Designing organized multiagent systems through MDPs

In complex, open, and heterogeneous environments, agents must be able to reorganize towards the most appropriate organizations to adapt unpredictable environment changes within Multi-Agent Systems (MAS). Types of reorganization can be seen from two different levels. The individual agents level (micro-level) in which an agent changes its behaviors and interactions with other agents to adapt its local environment. And the organizational level (macro-level) in which the whole system changes it structure by adding or removing agents. This chapter is dedicated to overview different aspects of what is called MAS Organization including its motivations, paradigms, models, and techniques adopted for statically or dynamically organizing agents in MAS.

2008

Abstract The ability to create effective multi-agent organizations is key to the development of larger, more diverse multi-agent systems. In this article we present KB-ORG: a fully automated, knowledge-based organization designer for multi-agent systems. Organization design is the process that accepts organizational goals, environmental expectations, performance requirements, role characterizations, and agent descriptions and assigns roles to each agent.

Research Square (Research Square), 2024

To design complex Multi-Agent Systems (MAS), the use of high-level abstraction concepts such as roles, protocols, and groups, makes the task relatively easier and closer to the reality of the system or domain that we want to simulate or create. These concepts were introduced through the multi-agent Organizational Models (OM) which can be seen at two levels: i) an abstract level which is the Organizational Structure (OS) and ii) a concrete level which is the Concrete Organization (CO) to be deployed and executed. This article is dedicated to the formal speci cation and validation of the identi ed roles in the Organizational Structure before a real deployment to generate systems with organizations exhibiting good qualities. Also, a proposal of a set of steps to automatically generate the code of the designed agent will be presented. In addition, the evaluation of some organizational aspects will be done at the abstract level makes it possible to adjust the design to remedy any failures before a real instantiation. The application domain used in this context, is a Cooperative Information Gathering System (CIGS) for travel organization.

2010

ABSTRACT Agent coordination and communication are important issues in designing decentralised agent systems, which are often modelled as flavours of Markov Decision Processes (MDPs). Because communication incurs an overhead, various scenarios for sparse agent communication have been developed. In these treatments, coordination is usually considered more important than making use of local information.

International Journal of Agent-Oriented Software Engineering, 2007

Multi-Agent Systems (MAS) has evolved towards the specification of global constraints that heterogeneous and autonomous agents are supposed to follow when concerning open systems. A subset of these constraints is known as the MAS organisation. This article describes a set of computational tools that supports the development and the programming of such systems. At the system level, it is provided a middleware which ensures that all agents will follow the organisational constraints. At the agent level, the AgentSpeak language is extended, using Jason features, so that the agents can perceive and act upon the organisation they belong.

Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems - AAMAS '05, 2005

We analyze the notion of organizational structure in multiagent systems and explain the precise added value and the effects of such organizational structure on the involved agents. To pursue this aim, contributions from social and organization theory are considered which provide a solid theoretical foundation to this analysis. We argue that organizational structures should be seen along at least three dimensions, instead of just one: power, coordination, and control. In order to systematize the approach, formal tools are used to describe the organizational structure as well as the effect of such structures on the activities in multiagent systems. We specify the properties and the consequences of organizational structures for the actions of the involved agents.

2002

Team formation, i.e., allocating agents to roles within a team or subteams of a team, and the reorganization of a team upon team member failure or arrival of new tasks are critical aspects of teamwork. We focus in particular on a technique called "Team Formation for Reformation", i.e., teams formed with lookahead to minimize costs of reformation. Despite significant progress, research in multiagent team formation and reorganization has failed to provide a rigorous analysis of the computational complexities of the approaches proposed or their degree of optimality. This shortcoming has hindered quantitative comparisons of approaches or their complexity-optimality tradeoffs, e.g., is the team reorganization approach in practical teamwork models such as STEAM optimal in most cases or only as an exception? To alleviate these difficulties, this paper presents R-COM-MTDP, a formal model based on decentralized communicating POMDPs, where agents explicitly take on and change roles to (re)form teams. R-COM-MTDP significantly extends an earlier COM-MTDP model, by analyzing how agents' roles, local states and reward decompositions gradually reduce the complexity of its policy generation from NEXP-complete to PSPACE-complete to P-complete. We also encode key role reorganization approaches (e.g., STEAM) as R-COM-MTDP policies, and compare them with a locally optimal policy derivable in R-COM-MTDP, thus, theoretically and empirically illustrating the complexityoptimality tradeoffs.

Lecture Notes in Computer Science, 2007

In this paper we present a methodology to specify Multiagent Systems, called MASINA. MASINA is based on MAS-CommonKADS; we use the models presented in this methodology to propose some extensions, modifications and substitutions allowing to describe the intelligent characteristics of an agent or group of agents, to use intelligent techniques for the accomplishment of tasks (e.g. artificial neural networks), and to specify emergent coordination approaches, among others.

電子情報通信学会技術研究報告 Ai 人工知能と知識処理, 1999

Although there are many projects focusing on multiagent systems, there are only a few focusing on systematic design of large scale multiagent system. In this paper we will formalize the knowledge representation and sharing of agents, using symbol structures, define agencies as organizations (i.e., a coalition of agents), propose a formalism to represent organizational Intelligence, devise a basic configuration for generalized agents (AG), and derive certain patterns for generalized agencies (GAG) in order to use them effectively in a large scale multiagent system design. The private knowledge of an AG agent is represented by symbol structure and AG agents can share their knowledge using combination, specialization and generalization methods. Opposite to the other works, organizational knowledge, is defined as a property of at least a pair of AG agents. GAG is defined as an organization of AG agents. Each GAG describes a problem, such as a business process that happens repeatedly, and describes the participant agents as well as the process towards the solution to the problem. Furthermore, alternative configurations, decisions and trade-offs are defined. Electronic commerce is an example of such systems.

Today within the multiagent community, we see at least four competing methods to building multiagent systems: beliefdesire-intention (BDI), distributed constraint optimization (DCOP), distributed POMDPs, and auctions or game-theoretic methods. While there is exciting progress within each approach, there is a lack of cross-cutting research. This article highlights the various hybrid techniques for multiagent teamwork developed by the teamcore group. In particular, for the past decade, the TEAMCORE research group has focused on building agent teams in complex, dynamic domains. While our early work was inspired by BDI, we will present an overview of recent research that uses DCOPs and distributed POMDPs in building agent teams. While DCOP and distributed POMDP algorithms provide promising results, hybrid approaches allow us to use the complementary strengths of different techniques to create algorithms that perform better than either of their component algorithms alone. For example, in the BDI-POMDP hybrid approach, BDI team plans are exploited to improve POMDP tractability, and POMDPs improve BDI team plan performance.