In most real-world reasoning problems, planning and scheduling phases are loosely coupled. For example, in project planning, the user comes up with a task list and schedules it with a scheduling tool like Microsoft Project. One can view automated planning in a similar way in which there is an action selection phase where actions are selected and ordered to reach the desired goals, and a resource allocation phase where enough resources are assigned to ensure the successful execution of the chosen actions. On the other hand, most existing automated planners studied in Artificial Intelligence do not exploit this loose-coupling and perform both action selection and resource assignment employing the same algorithm. The current work shows that the above strategy severely curtails the scale-up potential of existing state of the art planners which can be overcome by leveraging the loose coupling. Specifically, a novel planning framework called RealPlan is developed in which resource allocatio...

To date, no one planner has demonstrated clearly superior performance. Although researchers have hypothesized that this should be the case, no one has performed a large study to test its limits. In this research, we tested performance of a set of planners to determine which is best on what types of problems. The study included six planners and over 200 problems. We found that performance, as measured by number of problems solved and computation time, varied with no one planner solving all the problems or being consistently fastest. Analysis of the data also showed that most planners either fail or succeed quickly and that performance depends at least in part on some easily observable problem/domain features. Based on these results, we implemented a meta-planner that interleaves execution of six planners on a problem until one of them solves it. The control strategy for ordering the planners and allocating time is derived from the performance study data. We found that our meta-planner is able to solve more problems than any single planner, but at the expense of computation time.

Plan synthesis approaches in AI fall into two categories: domain-independent and domain-dependent. The domain-independent approaches are applicable across a variety of domains, but may not be very efficient in any one given domain. The domain-dependent approaches can be very efficient for the domain for which they are designed, but would need to be written separately for each domain of interest. The tediousness and the error-proneness of manual coding have hither-to inhibited work on domain-dependent planners. In this paper, we describe a novel way of automating the development of domain dependent planners using knowledge-based software synthesis tools. Specifically, we describe an architecture called CLAY in which the Kestrel Interactive Development System (KIDS) is used in conjunction with a declarative theory of domain independent planning, and the declarative control knowledge specific to a given domain, to semi-automatically derive customized planning code. We discuss what it means to write declarative theory of planning and control knowledge for KIDS, and illustrate it by generating a range of domainspecific planners using state space and plan space refinements. We demonstrate that the synthesized planners can have superior performance compared to classical refinement planners using the same control knowledge.