Workshop on Reliable Data-Driven Planning and Scheduling

ICAPS'23 Workshop on Reliable Data-Driven Planning and Scheduling (RDDPS)
Prague, Czech Republic
July 10, 2023

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 24, 2023 March 31, 2023 (AoE)
• Author Notification: April 28, 2023
• Camera-Ready Deadline: June 10, 2023 (AoE)
• ICAPS 2023 Workshops: July 10, 2023

Submission Details

All papers must be formatted according to the AAAI formatting guidelines. 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 a panel 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'23, 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).

Workshop Committee

Organizing and Program Committee:

• Sara Bernardini, Royal Holloway University of London, UK
• Jesse Davis, KU Leuven, Belgium
• Alan Fern, Oregon State University, USA
• Daniel Höller, Saarland University, Germany
• Jörg Hoffmann, Saarland University, Germany
• Michael Katz, IBM Research, USA
• Michele Lombardi, DISI, University of Bologna, Italy
• Scott Sanner, University of Toronto, Canada
• Marcel Steinmetz, Saarland University, Germany
• Sylvie Thiebaux, University of Toulouse, France, and Australian National University, Australia
• Eyal Weiss, Bar-Ilan University, Israel

List of Accepted Papers

• Argaman Mordoch, Roni Stern, Enrico Scala and Brendan Juba: Safe Learning of PDDL Domains with Conditional Effects pdf
• Xandra Schuler, Jan Eisenhut, Daniel Höller, Daniel Fišer and Joerg Hoffmann: Action Policy Testing with Heuristic-Based Bias Functions pdf
• Yuta Takata and Alex Fukunaga: Plausibility-Based Heuristics for Latent Space Classical Planning pdf
• Marcel Vinzent, Min Wu, Haoze Wu and Joerg Hoffmann: Policy-Specific Abstraction Predicate Selection in Neural Policy Safety Verification pdf
• Eyal Weiss, Ariel Felner and Gal Kaminka: A Generalization of the Shortest Path Problem to Graphs with Multiple Edge-Cost Estimates pdf

Workshop Schedule

The workshop will take place on July 10.