Showing papers on "Multi-agent system published in 2011"

Output Consensus of Heterogeneous Uncertain Linear Multi-Agent Systems

Abstract: This technical note studies the output consensus problem for a class of heterogeneous uncertain linear multi-agent systems. All the agents can be of any order (which might widely differ among the agents) and possess parametric uncertainties that range over an arbitrarily large compact set. The controller uses only the output information of the plant; moreover, the delivered information throughout the communication network is also restricted to the output of each agent. Based on the output regulation theory, it is shown that the output consensus is reached if the (state) consensus is achieved within the internal models among the agent's controllers (even though the plant's outputs, rather than the internal model's outputs, are communicated). The internal models can be designed and embedded into the controller, which provides considerable flexibility to designers in terms of the type of signals that are agreed on among the agents.

Agent-based computing from multi-agent systems to agent-based models: a visual survey

Abstract: Agent-based computing is a diverse research domain concerned with the building of intelligent software based on the concept of "agents". In this paper, we use Scientometric analysis to analyze all sub-domains of agent-based computing. Our data consists of 1,064 journal articles indexed in the ISI web of knowledge published during a 20 year period: 1990---2010. These were retrieved using a topic search with various keywords commonly used in sub-domains of agent-based computing. In our proposed approach, we have employed a combination of two applications for analysis, namely Network Workbench and CiteSpace--wherein Network Workbench allowed for the analysis of complex network aspects of the domain, detailed visualization-based analysis of the bibliographic data was performed using CiteSpace. Our results include the identification of the largest cluster based on keywords, the timeline of publication of index terms, the core journals and key subject categories. We also identify the core authors, top countries of origin of the manuscripts along with core research institutes. Finally, our results have interestingly revealed the strong presence of agent-based computing in a number of non-computing related scientific domains including Life Sciences, Ecological Sciences and Social Sciences.

Consensus of heterogeneous multi-agent systems

Abstract: In this study, the consensus problem of heterogeneous multi-agent system is considered. First, the heterogeneous multi-agent system is proposed which is composed of first-order and second-order integrator agents in two aspects. Then, the consensus problem of heterogeneous multi-agent system is discussed with the linear consensus protocol and the saturated consensus protocol, respectively. By applying the graph theory and Lyapunov direct method, some sufficient conditions for consensus are established when the communication topologies are undirected connected graphs and leader-following networks. Finally, some examples are presented to illustrate the theoretical results.

An agent-based simulation model of human-environment interactions in agricultural systems

Abstract: This paper describes an agent-based software package, called Mathematical Programming-based Multi Agent Systems (MP-MAS), which builds on a tradition of using constrained optimization to simulate farm decision-making in agricultural systems. The purpose of MP-MAS is to understand how agricultural technology, market dynamics, environmental change, and policy intervention affect a heterogeneous population of farm households and the agro-ecological resources these households command. The software is presented using the Overview, Design concepts, and Details (ODD) protocol. Modeling features are demonstrated with empirical applications to study sites in Chile, Germany, Ghana, Thailand, Uganda, and Vietnam. We compare MP-MAS with eight other simulators of human-environment interactions (ABSTRACT, CATCHSCAPE, ECECMOD, IMT, LUDAS, PALM, SAM, and SIM). The comparison shows that the uniqueness of MP-MAS lies in its combination of a microeconomic modeling approach and a choice of alternative biophysical modules that are either coded as part of the software or coupled with it using the Typed Data Transfer (TDT) library.

Sampled-Data Based Consensus of Continuous-Time Multi-Agent Systems With Time-Varying Topology

Abstract: This technical note studies consensus problems of multiple agents with continuous-time second-order dynamics, where each agent can obtain its positions and velocities relative to its neighbors only at sampling instants. It is assumed that the sampling period of each agent is independent of the others' and the interaction topology among agents is time-varying, where the associated direct graphs may not have spanning trees. If the union graph of all direct graphs has a spanning tree, then there exist controller gains and sampling periods such that consensus is reached. Moreover, two approaches are presented to design such controller gains and sampling periods. Simulations are performed to validate the theoretical results.

Epistemic planning for single- and multi-agent systems

Abstract: In this paper, we investigate the use of event models for automated planning. Event models are the action defining structures used to define a semantics for dynamic epistemic logic. Using event models, two issues in planning can be addressed: Partial observability of the environment and knowledge. In planning, partial observability gives rise to an uncertainty about the world. For single-agent domains, this uncertainty can come from incomplete knowledge of the starting situation and from the nondeterminism of actions. In multi-agent domains, an additional uncertainty arises from the fact that other agents can act in the world, causing changes that are not instigated by the agent itself. For an agent to successfully construct and execute plans in an uncertain environment, the most widely used formalism in the literature on automated planning is “belief states”: sets of different alternatives for the current state of the world. Epistemic logic is a significantly more expressive and theoretically better foun...

