Multi-agent Systems Research Research Articles

Decision as choice of potential intentions

Multi-agent systems research has been drawing innovative insights from other disciplines such as philosophy and cognitive science. While cognitive studies have shown that over 95% of human decisions conform to the recognition-primed decision making RPD model, there still lacks formal descriptions of RPD from agent perspective. The objective of this study is to establish a formal connection between agent mental attitudes and the meta-cognition concepts as used in RPD. We adopt the intentional attitudes as introduced in the SharedPlans theory to model the RPD process and its cognitive activities. In particular, situation recognition is framed as the formation of potential intentions, mental simulation and expectancy monitoring are modeled as two processes for intention evolution, and recognition diagnosis is linked to the notion of intention reconsideration. Such a mentalistic modeling of recognition suggests that the RPD process offers an ideal framework that can systematically integrate intention formation, intention evaluation, and intention reconsideration---three key activities that BDI agents typically implement. This study also indicates that agent-based modeling is a viable approach for formally studying meta-cognition concepts and team cognition concepts.

Trust and Normative Control in Multi-Agent Systems

Despite relevant insights from socio-economics, little research in multi-agent systems has addressed the interconnections between trust and normative notions such as contracts and sanctions. Focusing our attention on scenarios of betrayal, in this paper we combine the use of trust and sanctions in a negotiation process. We describe a scenario of dyadic relationships between truster agents, which make use of trust and/or sanctions, and trustee agents, characterized by their ability and integrity, which may influence their attitude toward betrayal. Both agent behavior models are inspired in socio-economics literature. Through simulation, we show the virtues and shortcomings of using trust, sanctions, and a combination of both in processes of selection of partners.

Agent-based manufacturing control based on distributed bid selection and publish-subscribe messaging: a simulation case study

Bidding-based negotiation schemes play a major role in multi-agent manufacturing systems research. Despite some concerns with message congestion, researchers have been proposing and studying negotiation schemes based on the contract net protocol (CNP). On the other hand, research in robotics has considered a variant of CNP based on publish-subscribe messaging designed for multi-robot coordination. A distinct feature of this variant involves distributing the bid evaluation and selection functions among robot agents. This paper discusses our adaptation of this design variant for multi-agent manufacturing systems and examines its performance implications. Using discrete-event simulation, we study how the adapted CNP design can help address the message congestion problem by cutting down on negotiation slack time. Our case study results show that it can enhance the resilience of the agent negotiation process to message congestion, thereby contributing to the overall performance of a multi-agent manufacturing system.

Summary on Research of Agent and Multi-Agent System

Distributed artificial intelligence is an important branch of the artificial intelligence， Agent and Multi-agent system are important aspects of distributed artificial intelligence research. This paper mainly introduce some research and exiting problems about Agent and Multi-agent system in recent years, including the Agent concept, characteristics and structure, Multi-agent system concept, structure and the coordination problem. Finally, I look forward to the development trend of Agent and Multi-agent system.

The Research Progress of Soccer Learning Mechanisms for Robot

This study makes a review of learning theories, methods, technologies and applications about robot soccer player’s behavior how to implement complex tasks. And pointed out that the limitations of its existence, as well as learning strategies of robot soccer. As a test bed of multi-agent system research of robot soccer system, many researchers conducted a study on the technology from different sides, and have achieved some success. The study indicated that researches currently on robot soccer system includes soccer robot architecture, collaboration, under the dynamic environment of a robot, sensor data fusion, complex reasoning and action learning in task, opponent modeling, and more.

