Integrating Golog and Planning : An Empirical Evaluation
Jens Claßen, Viktor Engelmann, Gabriele Röger, Gerhard Lakemeyer · 2008
The Golog family of action languages has proven to be ausefulmeansforthehigh-levelcontrolofautonomous agents, such as mobile robots. In particular, the IndiGologvariant,whereprogramsareexecutedinanonline manner, is applicable in realistic scenarios where agents possess only incomplete knowledge about the state of the world, have to use sensors to gather necessaryinformationatruntimeandneedtoreacttospontaneous, exogenous events that happen unpredictably due to a dynamic environment. Often, the specification of such an agent’s program also involves that certain subgoals have to be solved by means of planning. IndiGolog supports this in principle by providing a variety of lookahead mechanisms, but when it comes to pure,sequentialplanning,theseusuallycannotcompete with modern state-of-the-art planning systems, most of which being based on the Planning Domain Definition Language PDDL. Previous theoretical results provide insights on the semantical compatibility between Gologand PDDL andhowtheycompareintermsofexpressiveness. Inthispaper,wecomplementtheseresults with an empirical evaluation that shows that equipping IndiGolog with a PDDL planner (FF in our case) pays off in terms of the runtime performance of the overall system. For that matter, we study a number of example application domains and compare the needed computationtimes for varying problemsizes and difficulties.