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    <p>You're certainly not talking about 'scifi', but a lot falls outside the standard stuff software engineers are typically exposed to. I spent the last eight years building and using rule engines to do inference over semi-structured data in the retail world. </p> <p>Doing inference over data is a well established field. There are basically four classes of problems associated with this field:</p> <ul> <li>Knowledge acquisition (getting rules out of peoples heads and into the code/rules)</li> <li>Knowledge representation 'KR' (how to represent your data &amp; rules)</li> <li>Efficient pattern matching (matching a rule form a large ruleset against large number of facts/data)</li> <li>Inference / Reasoning (drawing further conclusions from rule matches, ie rules triggering more rules) </li> </ul> <p>For knowledge acquisition look at: <a href="http://en.wikipedia.org/wiki/Ripple_down_rules" rel="nofollow noreferrer">Ripple Down Rules</a> and Decision Trees, they go a long way and are easy to understand. Alternatively, the vast field <a href="http://en.wikipedia.org/wiki/Machine_learning" rel="nofollow noreferrer">Machine Learning</a> offer a variety of approaches to derive models from data.</p> <p>For knowledge representation look at <a href="http://en.wikipedia.org/wiki/Resource_Description_Framework" rel="nofollow noreferrer">RDF</a> and <a href="http://en.wikipedia.org/wiki/Web_Ontology_Language" rel="nofollow noreferrer">Owl</a>, and to a lesser degree <a href="http://en.wikipedia.org/wiki/Conceptual_graphs" rel="nofollow noreferrer">Conceptual Graphs</a>. In terms of expressiveness, RDF &amp; CG are roughly equivalent. The basic concept behind both is a serialisation independent graph (triple) representation of data. </p> <p>For pattern matching, the classic algorithm is <a href="http://en.wikipedia.org/wiki/Rete_algorithm" rel="nofollow noreferrer">Rete</a>, by Charles Forgy. </p> <p>For inference, there are two typical strategies: <a href="http://en.wikipedia.org/wiki/Forward_chaining" rel="nofollow noreferrer">Forward chaining</a> and <a href="http://en.wikipedia.org/wiki/Backward_chaining" rel="nofollow noreferrer">backward chaining</a>. Forward chaining is done over a ruleset like this:</p> <p>The data setup:</p> <pre><code> Rule 1: If A Then B Rule 2: If B Then C Facts: A </code></pre> <p>The execution:</p> <pre><code>Do { Newfacts = Eval(RuleSet, Facts) Facts = Facts + Newfacts } while (NewFacts.Count &gt; 0) </code></pre> <p>Feed the data A to this little algorithm, and you will 'infer' (discover) fact C, from the data, thanks to the rulebase. Note that there a a lot of gotchas with inference, especially around things like <a href="http://en.wikipedia.org/wiki/Non-monotonic_logic" rel="nofollow noreferrer">non-monotonic reasoning</a> (not just adding facts, but changing or removing facts, possibly giving rise to contradictions or loops in the inference). </p> <p>A simplistic and naive way to get some kind inference going would be to use a database and use joins to match up facts (statements). This may be enough for some applications. When it comes to reasoning, it's easy to get sucked into a world of complications and not-quite-there technologies. Keep it simple. </p>
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