Pattern matching in search problem solving

Search problems generally fall into the class of NP hard problems. Many real problems including planning, diagnosing, allocation tasks, classification tasks and scenario making, require examination of large search spaces. These problems cannot be solved directly (i.e., without searching) by simply applying the appropriate algorithms. Expert systems are usually applied to solve different search problems. However, only a few expert systems with the truth maintenance capability are able to solve them efficiently. A truth maintenance system is used to organize data into data abstractions that represent points in search spaces, and also to maintain data consistency, while the inference engine is used to examine problem states and to guide the search process. Knowing that expert systems spend about 90% of their time in each recognize-act cycle performing pattern matching, it is obvious that the pattern matching algorithm represents one of the most critical components of the inference engine. Two of the most popular pattern matching algorithms TREAT and RETE, are applied in almost all expert systems. Although these algorithms are very efficient in applications which do not require the examination of search spaces, they perform some futile work in search problem solving. We propose a pattern matching algorithm, specially tuned for the solution of search problems, which override the drawbacks of TREAT and RETE algorithms and increases the efficiency of the overall system.