Workshop on Reliable Data-Driven Planning and Scheduling

ICAPS'24 Workshop on Reliable Data-Driven Planning and Scheduling (RDDPS)
Banff, Alberta, Canada
June 3, 2024

Aim and Scope of the Workshop

Data-driven AI is the dominating trend in AI at this time. From a planning and scheduling perspective – and for sequential decision making in general – this is manifested in two major kinds of technical artifacts that are rapidly gaining importance. The first are planning models that are (partially) learned from data (e.g., a weather forecast in a model of flight actions). The second are action-decision components learned from data, in particular, action policies or planning-control knowledge for making decisions in dynamic environments (e.g., manufacturing processes under resource-availability and job-length fluctuations). Given the nature of such data-driven artifacts, reliability is a key concern, prominently including safety, robustness, and fairness in various forms, but possibly other concerns as well. Arguably, this is one of the grand challenges in AI for the foreseeable future.

Topics of Interest

Given this, the workshop welcomes contributions to any topic that roughly falls into the following problem space:

(1) Data-driven artifacts: Reliability of learned planning and scheduling models (e.g. action models, transition probabilities, environment prediction, etc.); learned action-decisions (e.g. action policies, components thereof, previous plans, etc.); combinations of both.

(2) Objectives: Reliability in whatever form, including risk, safety, robustness, fairness, error bounds, etc.; alongside possibly other concerns such as scalability and data efficiency, system design/engineering principles and challenges, and the interactions of these with reliability.

(3) Methodologies: Planning and scheduling algorithms in the presence of learned artifacts as per 1.; analyzing such artifacts (reasoning, verification, testing, etc.); making such analyses amenable to human users (visualization, interaction); potentially others as relevant to the objectives as per 2.

Important Dates

  • Submission Deadline: March 29, 2024 (AoE)
  • Author Notification: April 29, 2024
  • Camera-Ready Deadline: May 15, 2024 (AoE)
  • ICAPS 2024 Workshops: June 2-3, 2024

Submission Details

All papers must be formatted like at the main conference (ICAPS author kit). Submitted papers should be anonymous for double-blind reviewing. Paper submission is via EasyChair.

We call for two kinds of submissions: Technical papers, of length up to 8 pages plus references. The workshop is meant to be an open and inclusive forum, and we encourage papers that report on work in progress. Position papers, of length up to 4 pages plus references. Given that reliability of data-driven planning and scheduling is rather new at ICAPS, we encourage authors to submit positions on what they believe are important challenges, questions to be considered, approaches that may be promising. We will include any position relevant to discussing the workshop topic. We expect to group position paper presentations into a dedicated session, followed by an open discussion.

Every submission will be reviewed by members of the program committee according to the usual criteria such as relevance to the workshop, significance of the contribution, and technical quality.

At least one author of each accepted paper must attend the workshop in order to present the paper. Authors must register for the ICAPS conference in order to attend the workshop.

Policy on Previously Published Materials

Please do not submit papers that are already accepted for the ICAPS main conference. All other submissions, e.g. papers under review for IJCAI'24, are welcome. Authors submitting papers rejected from the ICAPS main conference, please ensure you do your utmost to address the comments given by ICAPS reviewers. Also, it is your responsibility to ensure that other venues your work is submitted to allow for papers to be already published in “informal” ways (e.g. on proceedings or websites without associated ISSN/ISBN).

Organizing Committee

  • Daniel Höller, Saarland University, Germany
  • Timo P. Gros, German Research Center for Artificial Intelligence, Germany
  • Marcel Steinmetz, University of Toulouse, France
  • Eyal Weiss, Bar-Ilan University, Israel
  • Jörg Hoffmann, Saarland University, Germany
  • Sylvie Thiebaux, University of Toulouse, France, and Australian National University, Australia

Program Committee

  • Pascal Bercher, The Australian National University, Australia
  • Jesse Davis, KU Leuven, Belgium
  • Timo P. Gros, German Research Center for Artificial Intelligence, Germany
  • Daniel Höller, Saarland University, Germany
  • Jörg Hoffmann, Saarland University, Germany
  • Michael Katz, IBM Research, USA
  • Scott Sanner, University of Toronto, Canada
  • Marcel Steinmetz, University of Toulouse, France
  • Sylvie Thiebaux, University of Toulouse, France, and Australian National University, Australia
  • Eyal Weiss, Bar-Ilan University, Israel

List of Accepted Papers

  • Bruce Chidley and Christian Muise: Using Probabilistic Planning to Model the Spread of COVID-19
  • Alba Gragera and Christian Muise: One Repair to Rule Them All: Repairing a Broken Planning Domain Using Multiple Instances
  • Timo P. Gros, Nicola Müller, Daniel Höller and Verena Wolf: Safe Reinforcement Learning Through Regret and State Restorations in Evaluation Stages
  • Mingyu Hao, Felipe Trevizan, Sylvie Thiebaux, Patrick Ferber and Jörg Hoffmann: Learned Pairwise Rankings for Greedy Best-First Search
  • Chaahat Jain, Lorenzo Cascioli, Laurens Devos, Marcel Vinzent, Marcel Steinmetz, Jesse Davis and Jörg Hoffmann: Safety Verification of Tree-Ensemble Policies via Predicate Abstraction
  • Alison Paredes, J. Benton, Jeremy Frank and Christian Muise: Bias in Planning Algorithms
  • Alison Paredes, J. Benton, Jeremy Frank and Christian Muise: Planning Bias: Planning as a Source of Sampling Bias

Workshop Schedule (June 3)

Welcome      
8:308:40Brief Welcome
        
SESSION 1: Model Creation
8:409:00One Repair to Rule Them All: Repairing a Broken Planning Domain Using Multiple Instances
Alba Gragera and Christian Muise
9:009:20Using Probabilistic Planning to Model the Spread of COVID-19
Bruce Chidley and Christian Muise
        
SESSION 2: Safety of Learned Policies
9:209:40Safe Reinforcement Learning Through Regret and State Restorations in Evaluation Stages
Timo P. Gros, Nicola Müller, Daniel Höller and Verena Wolf
9:4010:00Safety Verification of Tree-Ensemble Policies via Predicate Abstraction
Chaahat Jain, Lorenzo Cascioli, Laurens Devos, Marcel Vinzent, Marcel Steinmetz, Jesse Davis and Jörg Hoffmann
        
BREAK
10:0010:30 
        
SESSION 3: Learning and Planning
10:3010:50Learned Pairwise Rankings for Greedy Best-First Search
Mingyu Hao, Felipe Trevizan, Sylvie Thiebaux, Patrick Ferber and Jörg Hoffmann
10:5011:10Planning Bias: Planning as a Source of Sampling Bias
Alison Paredes, J. Benton, Jeremy Frank and Christian Muise
11:1011:30Bias in Planning Algorithms
Alison Paredes, J. Benton, Jeremy Frank and Christian Muise
        
SESSION 4: Open Discussion
11:3012:00Open discussion

Affiliated Projects

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