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Benton, NASA Ames, USA  \nMark Boddy, Adventium Labs, USA  \nMichael Cashmore, King's College London, UK Jeremy Frank, NASA Ames, USA  \nAndy Hertle, University of Freiburg, Germany Nir Lipovetzky, University of Melbourne, Australia Julie Porteous, Teesside University, UK  \nWheeler Ruml, University of New Hampshire, USA Scott Sanner, University of Toronto, Canada Vikas Shivashankar, Knexus Research, USA  \nForeword  \nAutomated planners are increasingly being integrated into online execution systems. The integration may, for example, embed a domain-independent temporal planner in a manufacturing system (e.g., the Xerox printer application) or autonomous vehicles. The integration may resemble something more like a ”planning stack” where an automated planner produces an activity or task plan that is further refined before being executed by a reactive controller (e.g., robotics) . Or, the integration may be a domain-specific policy that maps states to actions (e.g., reinforcement learning) . Online learning may or may not be involved, and may include adjusting or augmenting the model, determining when to repair versus replan, learning to switch policies, etc. A specific focus of these integrations involves online deliberation, bringing to the foreground concerns over how much computational effort planning should invest over time.  \nIn any of these systems, a planner generates action sequences that are eventually dispatched to an executive, yet taking action in a dynamic world rarely proceeds according to plan. When planning assumptions are challenged during execution, it raises a number of interesting questions about how the system should respond. Is the ”acting” side of the system responsible fora response or the ”planning” side? Or do the two need to cooperate and how much? When should the activity planner abandon or preempt the current goals? Should the task planner repair a plan or replan from scratch? Should the executive adjust its current policy, switch to a new one, or learn a new policy from more relevant experience?  \nThis set of working notes consolidates the papers to be presented at IntEx 2017 .  \nThese notes will be updated after the workshop is held with details of the workshop discussions.  \nMark Roberts, Sara Bernardini, Tim Niemueller, and Tiago Vaquero June 2017  \nContents  \nk-Robust Multi-Agent Path Finding  \nDorAtzmon, Roni Stern, Ariel Felner, Roman Bartak, Neng-Fa Zhou and Glenn Wagner 1  \nIntegrating Execution and Rescheduling  \nJeremy Frank 10  \nAutonomous Search-Detect-Track for Small UAVs  \nBob Morris, Anjan Chakrabarty, Joshua Baculi, Xavier Bouyssounouse and Rusty Hunt 19  \nAn Architecture for Integrated Timeline Planning and Model-based Execution  \nTiago Nogueira and Simone Fratini 27  \nGoal Reasoning as Multilevel Planning  \nAlison Paredes and Wheeler Ruml 36  \nAutomated Planning with Goal Reasoning in Minecraft  \nMark Roberts, Wiktor Piotrowski, Pyrce Bevan, David Aha, Maria Fox, Derek Long and Daniele Magazzeni 43  \nTowards Planning With Hierarchies of Learned Markov Decision Processes  \nJohn Winder, Shawn Squire, Matthew Landen, Stephanie Milani and Marie desJardins 50  \nk-Robust Multi-Agent Path Finding  \nDor Atzmon, Roni Stern, Ariel Felner and Roman Bartak and Neng-Fa Zhou  \nBen Gurion University of the Negev Charles University in Prague City University of New York  \nBe'er Sheva, Isreal Prague, Czech Republic New York City, NY, USA  \nAbstract  \nIn the multi-agent path-􀀌nding (MAPF) problem","cbCaihyjh2pq2iEZ","https://ap.wps.com/l/cbCaihyjh2pq2iEZ","pdf",7502814,57,"English","# Foreword\n# Contents\n## k-Robust Multi-Agent Path Finding\n## Integrating Execution and Rescheduling\n## Autonomous Search-Detect-Track for Small UAVs\n## An Architecture for Integrated Timeline Planning and Model-based Execution\n## Goal Reasoning as Multilevel Planning\n## Automated Planning with Goal Reasoning in Minecraft\n## Towards Planning With Hierarchies of Learned Markov Decision Processes\n# Abstract\n# Introduction and Overview","[{\"question\":\"What is the k-robust MAPF problem introduced in the included paper?\",\"answer\":\"It requires a multi-agent path-finding plan that remains robust to up to k unexpected delays per agent. The approach converts a conflict-based search solver to support k-robustness and analyzes policies for cases with more than k delays.\"}]","27th International Conference on Automated Planning and Scheduling - IntEx 2017 - Working Notes of the Workshop on Integrated Execution of Planning and Acting | PDF",1791087577,144]