EMAS 2017 » Submission Instructions

Submission Instructions

As a workshop, EMAS 2017 is an event which encourages peers to present new ideas, initiate new collaborations, and share experiences in the broad field of multi-agent systems engineering. We solicit the submission of papers that the authors might not consider ready for a journal publication but could be included in the post-proceedings (Springer LNAI series). These papers can make unorthodox claims, offer new views on traditional methods, and would typically generate fruitful and lively discussions.

Following the ICSE paper classification, authors are asked to identify their papers with one or more of the categories listed below:

  • Methodological
    • A paper in which the main contribution is a coherent system of broad principles and practices to interpret or solve a problem. This includes novel requirements elicitation methods, process models, design methods, development approaches, programming paradigms, and other methodologies. The authors should provide convincing arguments, ideally supported by commensurate experiences, why a new method is needed and what the benefits of the proposed method are in comparison to existing methods.
  • Analytical:
    • A paper in which the main contribution relies on new mathematical theory or algorithms. Examples include new logics and semantics for agent programming languages, algorithms for agent reasoning, algorithms for the efficient implementation of MAS languages. The paper should at least contain a detailed discussion of advantages and drawbacks with respect to related work. Proposals for a novel language (feature) should in particular provide arguments and/or examples to motivate its purpose in the context of engineering multi-agent systems, for example making it easier to express particular aspects of agent decision making. More thorough analysis techniques (a proof, complexity analysis, or run-time analysis, among others) are welcome.
  • Technological:
    • A paper in which the main contribution is of a technological nature. This includes novel tools, environments, testbeds, modeling languages, infrastructures, and other technologies. Such a contribution does not necessarily need to be evaluated with humans. However, clear arguments, backed up by evidence as appropriate, must show how and why the technology is beneficial, whether it is in automating or supporting some user task, refining our modeling capabilities, improving some key system property, etc.
  • Empirical:
    • A paper in which the main contribution is the empirical study of a MAS engineering technology or phenomenon. This includes empirical studies of the use of (existing or novel) MAS engineering methodologies and techniques in practice, such as (industrial) experience reports, controlled experiments, and case studies, using qualitative and/or quantitative data analysis. This also concerns empirical evaluations of algorithms and performance of MAS platforms. These contributions should clearly explain the motivation for the study, the study design and operation, report on the analysis of the results and lessons learned, and discuss threats to validity. Since EMAS is a workshop, small/preliminary studies are welcome!

Each submitted paper will be evaluated by three PC members. Papers must be original and not submitted simultaneously for publication elsewhere. Papers should be written in English, formatted according to the Springer LNCS style (either in LaTeX or in Word), and not exceed 16 pages.

Submission should be done via EasyChair . The main category has to be selected by the authors using the "categories" box in easychair. Additional categories can be specified at the end of the abstract.

Proceedings containing all accepted papers will be available online and provided electronically on a USB stick. A select number of high-quality submissions will be considered for publication in the workshop post-proceedings. In previous years, EMAS post-proceedings were published by Springer-Verlag in the Lecture Notes in Artificial Intelligence series.