Scaling up metric temporal planning

This dissertation aims at building on the success of domain-independent heuristics for classical planning to develop a scalable metric temporal planner. The main contribution is Sapa, a domain-independent heuristic forward-chaining planner which can handle various metric and temporal constraints as well as multi-objective functions. Our major technical contributions include: (1) The temporal planning-graph based methods for deriving heuristics that are sensitive to both cost and makespan. I provide different approaches for propagating and tracking the goal-achievement cost according to time and different techniques for deriving the heuristic values guiding Sapa using these cost-functions. To improve the heuristic quality, I also present techniques for adjusting the heuristic estimates to take into account action interactions and metric resource limitations. (2) The “partialization” techniques to convert the position-constrained plans produced by Sapa to generate the more flexible order-constrained plans. Toward this objective, a general Constraint Satisfaction Optimization Problem (CSOP) is developed and can be optimized under an objective function dealing with a variety of temporal flexibility criteria, such as makespan. (3) Sapaps, an extension of Sapa, that is capable of finding solutions for the Partial Satisfaction Planning Net Benefit (PSP Net Benefit) problems. Sapa ps uses an anytime best-first search algorithm to find plans with increasing better solution quality as it is given more time to run. For each of these contributions, I present the technical details and demonstrate the effectiveness of the implementations by comparing its performance against other state-of-the-art planners using the benchmark planning domains.

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