written entirely in Sun's Java language by Ernest Friedman-Hill at Sandia National Laboratories in Livermore, CA. Using Jess, you can build Java software that has the capacity to "reason" using knowledge you supply in the form of declarative rules. Jess is small, light, and one of the fastest rule engines available. Its powerful scripting language gives you access to all of Java's APIs. Jess includes a full-featured development environment based on the award-winning Eclipse platform.

Jess uses an enhanced version of the Rete algorithm to process rules. Rete is a very efficient mechanism for solving the difficult many-to-many matching problem (see for example "Rete: A Fast Algorithm for the Many Pattern/ Many Object Pattern Match Problem", Charles L. Forgy, Artificial Intelligence 19 (1982), 17-37.) Jess has many unique features including backwards chaining and working memory queries, and of course Jess can directly manipulate and reason about Java objects. Jess is also a powerful Java scripting environment, from which you can create Java objects, call Java methods, and implement Java interfaces without compiling any Java code.

Rule-Based Systems and Identification Trees

Using a set of assertions, which collectively form the ‘working memory’, and a set of rules that specify how to act on the assertion set, a rule-based system can be created. Rule-based systems are fairly simplistic, consisting of little more than a set of if-then statements, but provide the basis for so-called “expert systems” which are widely used in many fields. The concept of an expert system is this: the knowledge of an expert is encoded into the rule set. When exposed to the same data, the expert system AI will perform in a similar manner to the expert.

Rule-based systems are a relatively simple model that can be adapted to any number of problems. As with any AI, a rule-based system has its strengths as well as limitations that must be considered before deciding if it’s the right technique to use for a given problem. Overall, rule-based systems are really only feasible for problems for which any and all knowledge in the problem area can be written in the form of if-then rules and for which this problem area is not large. If there are too many rules, the system can become difficult to maintain and can suffer a performance hit.

To create a rule-based system for a given problem, you must have (or create) the following:

• A set of facts to represent the initial working memory. This should be anything relevant to the beginning state of the system.
  


• A set of rules. This should encompass any and all actions that should be taken within the scope of a problem, but nothing irrelevant. The number of rules in the system can affect its performance, so you don’t want any that aren’t needed.
  


• A condition that determines that a solution has been found or that none exists. This is necessary to terminate some rule-based systems that find themselves in infinite loops otherwise.
  

Rule-Based Systems and Identification Trees - James Freeman-Hargis