Towards Human-Centric, Traceable Negotiation Mechanisms for Sharing Autonomy in Multi-Agent Systems

Task allocation in multi-agent system can be defined as a problem of allocating a number of agents to the task. One of the problems in task allocation is to optimize the allocation of heterogeneous agents when there are multiple tasks which require several capabilities. To solve that problem, this research aims to modify the Ant Colony Optimization (ACO) algorithm so that the algorithm can be employed for solving task allocation problems with multiple tasks. In this research, we optimize the performance of the algorithm by minimizing the task completion cost as well as the number of overlapping agents. We also maximize the overall system capabilities in order to increase efficiency. Simulation results show that the modified ACO algorithm has significantly decreased overall task completion cost as well as the overlapping agents factor compared to the benchmark algorithm.

Potential game for dynamic task allocation in multi-agent system

MASQ (Multi-Agent Systems based on Quadrants) is a generic metamodel that integrates many of the concepts issued from the research in the multiagent systems field by defining four perspectives over agent-based interaction according to two axes: internal/external and individual/collective. The aim of this paper is to provide a formal description of MASQ by specifying clear relations between the basic elements of the four quadrants and by analysing the dynamics of a multi-agent system through its transitions and through the classical mind cycle. The proposed formal model has been designed as a theoretical framework and therefore the specifications of many components of a multi-agent system such as the internal architecture of minds, the casual laws of environments or the social laws in organizations and institutions are intentionally left generic to allow for many further concretizations.

A major challenge in the field of multi-agent systems is to enable autonomous agents to allocate tasks efficiently. In the context of massive multi-agents system (MMAS) which is characterised by a large number of dynamic and heterogeneous agents, traditional tasks allocations approaches based on the negotiation between agents, or a single allocator agent, proved impracticality. In this paper, we propose a decentralised and scalable approach for complex task allocation pro cooperative MMAS. Our approach is based on the idea of grouping agents according to their capacities to execute sub-tasks. Thus, the approach is based on two steps: 1 hierarchical organisation of agent groups using Galois sub-hierarchy, imminent from the formal concepts analysis approach (FCA) 2 computing the optimal allocation. These two steps are fully distributed among agents with the minimum of communication and a grant of finding the optimal allocation in a polynomial time. Further, this paper extends our last approach by distributing the global allocation process among all agents. It provides a solution based on cooperation among agents. This solution prohibits generation of conflicts. It is based on the idea that each agent has to pick out its own sub-task.

A major challenge in the field of Multi-Agent Systems is to enable autonomous agents to allocate tasks efficiently. In previous work, we have developed a decentralized and scalable method for complex tasks allocation for Massive Multi-Agent System (MMAS) based on two steps: 1) hierarchical organization of agent groups using Formal Concepts Analysis approach (FCA), 2) computing the optimal allocation. The first step is computed by one agent named global allocator that computes Galois lattice representing the hierarchical structure of agent groups. Then, it simplifies the completed lattices by pruning unnecessary groups. The second step distributes the tasks allocation process among all agent groups. Nevertheless, the hierarchical organization process is still centralized. Moreover, generation of Galois lattice composed by all concepts (2 min (|O |,|A |) concepts in the worst cases. Where 0 and A means, respectively, the set of objects and the set of attributs) and then simplification of the hierarchy of such size are not useful. This paper extends our last approach to distribute the organization process of agent groups among all agents by providing extension to the Pulton algorithm that generates Galois Sub-Hierarchy which is a polynomial size representation of a concept lattice. This decentralized self-organization of agents provides a flexible infrastructure for agents' dynamicity in MMAS.

Awareness is a concept that has been frequently studied in the context of Computer Supported Cooperative Work. However, other fields of computer science can benefit from this concept. Recent research in the multi-agent systems field has highlighted the relevance of complex interaction models such as multi-party communication and context awareness for simulation and adaptive systems. In this article, we present a generic interaction model that enables to use these different models in a standardized way. Emerging as a first-order abstraction, the environment, in the sense of a common medium for the agents, is a suitable paradigm to support the agents' awareness. We present an operational model, called Environment as Active Support of Interaction, to take into account all the agents that can be interested in a communication. This model is then extended for the regulation of multiagent systems interactions. Priority policies are given to manage the rules governing the context (un-)awareness of the agents. We also present a new AUML connector to create protocols that take into account the agent awareness to implement proactive behaviour, and several communication scenarios are proposed to show practical applications of this model.

The main challenge that artificial intelligence research is facing nowadays is how to guarantee the development of responsible technology. And, in particular, how to guarantee that autonomy is responsible. The social fears on the actions taken by AI can only be appeased by providing ethical certification and transparency of systems. (See for instance the Barcelona declaration https://www.iiia.csic.es/barcelonadeclaration/) However, this is certainly not an easy task. As we very well know in the multiagent systems field, the prediction accuracy of system outcomes has limits as multiagent systems are actually examples of complex systems. And AI will be social, there will be thousands of AI systems interacting among themselves and with a multitude of humans; AI will necessarily be multiagent. Although we cannot provide complete guarantees on outcomes, we must be able to define with accuracy what autonomous behaviour is acceptable (ethical), to provide repair methods for anomalous behaviour and to explain the rationale of AI decisions. Ideally, we should be able to guarantee responsible behaviour of individual AI systems by construction. I understand by an ethical AI system one that is capable of deciding what are the most convenient norms, abide by them and make them evolve and adapt. The area of multiagent systems has developed a number of theoretical and practical tools that properly combined can provide a path to develop such systems, that is, provide means to build ethical-by-construction systems: agreement technologies to decide on acceptable ethical behaviour, normative frameworks to represent and reason on ethics, and electronic institutions to operationalise ethical interactions. Along my career, I have contributed with tools on these three areas. In this keynote, I will describe a methodology to support their combination that incorporates some new ideas from law, and organisational theory.

