Planning for real robots to act in dynamic and uncertain environments is a challenging problem. A complete model of the world is not viable and an integration of deliberation and ...
Manuela M. Veloso, Elly Winner, Scott Lenser, Jame...
We develop an algorithm for merging plans that are represented in a richly expressive language. Speci cally, weare concerned with plans that have i quantitative temporal constrain...
Ioannis Tsamardinos, Martha E. Pollack, John F. Ho...
In this paper, we outline the requirements of a planning and decision aid to support US Army small unit operations in urban terrain and show how AI planning technologies can be ex...
Austin Tate, John Levine, Peter Jarvis, Jeff Dalto...
The Remote Agent Experiment (RAX) on the Deep Space 1 (DS1) mission was the first time that an artificially intelligent agent controlled a NASA spacecraft. One of the key componen...
Benjamin D. Smith, Martin S. Feather, Nicola Musce...
In the last years, some very promising domain independent heuristic state-space planners for STRIPS worlds, like ASP/HSP, HSPr and GRT, have been presented. These planners achieve...
We describe a methodology for representing and optimizing user preferences on plans. Our approach differs from previous work on plan optimization in that we employ a generalizatio...
Gregg Rabideau, Barbara Engelhardt, Steve A. Chien
Goal-directed Markov Decision Process models (GDMDPs) are good models for many decision-theoretic planning tasks. They have been used in conjunction with two different reward stru...