A Decentralized Approach for Anticipatory Vehicle Routing Using Delegate Multiagent Systems

Abstract: Advanced vehicle guidance systems use real-time traffic information to route traffic and to avoid congestion. Unfortunately, these systems can only react upon the presence of traffic jams and not to prevent the creation of unnecessary congestion. Anticipatory vehicle routing is promising in that respect, because this approach allows directing vehicle routing by accounting for traffic forecast information. This paper presents a decentralized approach for anticipatory vehicle routing that is particularly useful in large-scale dynamic environments. The approach is based on delegate multiagent systems, i.e., an environment-centric coordination mechanism that is, in part, inspired by ant behavior. Antlike agents explore the environment on behalf of vehicles and detect a congestion forecast, allowing vehicles to reroute. The approach is explained in depth and is evaluated by comparison with three alternative routing strategies. The experiments are done in simulation of a real-world traffic environment. The experiments indicate a considerable performance gain compared with the most advanced strategy under test, i.e., a traffic-message-channel-based routing strategy.

Introductory tutorial: agent-based modeling and simulation

Abstract: Agent-based simulation (ABS) is an approach to modeling systems comprised of individual, autonomous, interacting “agents.” Agent-based modeling offers ways to more easily model individual behaviors and how behaviors affect others in ways that have not been available before. There is much interest in developing agent-based models for many application problem domains. Applications range from modeling agent behavior in supply chains and the stock market, to predicting the success of marketing campaigns and the spread of epidemics, to projecting the future needs of the healthcare system. Progress in the area suggests that ABS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use agent-based models as electronic laboratories to aid in discovery. This brief tutorial introduces agent-based modeling and simulation by describing the basic ideas of ABS, discussing some applications, and addressing methods for developing agent-based models.

Dynamic consensus of linear multi-agent systems

Abstract: This study concerns the consensus of a network of agents with general linear or linearised dynamics, whose communication topology contains a directed spanning tree. An observer-type consensus protocol based on the relative outputs of the neighbouring agents is adopted. The notion of consensus region is introduced, as a measure for the robustness of the protocol and as a basis for the protocol design. For neutrally stable agents, it is shown that there exists a protocol achieving consensus together with a consensus region that is the entire open right-half plane if and only if each agent is stabilisable and detectable. An algorithm is further presented for constructing such a protocol. For consensus with a prescribed convergence speed, a multi-step protocol design procedure is given, which yields an unbounded consensus region and at the same time maintains a favourable decoupling property. Finally, the consensus algorithms are extended to solve the formation control problems.

Adaptive and Intelligent Systems for Collaborative Learning Support: A Review of the Field

Abstract: This study critically reviews the recently published scientific literature on the design and impact of adaptive and intelligent systems for collaborative learning support (AICLS) systems. The focus is threefold: 1) analyze critical design issues of AICLS systems and organize them under a unifying classification scheme, 2) present research evidence on the impact of these systems on student learning, and 3) identify current trends and open research questions in the field. After systematically searching online bibliographic databases, 105 articles were included in the review with 70 of them reporting concrete evaluation data on the learning impact of AICLS systems. Systems design analysis led us to propose a classification scheme with five dimensions: pedagogical objective, target of adaptation, modeling, technology, and design space. The reviewed articles indicate that AICLS systems increasingly introduce Artificial Intelligence and Web 2.0 techniques to support pretask interventions, in-task peer interactions, and learning domain-specific activities. Findings also suggest that AICLS systems may improve both learners' domain knowledge and collaboration skills. However, these benefits are subject to the learning design and the capability of AICLS to adapt and intervene in an unobtrusive way. Finally, providing peer interaction support seems to motivate students and improve collaboration and learning.

Adaptive cruise control: hybrid, distributed, and now formally verified

Abstract: Car safety measures can be most effective when the cars on a street coordinate their control actions using distributed cooperative control. While each car optimizes its navigation planning locally to ensure the driver reaches his destination, all cars coordinate their actions in a distributed way in order to minimize the risk of safety hazards and collisions. These systems control the physical aspects of car movement using cyber technologies like local and remote sensor data and distributed V2V and V2I communication. They are thus cyber-physical systems. In this paper, we consider a distributed car control system that is inspired by the ambitions of the California PATH project, the CICAS system, SAFESPOT and PReVENT initiatives.We develop a formal model of a distributed car control system in which every car is controlled by adaptive cruise control. One of the major technical difficulties is that faithful models of distributed car control have both distributed systems and hybrid systems dynamics. They form distributed hybrid systems, which makes them very challenging for verification. In a formal proof system, we verify that the control model satisfies its main safety objective and guarantees collision freedom for arbitrarily many cars driving on a street, even if new cars enter the lane from on-ramps or multi-lane streets. The system we present is in many ways one of the most complicated cyber-physical systems that has ever been fully verified formally.

