Learning from experimentation allows a system to acquire planning domain knowledge by correcting its knowledge when an action execution fails. Experiments are designed and planned...
The FF relaxed plan heuristic is one of the most effective techniques in domain-independent satisficing planning and is used by many state-of-the-art heuristic-search planners. Ho...
We describe the interface between a real-time resource allocation system with an AI planner in order to create fault-tolerant plans that are guaranteed to execute in hard real-tim...
Ella M. Atkins, Tarek F. Abdelzaher, Kang G. Shin,...
This paper discusses the specifics of planning in multiagent environments. It presents the formal framework MAPL (“maple”) for describing multiagent planning domains. MAPL al...
The structured programming literature provides methods and a wealth of heuristic knowledge for guiding the construction of provably correct imperative programs. We investigate the...