PDDL 2.1 supports modelling of complex temporal planning domains in which solutions must exploit concurrency. Few existing temporal planners can solve problems that require concurrency and those that do typically pay a performance price to deploy reasoning machinery that is not always required. In this paper we show how to improve the performance of forward-search planners that attempt to solve the full temporal planning problem, both by narrowing the use of the concurrency machinery to situations that demand it and also by improving the power of inference to prune redundant branches of the search space for common patterns of interaction in temporal domains that do require concurrency. Results illustrate the effectiveness of our ideas in improving the efficiency of a temporal planner that can solve problems with required concurrency, both in domains that exploit this ability and in those that do not.

We consider the problem of planning in domains with continuous linear numeric change. Such change cannot always be adequately modelled by discretisation and is a key facet of many interesting problems. We show how a forward-chaining temporal planner can be extended to reason with actions with continuous linear effects. We extend a temporal planner to handle numeric values using linear programming. We show how linear continuous change can be integrated into the same linear program and we discuss how a temporal-numeric heuristic can be used to provide the search guidance necessary to underpin continuous planning. We present results to show that the approach can effectively handle duration-dependent change and numeric variables subject to continuous linear change.