multiactor

Multiactor refers to a design and analysis approach in which a system comprises multiple autonomous agents, or actors, that operate, reason, and act concurrently. It is used in fields such as multi-agent systems, distributed artificial intelligence, robotics, and complex simulations. In a multiactor setting, each actor maintains its own state, owns its data, and executes behavior influenced by local goals, perception, and interactions with other actors. Communication typically occurs through asynchronous messaging or interaction protocols, enabling coordination, negotiation, and collaboration without centralized control.

Multiactor architectures vary from fully decentralized to partially centralized coordination. Common mechanisms include message passing, contract-based interactions, conflict resolution, and role assignment. The design must address issues such as synchronization, consistency, fault tolerance, and security, as adding actors increases potential for conflicts or emergent behavior. Important considerations include the granularity of actors, scalability, and the choice of communication standards.

Applications span traffic and swarm simulations, autonomous robotic teams, distributed optimization, adaptive workflows, and electronic marketplaces where multiple agents pursue competing or complementary objectives. Multiactor models are evaluated in terms of performance, robustness, and the quality of emergent solutions, rather than only individual-point measures. Related concepts include the Actor model in computing, multi-agent systems, and distributed artificial intelligence. See also agent-based modeling, distributed systems, and coordination protocols.