April 1, 2022April 8, 2022 - Submissions Due (DEADLINE EXTENDED!)
- May 6, 2022 - Notification
- June 3, 2022 - Camera-ready Due
- June 17, 2022 - Workshop Date
Automated planners are increasingly being integrated into online acting 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 (e.g., a planetary rover or anunderwater glider).The integration may resemble something more like an"acting and planning stack" where an automated planner produces an activity or task plan that is further refined by an actor before being executed by the execution platform of the actor, such as, a reactive controller (e.g., robotics).Orthe integration may be a domain-specific policy that maps states to actions (e.g., reinforcement learning).Models for planning and execution can be sameor different; the planning model can define context-dependent actions schema for online (re-)planning or can just specify flexibility to be handled separately at execution time. 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 involvesonline deliberation and managing the execution of actions, bringing to the foreground concerns over how much computational effort planning should invest over time.
n 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,or some dynamic events occur,it raises a number of interesting questions about how the system should respond and which is the scope of online deliberation versus execution.Is the "acting" side of the system responsible for a 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?
The sixth edition of the workshop on Integrated Planning, Acting, and Execution (IntEx) aims: (1) to provide a forum for discussing the challenges of integrating online planning, acting, and execution, and (2) to assess the potential for holding an integrated execution competition at ICAPS.
Topics of Interest
- online planning, acting, and execution
- position papers, benchmarks, or challenge problems for integrated execution
- improving planning performance from execution experience
- anytime or incremental planning
- discussions of plan dispatching or plan executives
- execution monitoring; comparing replanning, plan repair, re-goaling, plan merging
- managing open worlds with closed-world planners; model learning from experience
- determining an observation policy; policy switching; incremental policy adjustment
- modelling, languages and knowledge engineering for interleaved planning and execution
- architectures and application for integrated planning and execution, execution
- monitoring, mixed-initiative on-line re-planning and execution
Submissions may be regular papers (up to 8 pages plus references) or short position/challenge papers (up to 4 pages plus references). All papers should conform to the AAAI formatting guidelines and style (https://www.aaai.org/Publications/Templates/AuthorKit21.zip).
The papers must be submitted in a PDF format via EasyChair system (https://easychair.org/conferences/?conf=intex2022). Submissions will be reviewed by at least two referees.
We welcome existing publications from other venues that are appropriate for discussion at this workshop. Please note in the title area if this work is already accepted at another venue. If the work is under review at another venue (e.g., IJCAI-2022) please notify the organizers so we can avoid potential reviewing conflicts.
|Authors||Title||Time Slot (UTC)|
|Roni Stern, Wiktor Piotrowski, Lara Crawford, and Michael Youngblood||A System for Lifelong, Resilient, Job Shop Planning based on Learning Machine Capabilities from Operational Data||1215-1240|
|Rogelio E. Cardona-Rivera, M. Gardone, Logan Peterson, Laura Hiatt, and Mark Roberts||Re-examining the Planning Basis of Goal-driven Autonomy Problems||1240-1305|
|Gonzalo Montesino Valle and Michael Cashmore||Deep Reinforcement Learning for Plan Execution||1305-1330|
|Jérémy Turi and Arthur Bit-Monnot||Guidance of a Refinement-based Acting Engine with a Hierarchical Temporal Planner||1330-1355|
|Stefan-Octavian Bezrucav, Gerard Canal, Michael Cashmore, Burkhard Corves, and Andrew Coles||Towards Automatic State Recovery for Replanning||1355-1420|
|Break and Informal Poster Discussion||1420-1520|
|Invited Talk: Michael Cashmore and Liudvikas Nemiro||CraftBots Tutorial||1520-1720|
|Closing remarks and a brief discussion on IntEx and its future direction.||1720-1730|
Invited Talk: CraftBots Tutorial
CraftBots Repository | CraftBots Wiki
The CraftBots simulation aims to be a new benchmark and competition environment for integrated planning and execution. Using a planning system effectively in the control of an agent acting in real-time poses a variety of challenges in integrating phases of planning with execution of plans. Integrated systems are developed with a focus on particular challenges, and it has been typically difficult to test, benchmark, and compare these systems. To do so requires a benchmark that has transparent and well-defined rules, and can be adapted to exhibit the problem characteristics of interest.
Craftbots is an accessible (requirring only python3 and numpy) and adaptive benchmark simulation for integrated planning and execution. The simulation can be configured to present a wide variety of different scenarios and exhibit different problem features, such as temporal uncertainty, non-deterministic action outcomes, partial observability, and limited communications.
In this tutorial, Dr Michael Cashmore and Liudvikas Nemiro (both of University of Strathclyde) will guide participants through hands-on coding exercises during which participants will learn how to configure the scenario, implement a simple agent, and integrate the agent with the simulation API.
We invite all interested to join us for the exciting hands-on tutorial on CraftBots to learn about the framework and the competition!
- Paper submission deadline:
April 1, 2022April 8,2022(DEADLINE EXTENDED!)
- Notification of acceptance: May 6, 2022
- Camera-ready paper submission: June 3, 2022
- Workshop Date: June 17, 2022
- Wiktor Piotrowski Palo Alto Research Center, wiktorpi at parc.com
- Sunandita Patra University of Maryland, College Park, patras at umd.edu
- Mark "Mak" Roberts Naval Research Laboratory, mark.roberts at nrl.navy.mil
- Tiago Vaquero Jet Propulsion Laboratory, vaquero at jpl.nasa.gov
- Ron Alford, MITRE Corporation, USA
- Stefan Bezrucav, RWTH Aachen University, Germany
- Gerard Canal, King's College London, UK
- Michael Cashmore, University of Strathclyde, UK
- Dustin Dannenhauer, Parallax Advanced Research, USA
- Riccardo De Benedictis, ISTC-CNR, Italy
- Michael Floyd, Knexus Research, USA
- Jeremy Frank, NASA Ames, USA
- Shakil Khan, Ryerson University, Canada
- Sravya Kondrakunta, St. Olaf College, USA
- Oscar Lima, German Research Centre for AI – DFKI, Germany
- Andrea Micheli, Fondazione Bruno Kessler, Italy
- Marco Roveri, University of Trento, Italy
- Vikas Shivashankar, Amazon Kiva, USA
- Roni Stern, PARC/Ben-Gurion University of the Negev, Israel