An information agent is viewed as a deductive database consisting of 3 parts: an observation database containing the facts the agent has observed or sensed from its surrounding environment, an input database containing the information the agent has obtained from other agents, and an intensional database which is a set of rules for computing derived information from the information stored in the observation and input databases. Stabilization of a system of information agents represents a capability of the agents to eventually get correct information about their surrounding despite unpredictable environment changes and the incapability of many agents to sense such changes causing them to have temporary incorrect information. We argue that the stabilization of a system of cooperative information agents could be understood as the convergence of the behavior of the whole system toward the behavior of a "superagent", who has the sensing and computing capabilities of all agents combined. We show that unfortunately, stabilization is not guaranteed in general, even if the agents are fully cooperative and do not hide any information from each other. We give sufficient conditions for stabilization and discuss the consequences of our results.
In this paper communications for agents are based on push-technologies. It is interesting to see how the results could be extended to multiagent systems whose communication is based on pull-technologies ( @cite_10 , @cite_9 ).
@cite_9 @cite_10
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Decentralised coordination in multi-agent systems is typically achieved using communication. However, in many cases, communication is expensive to utilise because there is limited bandwidth, it may be dangerous to communicate, or communication may simply be unavailable at times. In this context, we argue for a rational approach to communication --- if it has a cost, the agents should be able to calculate a value of communicating. By doing this, the agents can balance the need to communicate with the cost of doing so. In this research, we present a novel model of rational communication, that uses reward shaping to value communications, and employ this valuation in decentralised POMDP policy generation. In this context, reward shaping is the process by which expectations over joint actions are adjusted based on how coordinated the agent team is. An empirical evaluation of the benefits of this approach is presented in two domains. First, in the context of an idealised bench-mark problem, the multiagent Tiger problem, our method is shown to require significantly less communication (up to 30 fewer messages) and still achieves a 30 performance improvement over the current state of the art. Second, in the context of a larger-scale problem, RoboCupRescue, our method is shown to scale well, and operate without recourse to significant amounts of domain knowledge. Multi-agent systems (MAS) can take many forms depending on the characteristics of the agents populating them. Amongst the more demanding properties with respect to the design and implementation of multi-agent system is how these agents may individually reason and communicate about their knowledge and beliefs, with a view to cooperation and collaboration. In this paper, we present a deductive reasoning multi-agent platform using an extension of answer set programming (ASP). We show that it is capable of dealing with the specification and implementation of the system's architecture, communication and the individual agent's reasoning capacities. Agents are represented as Ordered Choice Logic Programs (OCLP) as a way of modelling their knowledge and reasoning capacities, with communication between the agents regulated by uni-directional channels transporting information based on their answer sets. In the implementation of our system we combine the extensibility of the JADE framework with the flexibility of the OCT front-end to the Smodels answer set solver. The power of this approach is demonstrated by a multi-agent system reasoning about equilibria of extensive games with perfect information. Autonomous agents perform on behalf of the user to achieve defined goals or objectives. They are situated in dynamic environment and are able to operate autonomously to achieve their goals. In a multiagent system, agents cooperate with each other to achieve a common goal. Testing of multiagent systems is a challenging task due to the autonomous and proactive behavior of agents. However, testing is required to build confidence into the working of a multiagent system. Prometheus methodology is a commonly used approach to design multiagents systems. Systematic and thorough testing of each interaction is necessary. This paper proposes a novel approach to testing of multiagent systems based on Prometheus design artifacts. In the proposed approach, different interactions between the agent and actors are considered to test the multiagent system. These interactions include percepts and actions along with messages between the agents which can be modeled in a protocol diagram. The protocol diagram is converted into a protocol graph, on which different coverage criteria are applied to generate test paths that cover interactions between the agents. A prototype tool has been developed to generate test paths from protocol graph according to the specified coverage criterion. Just as POMDPs have been used to reason