Generalization in Planning

Overview

This workshop aims to build synergies across different AI communities in order to address all aspects of generalization of solutions for sequential decision making, including, but not limited to, representation of problems and solution concepts that enable efficient generalization and transfer of relevant knowledge, and algorithms for learning or synthesizing such generalized knowledge and solutions. We welcome contributions focusing on different formulations/representations for generalization, empirically validated methods, and theoretical analyses and foundations for generalization.

Humans are good at solving sequential decision making problems, generalizing from a few examples, and learning and expressing generalized knowledge that can help solve new problems. Computing such knowledge remains a long-standing open problem for Artificial Intelligence (AI). Over the last two decades, there has been remarkable progress in the performance of automated planning systems. However, real-world scalability and skill/plan generalization for complex, long-horizon tasks remains an open challenge.

We welcome contributions focusing on different formulations/representations for generalization, empirically validated methods, theoretical analyses, foundations for generalization, learning and transfer in planning.

This workshop is the sixth edition of the recurring GenPlan workshop series.

Workshop related queries can be addressed to the Organizing Committee at: genplan22\at\gmail.com.

Call for Papers

Topics of interest to this workshop bring together research being conducted not only in multiple sub-fields of AI research (including automated planning, knowledge representation, and reinforcement learning) but also in other fields of research, including formal methods and program synthesis.

We welcome submissions that address formal as well as empirical issues on topics such as:

• Formulation of generalized problems: propositional, first-order and other type of representations, and possible decomposition of such representations in terms of hierarchies, behaviors, multiple objectives, etc.
• Representation of solution structures that enable generalization and transfer.
• Learning high-level models for generalizable planning.
• Learning and synthesis approaches for computing solutions.
• Instantiation and execution of general solutions over new problem instances.
• Heuristics for plan and policy generalization.
• Learning policies and heuristics for generalized planning.
• Generation and detection of good examples for few-shot generalizable planning and learning.
• Program synthesis.
• Deriving domain control knowledge and partial policies with planning and learning.
• Generalization in environments with partial observability and/or noise.
• Generalization and transfer in reinforcement learning.

Submission Guidelines

Submissions can describe either work in progress or mature work that has already been published at other research venues and would be of interest to researchers working on generalization in planning. Previously published work in whole or in part may be in the form of a resubmission of a previous paper, or in the form of a position paper that overviews and cites a body of work. Submissions of papers being reviewed at other venues (NeurIPS, UAI, ICML, CoRL, IROS,...) 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.

Submissions may use either the IJCAI '22 paper format (https://www.ijcai.org/authors_kit) or the NeurIPS ’22 paper format. Camera-ready versions of accepted papers will be required in the IJCAI '22 format by the camera-ready deadline.

Two types of papers can be submitted:

• full technical papers with the length of up to 8 pages + 2 for references (9+2 pages in NeurIPS format)
• short papers with the length between 2 and 4 pages + 1 for references

Papers can be submitted via EasyChair at https://easychair.org/conferences/?conf=genplan22.

