Least Commitment on Variable Binding in Presence of Incomplete Knowledge

Abstract

Constraint Satisfaction techniques have been recognized to be effective tools for increasing the efficiency of least commitment planners. We focus on least commitment on variable binding. A constraint based approach for this issue has been previously proposed by Yang and Chan [21]. In this setting, the planning problem is mapped onto a Constraint Satisfaction Problem. Its variables represent domain objects and are defined on a finite domain of values; constraints remove inconsistent values from variable domains through constraint propagation. In many applications, however, it is not always convenient, if possible at all, to know in advance all objects belonging to variable domains. Thus, domain values should be retrieved during the plan construction only when needed. The interesting point is that data acquisition for each variable can be guided by the constraint (or the constraints) imposed on the variable itself, in order to retrieve only consistent values. For this purpose, we have extended a Partial Order Planner performing least commitment on variable binding. This extension can cope with incomplete knowledge. We use the Interactive Constraint Satisfaction framework defined in [12] in order to exploit the efficiency deriving from constraint propagation and the possibility of acquiring the domain knowledge during the plan construction. Experimental results and comparisons with related approaches show the effectiveness of the proposed technique.