MODeM 2023

Previous editions of this workshop may be found at the following urls:

• MODeM 2021

The workshop targets high-quality original papers covering all aspects of multi-objective decision making, including, but not limited to, the list of topics below. The following is a non-exhaustive list of topics that we would like to cover in the workshop:

• Multi-objective/multi-criteria/multi-attribute decision making
• Multi-objective reinforcement learning
• Multi-objective planning and scheduling
• Multi-objective multi-agent decision making
• Multi-objective game theory
• Preference elicitation for MODeM
• Social choice and MODeM
• Multi-objective decision support systems
• Multi-objective metaheuristic optimisation (e.g. evolutionary algorithms) for autonomous agents and multi-agent systems
• Multi-objectivisation
• Ethical AI through multi-objective modelling
• Explainable aI through multi-objective modelling
• Interactive systems for MODeM
• Applications of MODeM
• New benchmark problems for MODeM

Extended and revised versions of papers presented at the workshop will be eligible for inclusion in a journal topical collection.

Important Dates

• Submission Deadline: 30 June 2023   extended to 21 July 2023 (23:59 AoE)
• Notification of acceptance: 15 August 2023
• Camera-ready copies: 31 August 2023
• Workshop: 1 October 2023

Submission Details

Papers should be formatted according to the ECAI 2023 guidelines, and should be a maximum of 7 pages in length (with additional pages containing references only). Additionally, we welcome submission of preliminary results, i.e. work-in-progress, as well as visionary outlook papers that lay out directions for future research in a specific area, both up to 5 pages in length, although shorter papers are very much welcome, and will not be judged differently. Finally, we also accept recently published journal papers in the form of a 2 page abstract.

All submissions will be peer-reviewed (single-blind). Accepted work will be allocated time for poster and/or oral presentation during the workshop. Extended versions of original papers presented at the workshop will also be eligible for inclusion in a post-proceedings journal topical collection.

Papers can be submitted through Microsoft CMT.

Journal Topical Collection

After the workshop, all original contributions presented at MODeM 2023 will be invited to submit substantially improved and extended versions of their work, for consideration to be published in a post-proceedings topical collection of the Springer journal Neural Computing & Applications (NC&A, impact factor 5.102): Topical Collection on Multi-Objective Decision Making 2023.

Submission of preliminary work to the MODeM 2023 workshop does not confer an automatic entitlement to publish in the NC&A Topical Collection on Multi-Objective Decision Making 2023; all topical collection submissions must pass through the same rigorous NC&A review process and meet the standard NC&A publication criteria.

The Topical Collection on Multi-Objective Decision Making 2023 has an open call for papers; it is not necessary to submit preliminary work to the MODeM 2023 workshop in order to have your manuscript considered for publication in this topical collection. The topical collection is already open for submissions, and will remain open until 31 January 2024. Papers submitted to the topical collection will be sent out for peer review as soon as they are received, and first decisions can be expected within 3 months approx. from the date of submission.

Program

Sunday, 1 October

Accepted Papers

Invited Talks

Nawal Benabbou

Affiliation: Sorbonne Université (Paris, France)

Website: https://benabbou.lip6.fr

Talk Title: Combining Preference Elicitation and Search for Solving Multi-objective Optimization Problems

Abstract: The increasing complexity of applications encountered in multi-objective optimization leads us today to apply decision models to combinatorial sets of solutions which are implicitly defined. This significantly complicates the decision process and, in particular, the construction of a preference model fitting the objectives of the decision maker as well as the calculation of the optimal decision. Instead of approaching independently and separately these two subjects, we introduce incremental decision procedures aiming to integrate and combine the elicitation of preferences and the calculation of the preferred solution in order to determine the optimal choice without fully specifying the decision model. In these new interactive resolution schemes, asking preference questions to the decision maker during the exploration of the set of solutions allows to focus the elicitation of preferences on information that is really useful for separating competing solutions and thus reducing the number of questions required. This is the main benefit of the incremental approach to multi-objective decision making.

