KEPS

Despite the progress in automated planning and scheduling systems, these systems still need to be fed by carefully engineered domain and problem description and they need to be fine-tuned for particular domains and problems. Knowledge engineering for AI planning and scheduling deals with the acquisition, design, validation and maintenance of domain models, and the selection and optimization of appropriate machinery to work on them. These processes impact directly on the success of real-world planning and scheduling applications. The importance of knowledge engineering techniques is clearly demonstrated by a performance gap between domain-independent planners and planners exploiting domain dependent knowledge.

The workshop shall continue the tradition of several International Competitions on Knowledge Engineering for Planning and Scheduling (ICKEPS) and KEPS workshops. Rather than focusing only on software tools and domain encoding techniques –which are topics of ICKEPS– the workshop will cover all aspects of knowledge engineering for AI planning and scheduling.

Topics

We seek papers ranging from experience reports to the description of new technology in the following areas:

• formulation of domains and problem descriptions
• methods and tools for the acquisition of domain knowledge
• pre- and post-processing techniques for planners and schedulers
• acquisition and refinement of control knowledge
• formal languages for domain description
• re-use of domain knowledge
• translators from other application-area-specific languages to solver-ready domain models (such as PDDL)
• formats for specification of heuristics, parameters and control knowledge for solvers
• import of domain knowledge from general ontologies
• ontologies for describing the capabilities of planners and schedulers
• automated reformulation of problems
• automated knowledge extraction processes
• domain model, problem and plan validation
• visualization methods for domain models, search spaces and plans
• mapping domain properties and planning techniques
• plan representation and reuse
• knowledge engineering aspects of plan analysis

KEPS 2022 Schedule

15 minutes allocated to each paper, including discussion. The recorded videos are available on a dedicated YouTube playlist.

12.00 - 12.15 Welcome

12.15 - 13.45 Session 1

• Francesco Percassi and Sangeet Saha,
  QoS-Aware Approximate Real-time Tasks Execution on Multiprocessor Systems using PDDL+ Planning
• Vishal Pallagani, Priyadharsini Ramamurthy, Vedant Khandelwal, Revathy Venkataramanan, Kausik Lakkaraju, Sathyanarayanan N. Aakur and Biplav Srivastava,
  A Rich Recipe Representation as Plan to Support Expressive Multi-Modal Queries on Recipe Content and Preparation Process
• Irene Garcia-Camacho, Júlia Borràs and Guillem Alenyà,
  Knowledge Representation to Enable High-level Planning in Cloth Manipulation Tasks
• Thomas Eiter, Tobias Geibinger, Andrej Gisbrecht, Nelson Higuera Ruiz, Nysret Musliu, Johannes Oetsch and Daria Stepanova,
  An Open Challenge for Exact Job Scheduling with Reticle Batching in Photolithography
• José González Barroso, Raquel Fuentetaja and Susana Fernández,
  Planning-Based Approach for Silent Proactive Assistance

14.30 - 15.45 Session 2

• Alan Lindsay and Ron Petrick,
  Incremental Domain Model Acquisition with a Human in the Loop
• Maxence Grand, Damien Pellier and Humbert Fiorino,
  An Accurate HDDL Domain Learning Algorithm from Partial and Noisy Observations
• Ethan Callanan, Rebecca De Venezia, Victoria Armstrong, Alison Paredes, Tathagata Chakraborti and Christian Muise,
  MACQ: A Holistic View of Model Acquisition Techniques
• Emanuele De Pellegrin and Ronald Petrick,
  What Plan? Virtual Plan Visualization with PDSim
• Tiago Stegun Vaquero, Federico Rossi, Rebecca Castano, Ashkan Jasour, Ellen van Wyk, Nihal Dhamani, Anthon Barrett, Bennett Huffman and Marijke Jorritsma,
  A Knowledge Engineering Framework for Mission Operations of Increasingly Autonomous Spacecraft

16.45 - 17.45 Session 3

• Dancheng Gao, Andrew Coles and Amanda Coles,
  Learning Macro-Actions to Improve the Relaxed Planning Graph Heuristic
• Francesco Percassi, Enrico Scala and Mauro Vallati,
  On Translation-Based Approaches from Discrete PDDL+ to Numeric Planning
• Eran Hershkovich, Dor Atzmon, Guy Shani and Roni Stern,
  Pursuit Planning with Spatial Action Abstraction
• Mauricio Salerno and Raquel Fuentetaja,
  Elimination of Unnecessary Actions from Plans Using Automated Planning

18.30 - 19.45 Session 4 and Closing

• Chris Johnson, Pascal Bercher and Charles Gretton,
  A Study of the Power of Heuristic-based Pruning via SAT Planning
• Marshall Clifton and Charles Gretton,
  Fast Parallel PDR Algorithms for Planning
• Uwe Köckemann,
  The AI Domain Definition Language (AIDDL) - Three Use Cases for Integrated Planning, Reasoning, and Learning
• Viviane Bonadia dos Santos, Leliane N. Barros, Silvio Do Lago Pereira and Maria Viviane de Menezes,
  Symbolic FOND Planning for Temporally Extended Goals
• Alex Coulter, Teo Ilie, Renee Tibando and Christian Muise,
  Theory Alignment via a Classical Encoding of Regular Bisimulation

Organising Committee

• Lukas Chrpa, Czech Technical University, Czech Republic
• Ron Petrick Heriot-Watt University, UK
• Mauro Vallati, University of Huddersfield, UK
• Tiago Vaquero, Jet Propulsion Laboratory, Caltech, USA

Program Committee

• Roman Bartak, Charles University
• Yaniel Carreno, Edinburgh Centre for Robotics
• Susana Fernandez, Universidad Carlos III de Madrid
• Simone Fratini, European Space Agency - ESA/ESOC
• Alan Lindsay, Heriot-Watt University
• Lee Mccluskey, University of Huddersfield
• Eva Onaindia, Universitat Politècnica de València
• Andrea Orlandini, National Research Council of Italy (ISTC-CNR)
• Simon Parkinson, University of Huddersfield
• Francesco Percassi, University of Huddersfield
• Patricia Riddle, The University of Auckland
• Enrico Scala, Università' di Brescia
• Alessandro Umbrico, National Research Council of Italy (CNR-ISTC)