Yaeh efficiency is quite bad - here is the last part of what i have on PS

Forward chaining is functional when the initial facts are known but with no conclusion or hypotheses. An advantage of production system it that it can also work in the reverse.
If you know what the conclusion might be, forward chaining systems will be inefficient. You will keep on forward chaining until you run out of rules that apply or you have added your hypothesis to the working memory. But in the process the system is likely to do an immense amount of irrelevant work. However when matching what rules may give the conclusion, every rule and possible starting point will be given and you are left with a very large amount of data to sift through. Disjunctive knowledge in the part of the rule further adds to inefficiency.
Production systems are ideal where initial states change from user to user (such as in Mycin, a system created to help doctors with diagnosis and treatment of bacterial infection). When the production system is programmed to apply rules in order there will be no conflict in the system. The *natural *modularity of the reresentaion means that each rule only has to define one small and independent piece of information, however this can also make it difficult to track the effects of adding a new rule. These types of systems are often very large in size which can cause lots of problems in computers but by each rule being independent it is possible to split the knowledge base, makign maintenanca and debuging a lot easier. The indepence also makes it very easy to add extra rules (as long as they don’t conflict).
The main positive aspect of production systems is their plausibility. Production systems can provide explanations and justifications for their decisions simply by displaying the rules that were applied to reach any given conclusion.
A very important feature for Mycin was that uncertainty (probability) is easier to programme into this type of representation than others.
Other than inefficiency, the main other weakness of production rules is its inability to express disjunctive knowledge in the side of a rule, adding to restriction of expressive adequacy.


Now i have to concentrate on the semantic network side of things

Yep, there is certainly a debate raging between proponents of production systems and proponents of Bayesian networks. Certainly, everything a PS can do, a BS can do. It''s just that some people don''t like the difficulties associated with eliciting probabilities for BNs. I honestly think that the best tool for the job depends on the job!

As for the semantic networks... just remember that every semantic network can be written in terms of first order logic. Of course, semantic nets are a much nicer way of looking at things (which is how I see BNs over PSs... much easier to see the influences withing a problem).

Cheers,

Timkin