Planejamento baseado em busca e aprendizagem

Abstract

For decades, several planning systems have been proposed in order to nd solutions to many of the dierent problems encountered in everyday life. However, only in recent years, have planning systems started to obtain satisfactory results through the use of search methods for building plans. As a successful planner, Fast Forward combines the execution of two search methods, Enforced Hill Climbing and Best First Search. Techniques introduced by Fast Forward have also been applied in many other recent planners. Although Fast Forward strategy presents an enhanced performance when compared to alternative methods, it shows some weaknesses. For some planning problems Enforced Hill Climbing cannot escape from local maxima/minima, which means it might get stuck at dead ends, thus leading to a failure. In the event of such occurrences, a complete search is performed by Best First Search, which may fail due to lack of memory space. Note that these troubles could aect any search algorithm. This paper describes the development of a planner, called SLPlan, based on optmized versions of the algorithms Enforced Hill Climbing and Learning Real-Time A* that aim to reduce the impact of the troubles cited before in order to build plans more eciently. Besides, the application of planning techniques in solving relevant problems is of particular concern for the planning community. The generation of business processes ( work- ow) is one of the areas that can benet from the use of planning. This dissertation also species how SLPlan was applied to the automatic generation of business processes models.