Special issue: Development of service-based and agent-based computing systems

This special issue presents the best papers from theworkshops onService-OrientedComputing: Agents, Semantics and Engineering (SOCASE 2010) held in May 2010 in Toronto, Canada and the IEEE 2010 First International Workshop on Service-Oriented Computing and Multi-Agent Systems (SOCMAS 2010) held in July 2010 in Miami, Florida, USA. The goal of the workshops was to present the recent significant developments at the intersections of multi-agent systems, semantic technology, and service-oriented computing, and to promote crossfertilization of techniques. In particular, the workshops attempted to identify techniques from research on multi-agent systems and semantic technology that will have the greatest impact on automating serviceoriented application construction and management, focusing on critical challenges such as service quality assurance, reliability, and adaptability. The areas of service-oriented computing and Semantic Web services offer much of real interest to the multi-agent system community, including similarities in system architectures and provision processes, powerful tools, and the focus on many related issues including quality of service, security, and reliability. In addition, service-oriented computing and Semantic Web services offer various diverse application fields for both the concepts and methodologies of intelligent agent and multi-agent systems. Similarly, techniques developed in the multi-agent systems research community promise to have a strong impact on this fast growing technology. In particular, they enable services to be discovered and enacted across enterprise boundaries. If an organisation bases its success on services provided by others, then it must be able to trust that the services will perform as promised, whenever needed. Researchers in multi-agent systems have investigated such trust mechanisms.

Agent Technologies for Sensor Networks

The development of agent technologies for sensor networks has received increasing research attention within both the sensor network and multi-agent systems research communities. The International Workshops on Agent Technologies for Sensor Networks (ATSN) held in 2007, 2008 and 2009 sought to bring these communities together, and this special issue of The Computer Journal presents extended versions of some of the papers that appeared at these workshops, along with new submissions specifically for this journal.

Security applications of trust in multi-agent systems

The concept of trust as presented here focuses on the trustworthiness, or reliability, of information and information sources. Decision makers, or agents, can create judgments based on previous experience with other agents and by reputation information received from allied agents. These judgments, or trust assessments, are used to predict the behavior of other agents and analyze the trustworthiness, truthfulness, or quality of information. Research concepts have been developed within the trust community, and they are most commonly applied to multi-agent systems research. This work attempts to show that trust research can be directly applied to security problems. Modern trust concepts enforce soft security which can be applied in addition to conventional security methods to build a more robust secure system. This article examines two trust based techniques and demonstrates their basic effectiveness using empirical experimentation. These techniques are then applied in a case study drawn from a more robust domain concerning confidential message transmission. The benefits of applying trust-based techniques to secure a system are measurable, and the costs associated with such techniques are scalable to even the most resource constrained systems.

An Agent-based Federated Learning Object Search Service

Introduction The Brazilian Ministry of Education provides free digital pedagogical content by means of the Virtual and Interactive Net for Education program (RIVED, 2009), distributing these objects through the International Base of Educational Objects repository (BIOE, 2010). The main goal of these programs is to aid in the development and distribution of electronic educational material by using Learning Objects (LO) as the foremost technology to publish and disseminate such material. The material is formed by educational activities, which may contain multimedia resources, animations, and simulations. To locate a particular object in a repository is a difficult problem depending on the rightful indexation and cataloging of its material. This process corresponds to the fulfilling of the LO metadata with correct information. Metadata is information that describes the characteristics of certain documents, material, or LO. The main purpose of metadata is still to be understood and used by people or software agents in cataloging, searching, and similar tasks (Taylor, 2003). The cataloging and indexation process represents one of the greatest issues to locating educational contents, such as learning objects, because it is through this process that these objects can be found through search engines. Incorrect LO cataloging or indexation causes inefficacy in search processes. This situation is aggravated when LO are distributed and maintained in several distinct repositories. The increase of LO production in Brazil (and around the world) by several different institutions has shown the risk that the material remains unused by the general community, or at least with very restricted use, limited only to the members of the institution in case a unified search mechanism exists capable of finding LO in repositories of most anyone in the institution. Currently there is no standard infrastructure that gives support to a unified search and retrieval of educational resources such as LO (CORDRA Management Group, 2009). To assist in this situation, the present work proposes the creation of an agent-based federated catalog of learning objects (AgCAT). The general objective of this system is to provide an infrastructure of federated LO catalogs that are able to help in the search and retrieval of these educational resources. The system will make intensive use of technologies from Distributed Artificial Intelligence (DAI) and Multi-Agent Systems (MAS) research fields (Weiss, 1999; Wooldridge, 2002), seeking to optimize the LO search process. The system will use several protocols and technologies to harvest metadata from LO repositories and digital libraries. Several AgCAT systems can also be federated, forming a federation of LO catalogs. The search for LO in the federation is transparent for its users. A query made in any federated AgCAT system is transparently propagated to all other AgCAT systems in the federation. Therefore, apart from communication delay, a query in any AgCAT system is equivalent to the same query in any other federated system. Only the search propagation protocol must be supported by each federated AgCAT system. The administration and management of each federated AgCAT system is completely independent from the other federated systems, allowing for different institutions to be included easily in the federation. This work presents the functional structure and organization of the AgCAT system, showing the system's architecture, aspects of its prototype, and main results obtained until now. The next two sections present a literature review concerning the main topics related in the present work focusing on the metadata standards supported by AgCAT and the multi-agent technology that supports the system. The following section describes the multi-agent architecture of the system, the organization of its agents, particular details about the formation of the directory federation, and the metadata harvesting process. …

