Industrial production and trade require efficient and reliable supply networks. Growing interrelations between these networks and the inherent dynamics of the logistics domain result in a high complexity of global supply processes. Application of conventional centralized planning and control to these processes suffers from that complexity. Therefore, a need arises for decentralized methods empl...

Planning is a well known and studied field of Artificial Intelligence. Multi-Agent Planning concerns the construction of plans for a group of autonomous agents that can interact. The aim of multi-agent planning is to automatically find a solution such that, if every agent executes successfully his plan, the environment changes to a goal state. The solution can be found either by centralized or ...

Multi-agent planning (MAP) approaches have been typically conceived for independent or loosely-coupled problems to enhance the benefits of distributed planning between autonomous agents as solving this type of problems require less coordination between the agents’ sub-plans. However, when it comes to tightlycoupled agents’ tasks, MAP has been relegated in favour of centralized approaches and li...

The problem of efficient multirobot coordination has risen to the forefront of robotics research in recent years. Interest in this problem is motivated by the wide range of application domains demanding multirobot solutions. In general, multirobot coordination strategies assume either a centralized approach, where a single robot/agent plans for the group, or a distributed approach, where each r...

Most of the existing autonomous robot systems have a centralized hierarchical control architecture. In such robot systems, all planning, execution control, and monitoring tasks are performed by a single control unit on a defined level. In case of an error that occurs during the execution, this central control unit has the complete knowledge about the past executed actions and is able to reason ...

The problem of efficient multirobot coordination has risen to the forefront of robotics research in recent years. Interest in this problem is motivated by the wide range of application domains demanding multirobot solutions. In general, multirobot coordination strategies assume either a centralized approach, where a single robot/agent plans for the group, or a distributed approach, where each r...

Designing autonomous aerial robot team systems remains a grand challenge in robotics. Existing works this field can be categorized as centralized and decentralized. Centralized methods suffer from scale dilemmas, while decentralized ones often lead to poor planning quality. In paper, we propose an enhanced system with group planning. According the spatial distribution of agents, dynamically div...

The purpose of this paper is to present a C2 holonic reference architecture (HRA) that is applicable to Navy maritime headquarters (MHQ) with maritime operations center (MOC) for assessing, planning and executing multiple missions and tasks across a range of military operations. The control architecture consists of three levels: strategic level control (SLC), operational level control (OLC) and...

This paper considers the permissive supervisor synthesis for probabilistic systems modeled as Markov Decision Processes (MDP). Such systems are prevalent in power grids, transportation networks, communication networks and robotics. Unlike centralized planning and optimization based planning, we propose a novel supervisor synthesis framework based on learning and compositional model checking to ...