In this paper, the paradigm of rationality is examined from the point of view of designing, and possibly implementing, agents endowed with the capacity to interact in a multi-agent environment. The work that will be presented originates from a cross-fertilization between the fields of social simulation and Multi-Agent Systems (MAS) research. In the MAS field, models of intelligent agents in a multi-agent context have been formalized and implemented. The question then arises as to whether the model of rational agents may contribute fruitfully to construct and implement multi-agents systems.

In the field of multi-agent systems, a lot of attention has been focused lately on investigating various architectures and methodologies that promote effective organization and coordination within large-scale, complex, distributed systems. Specifically, the interest is in developing approaches that can be implemented within multi-agent systems to produce desirable emergent behavior that coordinates individual actors in a system competing for resources such as bandwidth, computing power, and data. This extended abstract outlines polymorphic self-* agents that evolve a core set of roles and behavior based on environmental cues. The agents adapt these roles based on the changing demands of the environment. The design combines strategies from game theory, stigmergy, and other biologically inspired models.

Nowadays, in order to face new challenges, extended enterprises must be up to date to the new strategic, economic and organizational structures. Consequently, intelligent software based on agent technology emerges to improve system design, and to increase enterprise competitive position as well. The competitiveness is based on the cooperation. Thus, within this cooperation, conflicts may arise. The automated negotiation plays a key role to look for a common agreement. Argumentation theory has become an important topic in the field of Multi-Agent Systems and especially in the negotiation problem. In this paper, first, an overview of an already proposed model MAIS-E2 (Multi-Agent Information System for an Extended Enterprise) is presented. Then an argumentation based negotiation framework: Relationship-Role and Interest Based Negotiation (R2-IBN) framework is presented, and within this framework, we focused mainly on, argument generation module via inference rules and argument selection module via fuzzy logic.

The paper considers the approach of using multi-agent models in simulation and decision making of complex systems. Furthermore, an overview of research in the field of multi-agent systems and software platforms for their implementation is carried out. In addition, an analysis of the applicability of intelligent multi-agent systems in the tasks of modelling, optimization and management of energy facilities is performed. This study shows that power industry can use intelligent multi-agent systems not only for the tasks of modelling the interaction of subjects as suppliers and consumers of electricity in the wholesale electricity market and other problems of game theory considering a small number of agents’ types. Moreover, intelligent multi-agent systems can be used to model power systems when it is necessary to take into account the deep integration of the systems. This is understood in the work as dependencies at the level of physical processes of generation, transmission, distribution and consumption of electricity, and at the level of interaction between large technological objects and economic entities. At the second level, it is necessary to consider infrastructure linkages and legal regulations for the economic interaction. As an example, the paper considers a hydropower plant. The entity that manages it must take into account not only the features of the station as a complex electrical facility itself, but also the features of the water usage regime, electricity consumption schedules, meteorological factors, etc. In turn, the use of water resources affects the interests of a large number of subjects, which also needs to be reckoned.

A survey of modelling and smart management tools for power grids with prolific distributed generation

A supply chain (SC) is a coordinated system involved in moving products or services from supplier to customer. With growing focus on supply chain management, it is realized that inventories across the supply chain should be efficiently managed through greater cooperation and better coordination. Multi-agent systems (MAS) offer a new dimension for cooperation and coordination. MAS consist of a collection of automated agents who can perform a specific task intelligently without any human supervision and communicate with each other cooperatively. In this research, a multi-agent system has been developed to simulate a multi echelon SC. These agents are coordinated to control inventories and minimize the total cost of SC by sharing information, forecasting knowledge and using negotiation mechanism in special situation. The result showed that in comparison to a traditional supply chain network, this system decreases the total cost and bullwhip effect.

In this paper a new intelligent control architecture for autonomous multi-robot systems is presented. Furthermore, the paper deals with task description, task distribution, task allocation and coordination of the system components. The main advantage of the new control architecture is the distributed execution of tasks or subtasks by components of the multi-robot system. The components are able to build teams dynamically thereby avoiding the bottle neck problem of the information flow in centralized controlled architectures. The communication and cooperation between components to achieve distributed organized control architectures is studied. The described intelligent control architecture is to replace the former control architecture of the autonomous robot KAMRO. >

GCRM (Group Control of Road Machine) system is a new research field for use of multi-agent system (MAS). In order to design a well-defined and easily implemented system based on MAS, there are some problems in system design. In this paper, general issues and special issues about design of a multi-agent system are all concerned. In general issues, architectural problem, coordination and negotiation mechanism and communication mechanism are common components for any scheme of multi-agent system. In special issues, an agent-based supply chain is specially designed in the GCRM system according to the system needs. An agent design considering human factors is put forward. As a result, we investigate some design guidelines from this work.