arXiv subjects

Antonio García-Domínguez

Publications and source records attributed to Antonio García-Domínguez.

4 recordsLinked to original sources

arXiv2026

A Comparative Study of MCP and A2A for Inter-Agent Coordination in LLM-Based Systems

Recent industry practice has seen the rapid emergence of agentic systems composed of heterogeneous, tool- and LLM-mediated agent components, raising practical questions about inter-agent coordination and protocol design. This paper presents an implementation-grounded comparison of the Model Context Protocol (MCP) and the Agent2Agent (A2A) protocol, from a multi-agent systems engineering perspective, using an inter-agent coordination scenario involving LLM-based agents. We evaluate an MCP-based and an A2A-based multi-agent implementation of the same software engineering task against a set of requirements derived from prior literature and discussions with industry partners, including agent discoverability, multi-part messaging, multi-turn conversations, asynchronous communication, observability, interoperability, and access control. The results evidence that MCP can support inter-agent coordination in constrained LLM-based systems through a comparatively lightweight implementation model with lower coordination complexity, although coordination concerns such as conversational state management and task lifecycle handling must be implemented explicitly at the application layer. In contrast, A2A provides richer native support for stateful, multi-turn coordination through protocol-level abstractions for tasks and lifecycle management, but this comes with substantially greater implementation and coordination complexity. Given the narrow scope of the evaluated coordination pattern, these findings are presented as design observations from an empirical experience report rather than general claims of protocol suitability or superiority across broader classes of MAS, highlighting trade-offs and how protocol abstractions shape the distribution of coordination responsibilities in contemporary agentic systems.

cs.MA↗

arXiv2024

Tree-Based versus Hybrid Graphical-Textual Model Editors: An Empirical Study of Testing Specifications

Tree-based model editors and hybrid graphical-textual model editors have advantages and limitations when editing domain models. Data is displayed hierarchically in tree-based model editors, whereas hybrid graphical-textual model editors capture high-level domain concepts graphically and low-level domain details textually. We conducted an empirical user study with 22 participants to evaluate the implicit assumption of system modellers that hybrid notations are superior, and to investigate the tradeoffs between the default EMF-based tree model editor and a Sirius/Xtext-based hybrid model editor. The results of the user study indicate that users largely prefer the hybrid editor and are more confident with hybrid notations for understanding the meaning of conditions. Furthermore, we found that the tree editor provided superior performance for analysing ordered lists of model elements, whereas activities requiring the comprehension or modelling of complex conditions were carried out faster through the hybrid editor.

cs.SE↗

arXiv2023

A Framework for History-Aware Hyperparameter Optimisation in Reinforcement Learning

A Reinforcement Learning (RL) system depends on a set of initial conditions (hyperparameters) that affect the system's performance. However, defining a good choice of hyperparameters is a challenging problem. Hyperparameter tuning often requires manual or automated searches to find optimal values. Nonetheless, a noticeable limitation is the high cost of algorithm evaluation for complex models, making the tuning process computationally expensive and time-consuming. In this paper, we propose a framework based on integrating complex event processing and temporal models, to alleviate these trade-offs. Through this combination, it is possible to gain insights about a running RL system efficiently and unobtrusively based on data stream monitoring and to create abstract representations that allow reasoning about the historical behaviour of the RL system. The obtained knowledge is exploited to provide feedback to the RL system for optimising its hyperparameters while making effective use of parallel resources. We introduce a novel history-aware epsilon-greedy logic for hyperparameter optimisation that instead of using static hyperparameters that are kept fixed for the whole training, adjusts the hyperparameters at runtime based on the analysis of the agent's performance over time windows in a single agent's lifetime. We tested the proposed approach in a 5G mobile communications case study that uses DQN, a variant of RL, for its decision-making. Our experiments demonstrated the effects of hyperparameter tuning using history on training stability and reward values. The encouraging results show that the proposed history-aware framework significantly improved performance compared to traditional hyperparameter tuning approaches.

cs.LG↗

arXiv2011

Saying Hello World with Epsilon - A Solution to the 2011 Instructive Case

Epsilon is an extensible platform of integrated and task-specific languages for model management. With solutions to the 2011 TTC Hello World case, this paper demonstrates some of the key features of the Epsilon Object Language (an extension and reworking of OCL), which is at the core of Epsilon. In addition, the paper introduces several of the task-specific languages provided by Epsilon including the Epsilon Generation Language (for model-to-text transformation), the Epsilon Validation Language (for model validation) and Epsilon Flock (for model migration).

cs.SE↗