explicitly about uncertainty in single-agent systems, there has been recent interest in using multi-agent POMDPs to coordinate teams of agents in the presence of uncertainty. Although multi-agent POMDPs are known to be highly intractable, communication at every time step transforms a multi-agent POMDP into a more tractable single-agent POMDP. In this paper, we present an approach that generates "centralized" policies for multi-agent POMDPs at plan-time by assuming the presence of free communication, and at run-time, handles the problem of limited communication resources by reasoning about the use of communication as needed for effective execution. This approach trades off the need to do some computation at execution-time for the ability to generate policies more tractably at plan-time. In our algorithm, each agent, at run-time, models the distribution of possible joint beliefs. Joint actions are selected over this distribution, ensuring that agents remain synchronized. Communication is used to integrate local observations into the team belief only when those observations would improve team performance. We show, both through a detailed example and with experimental results, that our approach allows for effective decentralized execution while avoiding unnecessary instances of communication. Communication is a critical factor for the big multi-agent world to stay organized and productive. Typically, most previous multi-agent "learning-to-communicate" studies try to predefine the communication protocols or use technologies such as tabular reinforcement learning and evolutionary algorithm, which can not generalize to changing environment or large collection of agents. In this paper, we propose an Actor-Coordinator-Critic Net (ACCNet) framework for solving "learning-to-communicate" problem. The ACCNet naturally combines the powerful actor-critic reinforcement learning technology with deep learning technology. It can efficiently learn the communication protocols even from scratch under partially observable environment. We demonstrate that the ACCNet can achieve better results than several baselines under both continuous and discrete action space environments. We also analyse the learned protocols and discuss some design considerations. With the ever growing usage of the world wide IT networks, agent technologies and multiagent systems (MAS) are attracting more and more attention. Multiagent systems are designed to be open systems, wheras agent technologies aim at the design of agents that perform well in environments that are not necessarily well-structured and benevolent. Emergent system behaviour is one of the most interesting phenomena one can investigate in MAS. However, there is more to MAS design than the interaction between a number of agents. For an effective system behaviour we need structure and organisation. This paper presents basic concepts of a theory for holonic multiagent systems with the aim to define the building blocks of a theory that can explain organisation and dynamic reorganisation in MAS. In doing so it tries to bridge the well-known micro-macro gap in MAS theories. The basic concepts are illustrated with three application scenarios: flexible manufacturing, order dispatching in haulage companies, and train coupling and sharing. The paper studies asynchronous consensus problems of continuous-time multi-agent systems with discontinuous information transmission. The proposed consensus control strategy is implemented based on the state information of each agent's neighbors at some discrete times. The asynchrony means that each agent's update times, at which the agent adjusts its dynamics, are independent of others'. Furthermore, it is assumed that the communication topology among agents is time-dependent and the information transmission is with bounded time-varying delays. If the union of the communication topology across any time interval with some given length contains a spanning tree, the consensus problem is shown to be solvable. The analysis tool developed in this paper is based on nonnegative matrix theory and graph theory. The main contribution of this paper is to provide a valid distributed consensus algorithm that overcomes the difficulties caused by unreliable communication channels, such as intermittent information transmission, switching communication topology, and time-varying communication delays, and therefore has its obvious practical applications. Simulation examples are provided to demonstrate the effectiveness of the theoretical results. This paper considers the basic PULL model of communication, in which in each round, each agent extracts information from few randomly chosen agents. We seek to identify the smallest amount of information revealed in each interaction (message size) that nevertheless allows for efficient and robust computations of fundamental information dissemination tasks. We focus on the Majority Bit Dissemination problem that considers a population of n agents, with a designated subset of source agents. Each source agent holds an input bit and each agent holds an output bit. The goal is to let all agents converge their output bits on the most frequent input bit of the sources (the majority bit). Note that the particular case of a single source agent corresponds to the classical problem of Broadcast (also termed Rumor Spreading). We concentrate on the severe fault-tolerant context of self-stabilization, in