Despite their near dominance, heuristic state search planners still lag behind disjunctive planners in the generation of parallel plans in classical planning. The reason is that directly searching for parallel solutions in state space planners would require the planners to branch on all possible subsets of parallel actions, thus increasing the branching factor exponentially. We present a varian...

Despite their near dominance, heuristic state search planners still lag behind disjunctive planners in the generation of parallel plans in classical planning. The reason is that directly searching for parallel solutions in state space planners would require the planners to branch on all possible subsets of parallel actions, thus increasing the branching factor exponentially. We present a varian...

AI planners have to compromise between the speed of the planning process and the quality of the generated plan. Anytime planners try to balance these objectives by finding plans of better quality over time, but current anytime planners often do not make effective use of increasing runtime beyond a certain limit. We present a new method of continuing plan improvement, that works by repeatedly de...

The Desktop Hypercube is an interactive tool designed to help planners improve police services without additional response units (or to maintain a constant level of service with fewer response units). It calculates a number of variables, including travel times, workloads, and preventive patrol frequencies, thus giving planners the information they need to achieve various goals, such as reductio...

Effective coordination of distributed human planners requires timely communication of relevant information to ensure the overall coherence of activities and the compatibility of assumptions. This paper presents a framework called CODA that provides targeted information dissemination among distributed human planners as a way of improving coordination. Within CODA, each planner declares interest ...

Planners are powerful tools for problem solving because they provide a complete sequence of actions to achieve a goal from a particular initial state. Classical planning research has addressed this problem in a domain-specific manner—the same algorithm generates a complete plan for any domain specification. This generality comes at a cost; domain-independent planners have difficulty with larges...

Planners are powerful tools for problem solving because they provide a complete sequence of actions to achieve a goal from a particular initial state. Classical planning research has addressed this problem in a domain-independent manner— the same algorithm generates a complete plan for any domain specification. This generality comes at a cost; domainindependent planners have difficulty with lar...

Probabilistic back-chaining planners, which use probabilities to represent and reason about uncertainty in the planning domain, typically have a larger search space than their classical counterparts. Therefore heuristics that can reduce their search effectively are even more important. The “footprint” principle leads to a family of heuristics for probabilistic planners produced by attempting to...

Though domain-dependent planners can be more efficient than general-purpose planners, no flexible and straight-forward framework is available to generate them. The trend in implementing planners has been to write general-purpose planners through traditional human-intensive programming effort. This thesis presents a general method for automatically generating efficient domain-specific classical ...

While even STRIPS planners must search for plans of unbounded length, temporal planners must also cope with the fact that actions may start at any point in time. Most temporal planners cope with this challenge by restricting action start times to a small set of decision epochs, because this enables search to be carried out in state-space and leverages powerful state-based reachability heuristic...