Consensus problems for linear time-invariant multi-agent systems with saturation constraints

Abstract: A class of networked multi-agent systems is studied in this study where each agent has an identical dynamics of a simple integrator and the topology of the connections is fixed. It is proved that, when there are saturation constraints, a general consensus protocol widely used in the literatures for this class of multi-agent systems remains valid. As an extension, a 'bang-bang' type of consensus protocol is proposed to achieve the finite-time consensus, which relaxes the previous undirected connection assumption.

An Agent-Based Model of Epidemic Spread Using Human Mobility and Social Network Information

Abstract: The recent adoption of ubiquitous computing technologies has enabled capturing large amounts of human behavioral data The digital footprints computed from these datasets provide information for the study of social and human dynamics, including social networks and mobility patterns, key elements for the effective modeling of virus spreading Traditional epidemiologic models do not consider individual information and hence have limited ability to capture the inherent complexity of the disease spreading process To overcome this limitation, agent-based models have recently been proposed as an effective approach to model virus spreading However, most agent-based approaches to date have not included real-life data to characterize the agents' behavior In this paper we propose an agent-based system that uses social interactions and individual mobility patterns extracted from call detail records to accurately model virus spreading The proposed approach is applied to study the 2009 H1N1 outbreak in Mexico and to evaluate the impact that government mandates had on the spreading of the virus Our simulations indicate that the restricted mobility due the government mandates reduced by 10% the peak number of individuals infected by the virus and postponed the peak of the pandemic by two days

Designing games for distributed optimization

Abstract: The central goal in multiagent systems is to design local control laws for the individual agents to ensure that the emergent global behavior is desirable with respect to a given system level objective. Ideally, a system designer seeks to satisfy this goal while conditioning each agent's control law on the least amount of information possible. Unfortunately, there are no existing methodologies for addressing this design challenge. The goal of this paper is to address this challenge using the field of game theory. Utilizing game theory for the design and control of multiagent systems requires two steps: (i) defining a local objective function for each decision maker and (ii) specifying a distributed learning algorithm to reach a desirable operating point. One of the core advantages of this game theoretic approach is that this two step process can be decoupled by utilizing specific classes of games. For example, if the designed objective functions result in a potential game then the system designer can utilize distributed learning algorithms for potential games to complete step (ii) of the design process. Unfortunately, designing agent objective functions to meet objectives such as locality of information and efficiency of resulting equilibria within the framework of potential games is fundamentally challenging and in many case impossible. In this paper we develop a systematic methodology for meeting these objectives using a broader framework of games termed state based potential games. State based potential games is an extension of potential games where an additional state variable is introduced into the game environment hence permitting more flexibility in our design space. Furthermore, state based potential games possess an underlying structure that can be exploited by distributed learning algorithms in a similar fashion to potential games hence providing a new baseline for our decomposition.

Rockwell Automation's Holonic and Multiagent Control Systems Compendium

Abstract: This paper provides a comprehensive overview of long-term R&D activities of the Rockwell Automation (RA) company in the field of holonic and agent-based manufacturing control systems. It presents a coherent framework of methodologies for designing agent-based control systems, tools that support implementation and validation of them, and agent applications that were developed for various industrial systems. The common attribute is the integration of novel techniques, such as multiagent systems or semantics with the current “old-fashioned” automation architectures represented mainly by programmable logic controllers. Retaining the ability of the control system to meet the strict real-time constraints and concurrently raise its intelligence has always been of highest importance. The paper does not focus merely on RA's work, but discusses all its aspects in a context of other existing works in this area.

Multi-agent event recognition in structured scenarios

Abstract: We present a framework for the automatic recognition of complex multi-agent events in settings where structure is imposed by rules that agents must follow while performing activities. Given semantic spatio-temporal descriptions of what generally happens (i.e., rules, event descriptions, physical constraints), and based on video analysis, we determine the events that occurred. Knowledge about spatio-temporal structure is encoded using first-order logic using an approach based on Allen's Interval Logic, and robustness to low-level observation uncertainty is provided by Markov Logic Networks (MLN). Our main contribution is that we integrate interval-based temporal reasoning with probabilistic logical inference, relying on an efficient bottom-up grounding scheme to avoid combinatorial explosion. Applied to one-on-one basketball, our framework detects and tracks players, their hands and feet, and the ball, generates event observations from the resulting trajectories, and performs probabilistic logical inference to determine the most consistent sequence of events. We demonstrate our approach on 1hr (100,000 frames) of outdoor videos.