Multi-agent-based Fuzzy Dispatching for Trucks at Container Terminal

At container terminals, containers are transported from the marshalling yard to the quay and vice versa by Container Trucks (CTs). This study discusses how to dispatch CTs by utilizing information about pickup and delivery locations and time in future delivery tasks based on dynamic dispatching strategy in which multiple tasks are matched with multiple CTs. n this paper, Multi-agent system (MAS) is used as the basis for an intelligent dispatch system. To aim at that the characteristic of management of container terminal is how to optimize resource of terminal, the trends of decision-making way for management of container terminal, research and application of Multi-Agent system is summarized. Relationship between transport tasks and service of CTS has been taken as a contract net using the fuzzy set theory and method. Considering the load of communication and consultation efficiency in system, the bidirectional negotiation mechanism is adopted. The dispatching model based on Contract Network Protocol (CNP) using bidirectional negotiation is provided for assigning optimal delivery tasks to CTs and fuzzy reasoning process of dispatching decisions is suggested. The method has both virtues of precision of static planning and flexibility of CNP and has been confirmed by cases.

Sequentially optimal repeated coalition formation under uncertainty

Coalition formation is a central problem in multiagent systems research, but most models assume common knowledge of agent types. In practice, however, agents are often unsure of the types or capabilities of their potential partners, but gain information about these capabilities through repeated interaction. In this paper, we propose a novel Bayesian, model-based reinforcement learning framework for this problem, assuming that coalitions are formed (and tasks undertaken) repeatedly. Our model allows agents to refine their beliefs about the types of others as they interact within a coalition. The model also allows agents to make explicit tradeoffs between exploration (forming “new” coalitions to learn more about the types of new potential partners) and exploitation (relying on partners about which more is known), using value of information to define optimal exploration policies. Our framework effectively integrates decision making during repeated coalition formation under type uncertainty with Bayesian reinforcement learning techniques. Specifically, we present several learning algorithms to approximate the optimal Bayesian solution to the repeated coalition formation and type-learning problem, providing tractable means to ensure good sequential performance. We evaluate our algorithms in a variety of settings, showing that one method in particular exhibits consistently good performance in practice. We also demonstrate the ability of our model to facilitate knowledge transfer across different dynamic tasks.

