2021 The Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, Doctoral Consortium.
Paper Link: https://www.ijcai.org/proceedings/2021/722
Abstract
Distributed artificial intelligence (DAI) studies artificial intelligence entities working together to reason, plan, solve problems, organize behaviors and strategies, make collective decisions and learn.
This Ph.D. research proposes a principled Multi-Agent Systems (MAS) cooperation framework, Self-Adaptive Swarm System (SASS), to bridge the fourth level automation gap between perception, communication, planning, execution, decision-making, and learning.
In the SASS, Robot Needs Hierarchy is the foundation. It surveys the system's utility from individual needs. Balancing the rewards between agents and groups for MAS through interaction and adaptation in cooperation optimizes the global system's utility and guarantees sustainable development for each group member, much like human society does.
As a novel DAI model, from modeling the MAS perspective, the planning and control govern the individual low-level safety and basic needs; capability and teaming needs are the preconditions and requirements of MAS cooperation in decision-making for achieving tasks; individuals upgrade themselves from interaction, cooperation, and adaptation in the process for the highest level needs learning, helping SASS self-evolution.