Workshop on Knowledge Engineering for Planning and Scheduling (KEPS)

Special focus on Learning and Data Driven Model Acquisition

Collocated with ICAPS 2019 in Berkeley, USA.

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.

We seek original papers ranging from experience reports to the description of new technology in the following areas: In particular, this year we encourage papers with a specific focus on the task of model acquisition, more concretely characterized by the three following categories:
  1. Solicitation: The manual process of specifying a model.
  2. Induction: The fully automatic synthesis of a model from data.
  3. Hybrid: A mix of partial-model induction and solicitation
In addition to the areas listed above, we particularly welcome works on: Check out the papers at: . The following is the list of accepted papers:

Time Program
09:00-09:15 Workshop Open
09:15-10:15 Invited Talk: Scott Sanner
10:15-11:00 Coffee Break
11:00-12:15 Session 1
Goal-constrained planning domain model formal verification of safety properties
Object-Centric Knowledge Representations in Hierarchical Planning for Decentralized Environments
Domain Modeling for Multi-Payload Planning of Experimental Satellite
Planning for Implicit Coordination using FOND
The International Competition on Knowledge Engineering for Planning and Scheduling: Food for Thoughts (and Call to Action)
12:30-14:15 Lunch Break
14:15-15:30 Session 2
Reprocess: Process Refinement for Improving Accuracy in Hybrid Planning Domain Models
Commonsense Reasoning and Knowledge Acquisition to Guide Deep Learning on Robots
One-shot learning: From domain knowledge to action models
Incremental Learning of Action Models for Planning
Incremental Learning of Discrete Planning Domains from Continuous Perceptions
15:30-16:00 Coffee Break
16:00-17:30 Tutorial: From Teaching the PDDL Novice to Empowering the Planning Solution Integrator