Bio: Dr. Nawal Benabbou is an assistant professor at Sorbonne Université (Paris), in the DECISION team of the LIP6 laboratory of computer science. Her research interests lie in the area of Algorithmic Decision Theory and its applications in Artificial Intelligence and Operations Research. She is the recipient of the “Artificial Intelligence Dissertation Award”, awarded by the French AI association (AFIA) in 2018, and is one of the vice-presidents of the French OR and Decision Making association (ROADEF). She is also actively reviewing for several top-ranked international AI conferences (e.g., AAAI, IJCAI, AAMAS, ECAI) and international journals (e.g., EJOR, AGNT, ANOR, 4OR).

Nic Wilson

Affiliation: University College Cork (Cork, Ireland)

Website: http://publish.ucc.ie/researchprofiles/D005/nwilson

Talk Title: Optimising with Respect to Partially Known Multi-Objective User Preferences

Abstract: Commonly, a decision support system will have only partial information about the preferences of a user. In multi-objective optimisation problems, user preferences are often modelled with some form of weighted sum of objective values, where the vector of weights is initially unknown. In this talk I will consider reasoning and optimisation with partial knowledge of preferences for such multi-objective optimisation problems. If an alternative is necessarily optimal, i.e., optimal for each consistent candidate preference function, then the system can safely recommend that alternative (even though the knowledge of the user preferences may still only be partial). Otherwise, there are different potentially optimal alternatives. One can increase the knowledge of the user preferences by querying the user, asking the user to compare two or more alternatives. I will discuss different querying strategies, which aim towards finding a necessarily optimal alternative.

Bio: Nic Wilson is a senior research fellow at the Insight Centre for Data Analytics, based in the School of Computer Science and Information Technology, University College Cork. His main research areas are in reasoning and optimisation with preferences and uncertainty, especially on combinatorial problems, and has published around a hundred peer-reviewed papers. Application areas of his work include scheduling, supply chain optimisation, transportation logistics, telecommunications, recommender systems and intelligent buildings.

Program Committee

• Athirai Irissappane, IBM, US
• Ben Abramowitz, Tulane University, US
• Brandon T. Fain, Duke University, US
• Debora Di Caprio, University of Trento, IT
• Dimitris Michailidis, University of Amsterdam, NL
• Florian Felten, SnT, University of Luxembourg, LU
• Francisco Javier Santos Arteaga, Universidad Complutense de Madrid, ES
• Fredrik Heintz, Linköping University, SE
• Gabriel Ramos, Unisinos, BR
• Johan Källström, Linköping University, SE
• Junlin Lu, University of Galway, IE
• Lucas Nunes Alegre, Federal University of Rio Grande do Sul (UFRGS), BR
• Manel Rodriguez-Soto, IIIA-CSIC, ES
• Marc Vincent, Thales Land and Air Systems / Sorbonne Université, FR
• Mathieu Reymond, Vrije Universiteit Brussel, BE
• Pieter Libin, Vrije Universiteit Brussel, BE
• Takuya Kanazawa, Hitachi, Ltd., US
• Thommen Karimpanal George, Deakin University, AU
• Vincent Corruble, Sorbonne University, FR
• Willem Röpke, Vrije Universiteit Brussel, BE
• Yijie Zhang, University of Copenhagen, DK
• Zhaori Guo, University of Southampton, UK

Organization

• Roxana Rădulescu (Vrije Universiteit Brussel, BE)
• Patrick Mannion (University of Galway, IE)
• Peter Vamplew (Federation University Australia, AU)
• Diederik M. Roijers (Vrije Universiteit Brussel, BE; City of Amsterdam, NL)
• Conor F. Hayes (Lawrence Livermore National Lab, US)

• Ali E. Abbas (University of Southern California, USA)
• Carlos A. Coello Coello (CINVESTAV-IPN, MX)
• Richard Dazeley (Deakin University, AU)
• Enda Howley (University of Galway, IE)
• Ann Nowé (Vrije Universiteit Brussel, BE)
• Patrice Perny (UPMC, FR)
• Marcello Restelli (Politecnico di Milano, IT)
• Nic Wilson (University College Cork, IE)

Contact

If you have any questions about the MODeM workshop, please contact the organizers at:
modem.workshop.2023 AT gmail.com