[Event at CIG] Last CfP of 1st CLaRAMAS workshop at AAMAS 2026

[Stefano Mariani]

Dear Colleague,
we hope this email finds you well :)

This is the last Call for Papers for the 1st International Workshop on 
“Causal Learning and Reasoning in Agents and Multiagent Systems” 
(CLaRAMAS), hosted by the 25th International Conference on Agents and 
Multiagent Systems (AAMAS).

The workshop seeks contributions at the *crossroads between learning of 
and reasoning with causal models, and engineering agents and agent-based 
systems (both programming and learning approaches)*.

The extended _deadline (March 1st) is strict_.
The workshop is _held on May 26th_.
Accepted papers will be published in _Springer CCIS series_.
_Prof. Emiliano Lorini_ will deliver a keynote speech.

Please,*feel free to extend this invitation to your collaborators* that 
you believe may be interested in sharing their work in progress on the 
subject.

Check out the workshop’s full scope & aims and the Call for Papers (also 
reported at the end of this email) on the workshop website: 
https://claramas-workshop.github.io/claramas2026/

  Looking forward to receive your feedback,
  our best regards.

  The CLaRAMAS Program Chairs
     --- Stefano Mariani, Mehdi Dastani, André Meyer-Vitali, Julien Siebert

  ##### Call for Papers #####

The concept of an “agent” represents a foundational abstraction in 
software engineering, encapsulating the notion of agency—namely, the 
capacity of a software entity to bring about effects in pursuit of 
specific goals within its operating environment. Exercising agency 
requires the ability to interpret the structure and dynamics of that 
environment and to anticipate its responses to the agent’s actions. In 
essence,

/agency hinges on understanding and leveraging the causal relationships 
among observable and controllable variables (e.g., through sensors and 
actuators)./

Such causal reasoning is indispensable for planning actions that 
reliably achieve intended objectives—a principle reinforced by recent 
research on causal inference in emerging “agentic AI” systems.

This requirement extends naturally to multi-agent systems (MAS), a 
cornerstone of distributed artificial intelligence, where multiple 
agents coexist and interact within a shared environment. These 
interactions – whether cooperative or competitive – contribute to 
individual and systemic goals, either through direct communication or 
indirect influence on the environment. Consequently,

/effective coordination in multi-agent settings depends on a causal 
understanding of interdependencies among agents’ behaviors./

Only by modeling these reciprocal influences can agents achieve robust 
and purposeful collaboration (or competition) toward their respective 
objectives.

  ⚠️ However, this fundamental role of causal modelling of the 
agent-environment and agent-agent relationships is not yet widely and 
deeply discussed in the AAMAS community. ⚠️

Accordingly, CLaRAMAS welcomes submissions dealing with the following 
topics of interest:

  * how to integrate causal learning in agent architectures and MAS
  * how to carry out causal learning of agent-environment and
    agent-agent relationships from the standpoint of an individual agent
    or of the MAS as a whole
  * how causal modelling and learning can be integrated in
    agent-oriented software engineering methodologies
  * exploring causal explainability, safety, and robustness in agent(s)
    design, for instance in robotic and multi-robot systems
  * how causal learning may integrate with learning-based approaches to
    agent design, such as with Reinforcement Learning for counterfactual
    reasoning, credit assignment, policy evaluation, policy improvement
    in single- and multi-agent systems
  * theoretical foundations of causal learning and reasoning in single-
    and multi-agent systems, including the relationship between
    sequential decision making (e.g., MDPs) and Pearl structural causal
    models, integration of game-theoretic formalisms, etc.
  * practical applications of causal learning and reasoning in MAS
  * cooperative planning, prediction, and diagnosis using (perhaps,
    partially) shared causal models
  * combining causal learning and reasoning with planning and adaptive
    control, including model-based, model-free and hybrid approaches
  * cooperative causal discovery and inference in MAS
  * neuro-symbolic AI via causal models
  * evaluation and benchmarks for causal MAS applications, including
    datasets, metrics, simulators, and reproducible experimental pipelines

  Check out the submission dates and instructions at the CLaRAMAS 
website: https://claramas-workshop.github.io/claramas2026

  ##### #####


-- 
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Stefano Mariani, PhD
Tenure-track researcher (RTDb) @ University of Modena and Reggio Emilia
   >https://smarianimore.github.io
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