Incremental cost consensus algorithm in a smart grid environment

Abstract: In a next generation power system, effective distributed control algorithms could be embedded in distributed controllers to properly allocate electrical power among connected buses autonomously. In this paper, we present a novel approach to solve the economic dispatch problem. By selecting the incremental cost of each generation unit as the consensus variable, the algorithm is able to solve the conventional centralized control problem in a distributed manner. The row-stochastic matrices have been used to indicate the different topologies of distribution systems and their configuration properties, such as convergence speeds. The simulation results of several case studies are provided to verify the algorithm.

Constructing consensus controllers for networks with identical general linear agents

Abstract: We use a high-gain methodology to construct linear decentralized controllers for consensus, in networks with identical but general multi-input linear time-invariant (LTI) agents and quitegeneral time-invariant and time-varying observation topologies.

Architecture for Building Conversational Agents that Support Collaborative Learning

Abstract: Tutorial Dialog Systems that employ Conversational Agents (CAs) to deliver instructional content to learners in one-on-one tutoring settings have been shown to be effective in multiple learning domains by multiple research groups. Our work focuses on extending this successful learning technology to collaborative learning settings involving two or more learners interacting with one or more agents. Experience from extending existing techniques for developing conversational agents into multiple-learner settings highlights two underlying assumptions from the one-learner setting that do not generalize well to the multiuser setting, and thus cause difficulties. These assumptions include what we refer to as the near-even participation assumption and the known addressee assumption. A new software architecture called Basilica that allows us to address and overcome these limitations is a major contribution of this article. The Basilica architecture adopts an object-oriented approach to represent agents as a network composed of what we refer to as behavioral components because they enable the agents to engage in rich conversational behaviors. Additionally, we describe three specific conversational agents built using Basilica in order to illustrate the desirable properties of this new architecture.

DistRM: distributed resource management for on-chip many-core systems

Abstract: The trend towards many-core systems comes with various issues, among them their highly dynamic and non-predictable workloads. Hence, new paradigms for managing resources of many-core systems are of paramount importance. The problem of resource management, e.g. mapping applications to processor cores, is NP-hard though, requiring heuristics especially when performed online. In this paper, we therefore present a novel resource-management scheme that supports so-called malleable applications. These applications can adopt their level of parallelism to the assigned resources. By design, our (decentralized) scheme is scalable and it copes with the computational complexity by focusing on local decision-making. Our simulations show that the quality of the mapping decisions of our approach is able to stay near the mapping quality of state-of-the-art (i.e. centralized) online schemes for malleable applications but at a reduced overall communication overhead (only about 12,75% on a 1024 core system with a total workload of 32 multi-threaded applications). In addition, our approach is scalable as opposed to a centralized scheme and therefore it is practically useful for employment in large many-core systems as our extensive studies and experiments show.

A Multifaceted Approach to Modeling Agent Trust for Effective Communication in the Application of Mobile Ad Hoc Vehicular Networks

Abstract: An increasingly large number of cars are being equipped with global positioning system and Wi-Fi devices, enabling vehicle-to-vehicle (V2V) communication with the goal of providing increased passenger and road safety. This technology actuates the need for agents that assist users by intelligently processing the received information. Some of these agents might become self-interested and try to maximize car owners' utility by sending out false information. Given the dire consequences of acting on false information in this context, there is a serious need to establish trust among agents. The main goal of this paper is then to develop a framework that models the trustworthiness of the agents of other vehicles, in order to receive the most effective information. We develop a multifaceted trust modeling approach that incorporates role-, experience-, priority-, and majority-based trust and this is able to restrict the number of reports that are received. We include an algorithm that proposes how to integrate these various dimensions of trust, along with experimentation to validate the benefit of our approach, emphasizing the importance of each of the different facets that are included. The result is an important methodology to enable effective V2V communication via intelligent agents.

JADE implemented mobile multi-agent based, distributed information platform for pervasive health care monitoring

Abstract: In this paper we aim to implement a highly distributed information infrastructure-MADIP by using Intelligent Agent paradigm, which is able to notify the responsible care-provider of abnormality automatically, offer distance medical advice, and perform continuous health monitoring for those who need it. To confront the issues of interoperability, scalability, and openness in heterogeneous e-health environments, a FIPA2000 standard compliant agent development platform-JADE (Java Agent DEvelopment Framework) was adopted for the design and implementation of the proposed intelligent multi-agent based MADIP system.