Overview of Policy-Oriented and Trusty Multi-Agent System Research

In a multi-agent system, the behavior of agent is difficult to be predicted and controlled reliably because of agent's autonomy, thereby which causes the “trust” crisis that whether the overall goal can be completed or not. In this paper, the multi-agent system credibility of research is introduced from the trust and reputation, and the existing collaboration systems are pointed out that which is lack of controllability and credibility, finally the method of solving credibility of multi-agent system is proposed, namely a two-tier policy-oriented mechanism and a credibility ensuring principle. The executing of system is controlled and guided from looking the collaborators, collaboration established, consultations and negotiations and other sectors, thus the implementation behavior of the system is ensured that which is controllable and trusty.

Comparing multiagent systems research in combinatorial auctions and voting

In a combinatorial auction, a set of items is for sale, and agents can bid on subsets of these items. In a voting setting, the agents decide among a set of alternatives by having each agent rank all the alternatives. Many of the key research issues in these two domains are similar. The aim of this paper is to give a convenient side-by-side comparison that will clarify the relation between the domains, and serve as a guide to future research.

Graph Process Specifications for Hybrid Networked Systems

Research in multi-agent systems has supplied a diverse collection of decentralized controllers to accomplish specific tasks. When agents execute a sequence of these controllers, the network behaves as a hybrid system, where the dynamics in each mode evolve according to a single controller in the sequence. This paper presents a formal specification for such a system that describes the underlying graph process associated with the information flow amongst agents in each mode. Since many decentralized controllers require specific information graph topologies in order to function properly, a problem that arises is that the information graph at the termination of one mode may not be sufficient to initiate the next mode in the sequence. We propose a Graph Process Specification (GPS) framework that describes the graph process. Furthermore, if two modes cannot be executed consecutively, a GPS provides a way to determine which modes can be inserted in between them to make the resulting sequence executable. We formally define a GPS, describe its execution, and provide examples that showcase its usage in composing together multiple decentralized controllers within a multi-agent system.

Emergent properties for data distribution in a cognitive MAS

Emergence is a key element in the research of multi-agent systems. Emergent properties provide higher level features to a system formed of simpler individuals. So far, emergence has been studied mostly through systems formed of reactive agents - that are easier to design and implement. As computational power increases and even small devices become more capable, cognitive agents become a promising solution for more complex problems. This paper presents a multi-agent system that uses cognitive agents and that is designed to manifest emergent properties: a data storage system that assures distribution and replication of data as emergent properties, without the need for these features to be controlled in a centralized way.

An Architecture of Intelligent Multi-Agent System: a Case Study in Soccergame Simulation

Multi-agent collaboration or teamwork and learning are critical research challenges especially for a large number of multi-agent applications. In multi-agent system, agents must accurately perceive and act despite a quickly changing and noisy environment. They have to act not only at individual skills but also at collaborative and adversarial behaviours. Soccer team simulations are used to provide a suitable test bed for research in multi-agent collaboration systems. In this paper, we proposed agent architecture for soccer team simulation. It is an agent architecture within which a flexible team organization and decision-making are presented. The proposed agent architecture includes four main components. First, the world model that describes the environment, which provides a very important resource for making decisions. The world model information is collected through environment monitoring and world prediction for the unobserved information. The second component is team organization, which is achieved by means of “Team Agreement”. The third one is an inter-agent communication paradigm that enables team coordination and knowledge transfer between agents. The fourth is a decision-making process, which includes passing policy, shoots policy, and strategic position policy. In the decision-making process, we proposed and implemented heuristic passing algorithm, neural-network-based shooting algorithm, and heuristic strategic position algorithm. Experimental results of the proposed system prove its stability and applicability. Hence more, the experimental results conclude that coaching enhances the proposed collaboration multi-agent system.

AN ARCHITECTURE OF INTELLIGENT MULTI-AGENT SYSTEM: A CASE STUDY IN SOCCER GAME SIMULATION

AbstractMulti-agent collaboration or teamwork and learning are critical research challenges especially for a large number of multi-agent applications. In multi-agent system, agents must accurately perceive and act despite a quickly changing and noisy environment. They have to act not only at individual skills but also at collaborative and adversarial behaviours. Soccer team simulations are used to provide a suitable test bed for research in multi-agent collaboration systems. In this paper, we proposed agent architecture for soccer team simulation. It is an agent architecture within which a flexible team organization and decision-making are presented. The proposed agent architecture includes four main components. First, the world model that describes the environment, which provides a very important resource for making decisions. The world model information is collected through environment monitoring and world prediction for the unobserved information. The second component is team organization, which is ach...

