Coordination is a key problem in massively multi-agent systems. As applications execute on distributed computer systems, coordination mechanisms must scalably bridge the network distance between where decisions are made and where they are to be enforced. Our work on the CyberOrgs model1 addresses this challenge by encapsulating distributed multi-agent computations along with computational and c...

The Multi-Agent System (MAS) community has developed, over the last few years, a number of fundamental algorithms for coordinating agents in a variety of environments. These algorithms employ both centralised and decentralised forms of coordination and have been shown to reach efficient solutions, albeit with exponential complexity in some cases. For example, distributed constraints optimisatio...

Agent coordination based on Activity Theory postulates that agents control their own behavior from the outside by using and creating artifacts through which they interact. Based on this conception, we envisage social engagements as first-class resources that agents exploit in their deliberative cycle (as well as beliefs, goals, intentions), and propose to realize them as artifacts that agents c...

We consider the scheduling method that allows to minimize traveling time of a AGV (and thus energy consumption) at each zones, while waiting automated guided vehicles (AGVs) and/or containers for pickup and delivery (PD) transfer points (TPs) installed between adjacent zones in tandem systems. Scheduling these systems is often not possible from single point by intelligent agent. Intead, has be ...

A multiagent system is one in which multiple autonomous agents interact to solve complex tasks. Each agent in a multiagent system has its own set of goals. The central problem addressed in this paper is coordination in multiagent systems. Coordination is a process by which, a system of agents work together harmoniously. The focus of this paper is on two types of coordination, resource coordinat...

This paper is intended to give an intuitive overview of the operations of MASA-CIRCA, the Multi-Agent SelfAdaptive Cooperative Intelligent Real-Time Control Architecture. While individual CIRCA agents have been under development for some time, we have only recently begun developing the architecture’s multiagent capabilities. This paper briefly describes the high-level negotiation functions that...

Agents' behaviour is not only dictated by utility calculus, as has frequently been proposed. Even if we adopt a welfarist design, interests and not utilities seem to guide a very important part of agents' behaviour in Multi-Agent Systems (MAS). Agents have rights that protect their interests against additive utilities: Rights allow them enough freedom, but at the same time constrain them (prohi...

Mobile UNITY refers to a notation system and proof logic initially designed to accommodate the special needs of the emerging field of mobile computing. The model allows one to define units of computation and mobility and the formal rules for coordination among them in a highly decoupled manner. In this paper, we reexamine the expressive power of the Mobile UNITY coordination constructs from a n...

Many computer systems are comprised of multiple entities (or agents) with common objectives. Though these systems can be made intelligent, using artificial intelligence techniques, individual agents are often restricted in their capabilities and have only limited knowledge of their environment. However, the group as a whole is capable of executing more complex tasks than a single agent can perf...

Transfer learning leverages an agent’s experience in a source task in order to improve its performance in a related target task. Recently, this technique has received attention in reinforcement learning settings. Training a reinforcement learning agent on a suitable source task allows the agent to reuse this experience to significantly improve performance on more complex target problems. Curren...