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 canno: be solved directly (i.e., without searching) by simply applying the appropriate algorithms. Expert systems are usually applied to solve diyerent search problems. Honever, only a few expert systems with the truth maintenance capability are able to solve them esficiently. A truth maintenance system is used to organize data into data abstractions that represent points in search spaces, and to maintain data consistency, while inference engine is used to examine problem states and to guide search process. Knowing that expert systems spend about 90% of time in each recognize-act cycle performing pattern matching, it is obvious that the pattern-matching algorithm represents one of the most critical component of inference engine. Two most popular pattern-matching algorithms, TREAT and RETE, are applied in almost all expert systems. Although these algorithms are very efJicient in applications which do not require the examination of search spaces, they perform some futile work in search probiem solving. In this paper’ we propose a patternmatching algorithm, specially tuned for the solution of searc*b problems, which override.; the drawbacks of TREAT and PJTE algorithms, and incrtiases the efficiency of the Overilll system.