Skip to main content
SpringerLink
Log in
Springer
Neural Computing and Applications
Publishing model:
Hybrid

Neural Computing and Applications - Topical Collection on Explainable Sequential Decision-Making

Aims and Scope

As we work with AI and rely on AI for more and more decisions that influence our lives, the research area of explainable AI (XAI) has rapidly developed, with goals such as increasing trust, enhancing collaboration, and enabling transparency in AI. The focus of this topical collection is on explainable sequential decision-making – XAI for systems that are required to make a sequence of decisions to achieve their goals or objectives. This stands in contrast to the substantial existing work on interpretable machine learning, which generally focuses on the single input-output mappings of "black box" models such as neural networks. While such ML models are an important tool, intelligent behavior extends over time and needs to be explained and understood as such. We may have superhuman chess agents, but can they teach us how to play? We may have search & rescue robots, but can we effectively and efficiently communicate with them in the field?

This topical collection targets high-quality original papers covering all aspects of explainable sequential decision-making. Manuscripts that extend a previous conference or workshop publication are welcome, provided that there is a significant amount (at least 30%) of new material in the submission. Relevant topics include, but are not limited to, the following:

  • Explainable/interpretable/intelligible reinforcement learning
  • Explainable planning and search
  • Explainability in Multi-Agent Systems
  • Explainability for and through negotiations or argumentation
  • Extended explanatory dialogue with users
  • Modeling users over extended interactions
  • Explanation-aware sequential decision-making
  • Foundational frameworks for formalizing and evaluating explainable agency in sequential decision-making settings
  • Integration of explainable agents and explainable deep learning, e.g. when DL models are guiding agent behaviors
  • User interfaces/visualizations for explaining agent behavior, learning or planning
  • Evaluation methods for explainable sequential decision-making systems
  • Explainability for embodied systems/robotics
  • Other practical applications for explainability in sequential or goal-oriented tasks, e.g. in planning/scheduling, in pathfinding, etc.
  • Policy/plan summarization
  • Cognitive theories
  • Empirical studies in explainable sequential decision making

Guest Editors

Hendrik Baier (Lead Guest Editor), Eindhoven University of Technology, The Netherlands, h.j.s.baier@tue.nl
Sarath Sreedharan, Colorado State University, USA, ssreedh3@colostate.edu

Manuscript submission deadline: 1st July 2024

Peer Review Process

All the papers will go through peer review,  and will be reviewed by at least two reviewers. A thorough check will be completed, and the guest editor will check any significant similarity between the manuscript under consideration and any published paper or submitted manuscripts of which they are aware. In such case, the article will be directly rejected without proceeding further. Guest editors will make all reasonable effort to receive the reviewer’s comments and recommendation on time.

The submitted papers must provide original research that has not been published nor currently under review by other venues. Previously published conference papers should be clearly identified by the authors at the submission stage and an explanation should be provided about how such papers have been extended to be considered for this special issue (with at least 30% difference from the original works).

Submission Guidelines

Paper submissions for the special issue should strictly follow the submission format and guidelines (https://www.springer.com/journal/521/submission-guidelines (this opens in a new tab)). Each manuscript should not exceed 16 pages in length (inclusive of figures and tables).

Manuscripts must be submitted to the journal online system at https://www.editorialmanager.com/ncaa/default.aspx (this opens in a new tab) or via the 'Submit manuscript' button on the journal homepage.
Authors should select “TC: Explainable Sequential Decision Making” during the submission step ‘Additional Information’.

Author Resources

Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals.  
Springer provides a host of information about publishing in a Springer Journal on our Journal Author Resources page, including  FAQs (this opens in a new tab),  Tutorials (this opens in a new tab)  along with  Help and Support (this opens in a new tab).
Other links include:

Search

Navigation