Forward versus Backward Chaining
As if choosing between different BRE vendors wasn't tough enough, there's an added consideration: is the problem you are trying to solve more amenable to a forward or backward chaining approach? Forward chaining, which is the default behavior of most commercial and open-source rule engines, is described as
An inference engine using forward chaining searches the inference rules until it finds one where the If clause is known to be true. When found it can conclude, or infer, the Then clause, resulting in the addition of new information to its dataset.
while backward chaining is described as
An inference engine using backward chaining would search the inference rules until it finds one which has a Then clause that matches a desired goal. If the If
clause of that inference rule is not known to be true, then it is added
to the list of goals (in order for your goal to be confirmed you must
also provide data that confirms this new rule).
So in other words one approach starts with some facts and applies rules to find all possible conclusions, the other starts with the desired conclusion(s) and works backwards to find supporting facts. You can sort of view these approaches as two variations on search, with each step forward or backward forming a tree, either spanning out forwards towards conclusions or spanning out backwards towards initial facts. In point of fact, most rule systems don't go about finding all possible branches in this tree but use some sort of planning or goal setting to guide their search.
What are the advantages of one approach over the other and when would you use each? As it turns out, they can both solve the same problems. Most computer science students have to show at one point in their studies that any backward chaining rule system can be rewritten as an equivalent forward chaining system. Showing the reverse isn't quite so easy, but can be done in a graduate course. So both forward and backward chaining approaches can solve the same problems, but they have somewhat different performance characteristics, and certain problems are much easier to express in one or the other. Charles Forgy put it very succinctly in one of his articles on backward versus forward chaining (sorry, no link. The articles don't appear to be available now that Rulespower has been acquired by Fair Isaac)
You might ask why you would want to use a forward chaining system if you have to write rules to manage subgoals. After all, backward chaining systems automatically manage the subgoals. There answer is very simple: Backward chaining systems are more limited than forward chaining systems. There are many kinds of tasks that can be handled easily with a forward chaining system that are either difficult or impossible with a backward chaining system. Backward chaining systems are good for diagnostic and classification tasks, but they are not good for planning, design, process monitoring, and quite a few other tasks. Forward chaining systems can handle all these tasks.
So, if you already know what you are looking for, e.g. a customer that might be committing fraud, a patient at risk for breast cancer, etc., then backward chaining may be a good solution. On the other hand, if you don't necessarily know the final state of your solution, e.g. improvements to a business process, suggesting next steps in a due diligence investigation, or directing data transformations in an ETL process, then a forward chaining approach my be preferrable.
On the design front, backward chaining product will often have better justification or explanation mechanisms, i.e. explanations on how you arrived at a particular goal. If this is important to you, as it might be if you are working in clinical medicine, then a backward chaining solution might be a good fit.
On the performance front, there are certain circumstances where backward chaining might be better. For instance, if you have a small number of rules and a huge number of facts, you might be able to lazy load only those facts that are relevant to fulfilling goals.
If you'd like to play around a bit with a backward chaining rule engine, give Mandarax a try. Also, some commercial BRE's provide backward chaining as well as some other rule execution algorithms.
Feeling pretty good about your grasp of backward and forward chaining? Did you know there are hybrid approaches that combine both backward and forward chaining? Yep, it's true. But we won't go there today.