Forward chaining inference rules are required when reasoning is based on complex inter-linked conditions. We use RETE-based inference forward chain business rule to implement complex rule-based, decision making for production or expert systems using .Net framework. Forward chaining starts with the available data and uses inference rules to extract more data (from an end user, for example) until a goal is reached.
Our rule engine uses RETE algorithm, which is an efficient pattern-matching algorithm for implementing production rule systems.
Your application needs a forward chain inference engine if:
- It needs to implement a pattern-matching algorithm.
- It requires a data-driven reasoning mechanism.
- It needs a runtime and on-the-fly decision based on the facts and rules.
- It is a rule-based, decision making or expert system implemented in .Net framework.
Whenever new facts are asserted into a system, many rules may need to be re-evaluated to determine which are satisfied by these facts. A brute-force implementation could simply re-evaluate all existing rules against the newly introduced facts. This approach would achieve a result but would exhibit a very poor performance, as the number of rules and facts being considered have increased. Dr. Charles L. Forgy developed an algorithm that constructs a sophisticated network of nodes to represent the conditions associated with each rule and the dependencies between those conditions.
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