580 California St., Suite 400
San Francisco, CA, 94104
Key research themes
1. What are the current software platforms and frameworks available for developing scalable and flexible multi-agent systems in diverse application domains?
This theme focuses on surveying and evaluating the state-of-the-art agent development platforms that provide the necessary infrastructure for building multi-agent systems (MAS). The research examines both historical developments and modern specialized or general-purpose platforms, with attention to their architecture, communication protocols, support for agent models (e.g., BDI), and suitability for different domains or scales. Understanding these platforms is critical for practitioners seeking to leverage mature, active tools for deploying agent-based solutions across industrial automation, mobility, and large-scale distributed environments.
Key finding: This paper provides a comprehensive and updated review of agent platforms, differentiating between general-purpose and domain-specific platforms. Highlighted is the ongoing evolution of these platforms over nearly 40 years,... Read more
Key finding: The authors introduce PANGEA, a platform designed to support multi-agent systems modelled as Virtual Organizations, emphasizing flexible organizational topologies, dynamic reconfiguration, and role-based communication. The... Read more
Key finding: This work proposes a cyber-physical production system (CPPS) architecture that integrates Industrial Agents (IAs) based on Multi-Agent Systems, aligning Industry 4.0 standardization efforts (e.g., RAMI4.0, IEEE P2660.1). The... Read more
Key finding: This chapter surveys methodological and architectural approaches that capture the multifaceted nature of agents, such as autonomy, flexibility, and adaptiveness, within the context of simulation and systems engineering. It... Read more
2. How do multi-agent system methodologies and modeling tools address the complexities of agent-oriented software engineering, supporting belief-desire-intention (BDI) architectures and organizational structures?
Research in agent-oriented software engineering (AOSE) methodologies and domain-specific modeling languages (DSMLs) seeks to provide formalized processes and tools for designing MAS that incorporate mentalistic agent models such as BDI, artifact-based environments, and organizational frameworks. These methodologies facilitate systematic analysis, design, and code generation, essential for reliable, scalable, and maintainable MAS development, especially in complex or safety-critical domains.
Key finding: This paper systematically analyzes various AO methodologies, predominantly those extending object-oriented and knowledge engineering approaches to accommodate agent-specific features like mental states, social interactions,... Read more
Key finding: Introducing DSML4JaCaMo, the study presents a metamodeling and graphical modeling tool supporting integrated development of BDI agents (Jason), artifacts (Cartago), and organizational structures (Moise). The tool enables... Read more
Key finding: This paper adapts AOSE for embedded hardware/software hybrid MAS, proposing the DIAMOND method with an iterative spiral lifecycle integrating multi-agent analysis and component-based design. It emphasizes challenges of... Read more
3. How can multi-agent systems be leveraged for modeling complex dynamic environments and what are the benefits of adopting multi-agent system perspectives in process mining and simulation?
This theme investigates the agent-based paradigm's utility in representing and analyzing complex systems through autonomous entities interacting within dynamic environments. It encompasses research on agent-based process mining, simulation approaches, and modeling techniques that provide improved modularity, scalability, and interpretability over traditional monolithic models, enabling richer understanding and control of distributed processes and socio-technical systems.
Key finding: Agent Miner introduces a divide-and-conquer algorithm that mines event logs to reveal agent interactions and individual agent behaviors modeled as Petri nets. Compared to conventional process discovery focusing on control... Read more
Key finding: The paper articulates a multi-layered model of the multi-agent environment specially applied to transportation and mobility domains. It conceptualizes the environment as a spatio-temporal reference and as an interaction... Read more
Key finding: This survey highlights the practical applications and theoretical underpinnings that make MAS a promising approach for modeling complex, distributed environments. It addresses the required autonomy, proactivity, and social... Read more
4. What are the emerging trends and frameworks in Multi-Agent Large Language Models (LLMs) and their implications for collaborative AI systems?
New multi-agent system frameworks leverage the advanced language understanding and generation capabilities of Large Language Models, enabling autonomous agents to collaborate dynamically on complex tasks such as software development, knowledge synthesis, and decision-making. Research explores design principles, role specialization, adaptive workflows, and low-code platforms that lower adoption barriers while highlighting challenges like scalability and cost implications in real-world deployment.
Key finding: This literature review traces the evolution from early rule-based multi-agent systems to sophisticated multi-agent LLM frameworks such as AutoGen, AG2, MetaGPT, and LangGraph. It details architectural advancements enabling... Read more