Merging Plans with Quantitative Temporal Constraints, Temporally Extended Actions, and Conditional Branches

We develop ml algorithm for merging plans that m’c represented in a richly expressive language. Specifically. we arc concerned with plans that have (i) quantitative temporal constraints, (ii} at.lions that are not instantaneous, but rather have temporal extent, and (iii) conditional branches. Given a set ~q of such plans, our algorithm finds a set of constraints that jointly ensure that the plans in 8 are mutually consistent, if such a set of constraints exists. The algorithm has three phases. In the first, it employs a new data structtu-e, a conditional simple temporal network (CSTN). identify conflicts between the plans. Next. it uses an approach developed by Yang (1997) to suggest a potential resolution of the identified conflicts. Finally, the CSTN is again used to check whether the proposed resolution observes all the temporal constraints. We have implemented our approach, mid we present preliminary experimental evidence about domain factors that influence its performance.