Knowledge Engineering for Planning and Scheduling (KEPS)

Despite the progress in automated planning and scheduling systems, these systems still need to be fed by carefully engineered domain and problem descriptions, and 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 continues the tradition of several International Competitions on Knowledge Engineering for Planning and Scheduling (ICKEPS) and previous 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. We seek original 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

We are pleased to accept papers based on recent publications from other (non ICAPS) venues such as specialized conferences (AAMAS, ICRA, KR, …), or general AI conferences (AAAI, IJCAI, ECAI, …). This must be however clearly indicated in the submitted paper.

Important Dates

  • Paper submission deadline: July 20, 2020 July 31, 2020 (an extended deadline – firm !) (UTC-12 timezone)
  • Notification: August 30, 2020, September 11, 2020
  • Camera-ready paper submission: September 30, 2020

Submission Instructions

Two types of papers can be submitted. Full technical papers with a length up to 8 pages are standard research papers. Short papers with a length between 2 and 4 pages can describe either a particular application, or focus on open challenges.

Submissions of papers being reviewed at other venues are welcome since this is a non archival venue and we will not require a transfer of copyright. If such papers are currently under blind review, please anonymize the submission.

All papers should conform to the AAAI style template. The submission is done via EasyChair.

KEPS talks

Workshop Schedule (Paris timezone)

Welcome 2.00pm – 2.10pm


  • Shivam Miglani and Neil Yorke-Smith: NLtoPDDL: One-Shot Learning of PDDL Models from Natural Language Process Manuals paper
  • Ignacio Vellido, Juan Fernández-Olivares and Raul Perez: A Knowledge Based Process for the Generation of HTN Domains from VGDL Video Game Descriptions paper
  • Leonardo Lamanna, Alfonso Emilio Gerevini, Alessandro Saetti, Luciano Serafini and Paolo Traverso: On-line Learning of Planning Domains from Sensor Data in PAL: Scaling up to Large State Spaces paper
  • Joerg Hoffmann, Holger Hermanns, Michaela Klauck, Marcel Steinmetz, Erez Karpas and Daniele Magazzeni: Let’s Learn their Language? A Case for Planning with Automata-Network Languages from Model Checking paper
  • Mauro Vallati and Lukas Chrpa: On the Robustness of Domain-Independent Planning Engines: The Impact of Poorly-Engineered Knowledge paper
  • Rubiya Reba, Rabia Jilani, Alan Lindsay and Lee McCluskey: Acquiring Process Knowledge in Hybrid Planning Domains using Machine Learning paper

Break 3.40pm – 4.30pm


  • Yotam Amitai, Erez Karpas, Ayal Taitler and Per-Olof Gutman: Automatic Generation of Flexible Plans via Diverse Temporal Planning paper
  • Enrico Scala and Mauro Vallati: PDDL+ Grounding: Can We Take Advantage of Classical Planning Approaches? paper
  • Qianyu Zhang and Christian Muise: Action Usability via Deadend Detection paper
  • Lukas Chrpa, Pavel Rytir and Rostislav Horcik: Acting in Dynamic Environments: Models of Agent-Environment Interaction paper
  • Stanislav Sitanskiy, Laura Sebastia and Eva Onaindia: Agent behaviour recognition using text analysis paper
  • Zilu Tang and Masataro Asai: Discrete Word Embedding for Logical Natural Language Understanding paper

Break 6.00pm – 7.00pm


  • Pierre Le Bras, Yaniel Carreno, Alan Lindsay, Ron Petrick and Mike Chantler: PlanCurves: An Interface for End-Users to Visualise Multi-Agent Temporal Plans paper
  • Alan Lindsay, Bart Craenen, Sara Dalzel-Job, Robin Hill and Ron Petrick: Supporting an Online Investigation of User Interaction with an XAIP Agent paper
  • Emanuele De Pellegrin: PDSim: Planning Domain Simulation with the Unity Game Engine paper
  • Alan Lindsay: Using Generic Subproblems for Understanding and Answering Queries in XAIP paper
  • Alba Gragera, Ángel García-Olaya and Fernando Fernández: Supporting the Formalization of Use Cases in Social Robotics paper

Break 8.30pm – 9.30pm


  • Maxence Grand, Humbert Fiorino and Damien Pellier: AMLSI: A Novel and Accurate Action Model Learning Algorithm paper
  • Masataro Asai: Unsuccessful Neural-Symbolic Descriptive Action Modeling from Images: The Search for STRIPS paper
  • Peter Gregory: PDDL Templating and Custom Reporting: Generating Problems and Processing Plans paper
  • Helen Harman and Pieter Simoens: Learning Symbolic Action Definitions from Unlabelled Image Pairs paper
  • Maurício Steinert and Felipe Meneguzzi: Planning Domain Generation from Natural Language Step-by-Step paper


  • Lukas Chrpa (Czech Technical University in Prague)
  • Ron Petrick (Heriot-Watt University)
  • Mauro Vallati (University of Huddersfield)
  • Tiago Vaquero (JPL)

Program Committee

  • Roman Barták (Charles University)
  • Amedeo Cesta (CNR – National Research Council of Italy)
  • Susana Fernandez (Universidad Carlos III de Madrid)
  • Simone Frantini (European Space Agency – ESA/ESOC)
  • Alan Lindsay (Heriot-Watt University)
  • Lee McCluskey (University of Huddersfield)
  • Eva Onaindia (Universitat Politècnica de València)
  • Andrea Orlandini (CNR – National Research Council of Italy)
  • Simon Parkinson (University of Huddersfield)
  • Patricia Riddle (University of Auckland)
  • Francesco Percassi (University of Huddersfield)
  • Alvin Ng (Heriot-Watt University)
  • Chris Geib (SIFT)
  • Yaniel Carreno (Heriot-Watt University)
  • Mary Ellen Foster (University of Glasgow)