Evolutionary mechanism design: a review

The advent of large-scale distributed systems poses unique engineering challenges. In open systems such as the internet it is not possible to prescribe the behaviour of all of the components of the system in advance. Rather, we attempt to design infrastructure, such as network protocols, in such a way that the overall system is robust despite the fact that numerous arbitrary, non-certified, third-party components can connect to our system. Economists have long understood this issue, since it is analogous to the design of the rules governing auctions and other marketplaces, in which we attempt to achieve socially-desirable outcomes despite the impossibility of prescribing the exact behaviour of the market participants, who may attempt to subvert the market for their own personal gain. This field is known as "mechanism design": the science of designing rules of a game to achieve a specific outcome, even though each participant may be self-interested. Although it originated in economics, mechanism design has become an important foundation of multi-agent systems (MAS) research. In a traditional mechanism design problem, analytical methods are used to prove that agents' game-theoretically optimal strategies lead to socially desirable outcomes. In many scenarios, traditional mechanism design and auction theory yield clear-cut results; however, there are many situations in which the underlying assumptions of the theory are violated due to the messiness of the real-world. In this paper we review alternative approaches to mechanism design which treat it as an engineering problem and bring to bear engineering design principles, viz.: iterative step-wise refinement of solutions, and satisficing instead of optimization in the face of intractable complexity. We categorize these approaches under the banner of evolutionary mechanism design.

Games and Agents: Designing Intelligent Gameplay

There is an attention shift within the gaming industry toward more natural (long‐term) behavior of nonplaying characters (NPCs). Multiagent system research offers a promising technology to implement cognitive intelligent NPCs. However, the technologies used in game engines and multiagent platforms are not readily compatible due to some inherent differences of concerns. Where game engines focus on real‐time aspects and thus propagate efficiency and central control, multiagent platforms assume autonomy of the agents. Increased autonomy and intelligence may offer benefits for a more compelling gameplay and may even be necessary for serious games. However, it raises problems when current game design techniques are used to incorporate state‐of‐the‐art multiagent system technology. In this paper, we will focus on three specific problem areas that arise from this difference of view: synchronization, information representation, and communication. We argue that the current attempts for integration still fall short on some of these aspects. We show that to fully integrate intelligent agents in games, one should not only use a technical solution, but also a design methodology that is amenable to agents. The game design should be adjusted to incorporate the possibilities of agents early on in the process.

Opportunities for multiagent systems and multiagent reinforcement learning in traffic control

The increasing demand for mobility in our society poses various challenges to traffic engineering, computer science in general, and artificial intelligence and multiagent systems in particular. As it is often the case, it is not possible to provide additional capacity, so that a more efficient use of the available transportation infrastructure is necessary. This relates closely to multiagent systems as many problems in traffic management and control are inherently distributed. Also, many actors in a transportation system fit very well the concept of autonomous agents: the driver, the pedestrian, the traffic expert; in some cases, also the intersection and the traffic signal controller can be regarded as an autonomous agent. However, the "agentification" of a transportation system is associated with some challenging issues: the number of agents is high, typically agents are highly adaptive, they react to changes in the environment at individual level but cause an unpredictable collective pattern, and act in a highly coupled environment. Therefore, this domain poses many challenges for standard techniques from multiagent systems such as coordination and learning. This paper has two main objectives: (i) to present problems, methods, approaches and practices in traffic engineering (especially regarding traffic signal control); and (ii) to highlight open problems and challenges so that future research in multiagent systems can address them.