Role-Based Multi-Agent Systems

• Role-Based Multi-Agent Systems (RBMAS) are defined by explicit assignment of roles to diverse agents, enhancing structured interactions and task coordination.
• Formal techniques such as Colored Petri Nets and the MASA-Method underpin systematic role mapping and communication, ensuring coherent agent behavior.
• The approach fosters scalable integration of humans, robots, and software, enabling dynamic role allocation and improved cooperation in complex environments.

A role-based multi-agent system (RBMAS) is characterized by the explicit definition and assignment of roles to agents—whether they are software, robots, or humans—to structure their behaviors, interactions, and responsibilities within the context of complex, often heterogeneous, environments. The role-based paradigm is distinguished from conventional designs by its emphasis on organizational modeling, where roles, associated knowledge, and communication patterns are central to achieving coherent and adaptive system objectives.

1. Formal Role Modeling and Assignment

In RBMAS, roles provide the foundational abstraction that governs both the structure and the operation of the agent society. Each component—human, robotic, or software—is considered an autonomous agent capable of assuming one or more roles (e.g., designer, worker-programmer, memory image generator, machine) (Lahlouhi, 2014). The system's organization is formally defined as a pair:

$$Organization = (Organization's\ structure, Global\ objective)$$

where the structure comprises roles and their communication links, and the global objective specifies the overarching system goal.

Role assignment is mechanistically mapped: at the design level, using methodologies such as MASA-Method, Colored Petri Nets (CPN) are employed to annotate transitions with roles (e.g., $<t_{DM},\varnothing>$ or $WP.S1.Des$), clearly delineating the knowledge procedures each role governs. This explicit mapping from the organization model to the multi-agent model eliminates the human-as-user dichotomy, ensuring that all agent types are conceptually equivalent and subject only to implementation-specific interface differences.

2. Cooperative Protocols and Interface Agents

Cooperation in RBMAS is enforced through standardized, speech act-based communication protocols and mediated by specialized interface agents. Each interface agent (e.g., $hS$ for humans, $rS$ for robots) holds the objectives and plans of its associated partner (whether human or robot), aligning individual agent behavior with the system’s global objective (Lahlouhi, 2014). Message exchange and synchronization are rigorously modeled in the CPN framework, where transitions for sending and receiving messages are explicitly represented. This guarantees that knowledge and task coordination flow seamlessly between peer agents, regardless of their underlying nature.

Such communication is further structured in two tiers:

• A shared set of communication sensors and effectors
• Task-level transitions specifying explicit points of message emission and reception, e.g., $(WP.S1, performative, action, parameters)$ for emissions.

These mechanisms abstract away underlying heterogeneity and enable the integrated, coherent exploitation of both human and artificial system capabilities.

3. Methodological Foundations: The MASA-Method

The MASA-Method provides a rigorous, multi-level development process for RBMAS, supporting both design-time specification and run-time operation (Lahlouhi, 2014). The methodology includes:

• A multi-agent organizational meta-model encapsulating both roles and task decomposition.
• Design-time formalization via Colored Petri Nets, enabling explicit role-task-procedure relations.
• Derivation procedures for mapping global tasks onto agent role assignments.
• Codification of communication and coordination using CPNs, where transitions become the locus of inter-agent information transfer.

The methodology's systematic workflow:

1. Define the organization (roles; global objectives).
2. Derive an agent model mapping roles to actual agents.
3. Design individual agent (and interface agent) task models as CPNs, with communication as first-class transitions.
4. Implement as a distributed, object-oriented MAS, with unified protocols and role logic.

4. Integration of Heterogeneous Entities

Traditional systems treat humans as mere users or system operators, whereas RBMAS elevates all entities—humans, robots, software—to peer-agent status (Lahlouhi, 2014). The major integration challenges addressed by the role-based view include:

• Elimination of incoherence arising from ill-defined human roles.
• Avoidance of attempts to build universal “user” models.
• Coherent exploitation of heterogeneous system resources.

The introduction of interface agents bridges cognitive, intellectual, and physical asymmetries between system components, ensuring that global objectives can be decomposed and distributed across entities with fundamentally different abilities.

5. Case Study: Manufacturing Society

A canonical application in the paper is the pieces manufacturing society. The organization comprises distinct roles, with CPN-modeled transitions such as:

• Schema design (designer)
• Program translation (worker-programmer)
• Program memory image generation (software)
• Material supply (human worker)
• Manufacturing execution (robot)

Each role is mapped to a specific agent or set of agents, and the workflow—including communication and knowledge transfer—is visualized as successive CPN transitions. The approach’s strengths are evident in stepwise derivation from the organization to the agent model and ultimately to executable agent processes, each role and communication event clearly specified.

6. Comparative Perspective and Scalability

RBMAS departs from conventional user-centric models by assigning clear roles and tasks even to human participants, leading to improved clarity, coordination, and performance (Lahlouhi, 2014). The approach leverages the complementary strengths of robots (speed, precision), software agents (automation, calculation), and humans (adaptability, intelligence). Explicit modeling of communication reduces the risk of incoherent operations and ensures scalability: as new roles are added, they can be integrated into established communication and coordination frameworks without ad hoc extensions.

7. Future Research and Challenges

Several open directions are outlined:

• Enhancement of interface agent design to better handle large-scale, high-dimensional, and graphical communication loads.
• Adaptation towards dynamic role reallocation or modification in changing environments.
• Refinement of speech-act communication protocols for richer, context-sensitive interaction.
• Broadening to domains beyond manufacturing for generality and empirical validation.
• Incorporation of advanced decision-making and learning capabilities in both human-in-the-loop and fully autonomous agents.

Summary Table: Organizational Role Modeling (based on CPN)

Role Type	Description	CPN Transition Example
Designer (D)	Generates production schemas	WP.S1.Des
Worker-Programmer	Translates schema into machine program	WP.I.G
Memory Image Gen.	Converts program into executable forms	PP.I.G
Machine (Robot)	Executes manufacturing with supplied materials	M.Pc.Ma

In summary, role-based multi-agent systems, as rigorously instantiated by the MASA-Method and demonstrated via formal Colored Petri Net models, establish a unified, explicit, and scalable paradigm for integrating heterogeneous agents—human, robotic, and software—within dynamic, cooperative organizations. This design not only provides a structured solution to traditional integration and coordination challenges but also paves the way for future advances in dynamic role assignment, adaptive cooperation, and expansion to broader domains (Lahlouhi, 2014).