Important Dates

Invited Speakers

Blai Bonet

Universitat Pompeu Fabra, Spain

Tesca Fitzgerald

Carnegie Mellon University, USA

Eva Onaindia

Universitat Politècnica de València, Spain

Accepted Papers

• Mechanically Proving Guarantees of Generalized Heuristics: First Results and Ongoing Work
  Mohammad Abdulaziz, Florian Pommerening, and Augusto B. Corrêa
• Mimicking Behaviors in Separated Domains
  Giuseppe De Giacomo, Dror Fried, Fabio Patrizi, and Shufang Zhu
• Symbolic Approaches to LTL_f Best-Effort Synthesis
  Giuseppe De Giacomo, Gianmarco Parretti, and Shufang Zhu
• Learning General Policies and Helpful Action Classifiers from Partial State Spaces
  Dominik Drexler, Javier Segovia-Aguas, and Jendrik Seipp
• Learning Sketches for Decomposing Planning Problems into Subproblems of Bounded Width
  Dominik Drexler, Jendrik Seipp, and Hector Geffner
• Automatic Cross-Domain Task Plan Transfer by Caching Abstract Skills
  Khen Elimelech, Lydia E. Kavraki, and Moshe Y. Vardi
• Optimal Planning as Constraint Optimization - Preliminary Report
  Enrico Giunchiglia and Armando Tacchella
• Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems
  Rushang Karia, Rashmeet Kaur Nayyar, and Siddharth Srivastava
• Skill Transfer for Temporally-Extended Task Specifications
  Jason Xinyu Liu, Ankit Shah, Eric Rosen, George Konidaris, and Stefanie Tellex
• What can we Learn Even From the Weakest? Learning Sketches for Programmatic Strategies
  Leandro C. Medeiros, David S. Aleixo, and Levi H. S. Lelis
• First-Order Dead End Situations for Policy Guidance in Relational Problems
  Jun Hao Alvin Ng and Ronald P. A. Petrick
• Representation and Synthesis of C++ Programs for Generalized Planning
  Javier Segovia-Aguas, Yolanda E-Martín, and Sergio Jiménez Celorrio
• Inventing Relational State and Action Abstractions for Effective and Efficient Bilevel Planning
  Tom Silver, Rohan Chitnis, Nishanth Kumar, Willie McClinton, Tomás Lozano-Pérez, Leslie Pack Kaelbling, and Joshua Tenenbaum
• Discovering User-Interpretable Capabilities of Black-Box Planning Agents
  Pulkit Verma, Shashank Rao Marpally, and Siddharth Srivastava

Related Workshops

This workshop builds upon GenPlan workshop series. However, new workshops on reasoning about actions include as one of their topics the generalization in planning. This is the list of most related and recent workshops:

• Workshop on Generalization in Planning at IJCAI 2021
• Workshop on Generalization in Planning at AAAI 2020
• Workshop on Reasoning about Actions and Processes: Highlights of Recent Advances at ICAPS 2019
• Workshop on Reasoning about Actions and Processes: Highlights of Recent Advances at KR 2018
• Workshop on Generalized Planning at ICAPS 2017
• Workshop on Generalized Planning at AAAI 2011
• Workshop on Generalized Planning: Macros, Loops, Domain Control at ICAPS 2009

Committees

Organizing Committee

Pulkit Verma

Arizona State University, USA

Yuqian Jiang

The University of Texas at Austin, USA

Rushang Karia

Arizona State University, USA

Jendrik Seipp

Linköping University, Sweden

Advisory Board

Blai Bonet

Universitat Pompeu Fabra

Giuseppe De Giacomo

Sapienza Università di Roma

Hector Geffner

Universitat Pompeu Fabra and ICREA

Anders Jonsson

Universitat Pompeu Fabra

Sheila McIlraith

The University of Toronto

Siddharth Srivastava

Arizona State University

Peter Stone

The University of Texas at Austin and Sony AI

Sylvie Thiébaux

Australian National University

Shlomo Zilberstein

The University of Massachussets Amherst

Program Committee

• Cameron Allen, Brown University, USA.
• Blai Bonet, Universitat Pompeu Fabra, Spain.
• Augusto B. Corrêa, Universität Basel, Switzerland.
• Giuseppe De Giacomo, Sapienza Università di Roma, Italy.
• Dominik Drexler, Linköping University, Sweden.
• Yunshu Du, Sony AI, USA.
• Salomé Eriksson, Universität Basel, Switzerland.
• Patrick Ferber, Universität Basel, Switzerland.
• Raquel Fuentetaja, Universidad Carlos III de Madrid, Spain.
• Hector Geffner, ICREA and Universitat Pompeu Fabra, Spain.
• Daniel Gnad, Linköping University, Sweden.
• Sergio Jiménez Celorrio, Universitat Politècnica de València, Spain.
• Anders Jonsson, Universitat Pompeu Fabra, Spain.
• Andres Occhipinti Liberman, Universitat Pompeu Fabra, Spain.
• Bo Liu, The University of Texas at Austin, USA.
• Reuth Mirsky, Bar Ilan University, Israel.
• Sriraam Natarajan, The University of Texas at Dallas, USA.
• Rashmeet Kaur Nayyar, Arizona State University, USA.
• Eva Onaindia, Universitat Politècnica de València, Spain.
• Hector Palacios, ServiceNow Research, Canada.
• Ron Petrick, Heriot-Watt University, United Kingdom.
• Javier Segovia-Aguas, Universitat Pompeu Fabra, Spain.
• Tom Silver, Massachusetts Institute of Technology, USA.
• Siddharth Srivastava, Arizona State University, USA.
• Simon Ståhlberg, Linköping University, Sweden.
• Roni Stern, PARC, USA and Ben Gurion University of the Negev, Israel.