This paper introduces a domain-specific modelling language (DSML) called DSML4JaCaMo to develop belief-desireintention (BDI) agents.The DSML's design covers aspects of Jason, Cartago, and Moise from viewpoints that follow the metamodelling approach.In this way, the DSML4JaCaMo enables graphical modelling of JaCaMo's multi-agent systems (MASs), providing comprehensive support for defining agents' beliefs, desires, and intentions (BDI) using Jason, specifying artifacts and their operations with Cartago, and outlining organizational structures and norms via Moise.The DSML's operational semantics ensure seamless integration of these components, facilitating automatic code generation and artifact construction for creating a JaCaMo-based system.The graphical syntax contributes to ease of use, making it accessible for novice and experienced developers.This work aims to enhance the JaCaMo ecosystem by offering a model-driven approach to provide abstraction on MAS development as well as facilitating design and implementation.

Transport systems are pivotal in sustainable development, profoundly impacting social, environmental, and economic sustainability in urban settings. Integrating decision support systems becomes imperative to underscore the necessity for change and facilitate informed policy decisions based on current conditions. This study evaluates Budapest, Hungary's urban public transport system to foster sustainability. Experts in the transportation field in Budapest were engaged as evaluators. The study employs the newly developed Magnitude Based Fuzzy Analytic Hierarchy Process, chosen for its accuracy and computational efficiency compared to existing methods. The obtained results align with those from the Modified Fuzzy Logarithmic Least Squares method, affirming its reliability. Comparative and sensitivity analyses validate, ensure consistency, and test the robustness of the proposed model findings. The study concludes that introducing new buses emerges as the most viable solution for enhancing the service quality of the existing transport system. The adopted results on a real dataset, its application in transportation evaluation, and the recommendation of new buses as an optimal solution offer insights into sustainable urban transport development.

Ordered weighted average (OWA) operator provides a parameterized class of mean type operators between the minimum and the maximum. It is an important tool that can reflect the strategy of a decision maker for decision-making problems. In this study, the idea of obtaining the stress function from OWA weights has been put forward to generalize and characterize OWA weights. The main idea in this paper is mainly constructed on the basis that, generally, stress functions can be constructed using a mixture of constant and linear components. So, we can consider the stress function as a piecewise linear function. For obtaining stress functions as piecewise linear functions, we present a clustering-based approach for OWA weight generalization. This generalization is made using the DBSCAN algorithm as the learning method of a stress function associated with known OWA weights. In the learning process, the whole data set is divided into clusters, and then linear functions are obtained via a least squares estimator.

Development of software agents according to belief–desire–intention (BDI) model usually becomes challenging due to autonomy, distributedness, and openness of multi‐agent systems (MAS). Hence, here, a domain‐specific modelling language (DSML), called DSML4BDI, is introduced to support development of BDI agents. The syntax of the language provides the design of agent components required for the construction of the system according to the specifications of BDI architecture. The implementation of designed MAS on Jason BDI platform is also possible via model‐to‐text transformations built in the DSML. The comparative evaluation results showed that a significant amount of artefacts required for the exact MAS implementation can be automatically achieved by employing DSML4BDI. Moreover, time needed for developing a BDI agent system from scratch can be reduced to one‐third in the case of using DSML4BDI. Finally, qualitative assessment, based on the developers’ feedback, exposed how DSML4BDI facilitates development of BDI agents.