which a correct configuration must be reached eventually, despite all agents starting the execution with arbitrary initial states. In particular, the specification of who is a source and what is its initial input bit may be set by an adversary. We first design a general compiler which can essentially transform any self-stabilizing algorithm with a certain property (called "the bitwise-independence property") that uses l-bits messages to one that uses only log l-bits messages, while paying only a small penalty in the running time. By applying this compiler recursively we then obtain a self-stabilizing Clock Synchronization protocol, in which agents synchronize their clocks modulo some given integer T, within O(log n log T) rounds w.h.p., and using messages that contain 3 bits only. We then employ the new Clock Synchronization tool to obtain a self-stabilizing Majority Bit Dissemination protocol which converges in O(log n) time, w.h.p., on every initial configuration, provided that the ratio of sources supporting the minority opinion is bounded away from half. Moreover, this protocol also uses only 3 bits per interaction. Abstract In multi-agent systems, the study of language and communication is an active field of research. In this paper we present the application of Reinforcement Learning (RL) to the self-emergence of a common lexicon in robot teams. By modeling the vocabulary or lexicon of each agent as an association matrix or look-up table that maps the meanings (i.e. the objects encountered by the robots or the states of the environment itself) into symbols or signals we check whether it is possible for the robot team to converge in an autonomous, decentralized way to a common lexicon by means of RL, so that the communication efficiency of the entire robot team is optimal. We have conducted several experiments aimed at testing whether it is possible to converge with RL to an optimal Saussurean Communication System. We have organized our experiments alongside two main lines: first, we have investigated the effect of the team size centered on teams of moderated size in the order of 5 and 10 individuals, typical of multi-robot systems. Second, and foremost, we have also investigated the effect of the lexicon size on the convergence results. To analyze the convergence of the robot team we have defined the team’s consensus when all the robots (i.e. 100 of the population) share the same association matrix or lexicon. As a general conclusion we have shown that RL allows the convergence to lexicon consensus in a population of autonomous agents. In multi-agent system, we often face incompleteness of information due to communication failure or other agent's suspension of decisions. To solve the incompleteness, we previously proposed speculative computation using abduction in the context of matter-slave multi-agent systems and gave a procedure in abductive logic programming [14]. In the work, a master agent prepares a default value for a question in advance and it performs speculative computation using the default without waiting for a reply for the question. This computation is effective unless the contradictory reply with the default is returned. However, we find that this mechanism is not sufficient for speculative computation in more general multi-agent systems such that replies can be revised according to other agents' speculative computation. In this paper, we formalize speculative computation with multi-agent belief revision and propose a correct procedure for such computation. In conversational agents with multiparty communication functionality, a system needs to be able to identify the addressee for the current floor and respond to the user when the utterance is addressed to the agent. This study proposes some addressee identification models based on speech and gaze information, and tests whether the models can be applied to different proxemics. We build an addressee identification mechanism by implementing the models and incorporate it into a fully autonomous multiparty conversational agent. The system identifies the addressee from online multimodal data and uses this information in language understanding and dialogue management. Finally, an evaluation experiment shows that the proposed addressee identification mechanism works well in a real-time system, with an F-measure for addressee estimation of 0.8 for agent-addressed utterances. We also found that our system more successfully avoided disturbing the conversation by mistakenly taking a turn when the agent is not addressed. We are interested in principles for designing and building open distributed systems consisting of multiple cyber-physical agents, specifically, where a coherent global view is unattainable and timely consensus is impossible. Such agents attempt to contribute to a system goal by making local decisions to sense and effect their environment based on local information. In this paper we propose a model, formalized in the Maude rewriting logic system, that allows experimenting with and reasoning about designs of such systems. Features of the model include communication via sharing of partially ordered knowledge, making explicit the physical state as well as the cyber perception of this state, and the use of a notion of soft constraints developed by Martin Wirsing and his team to specify agent behavior. The paper begins with a discussion of desiderata for such models and concludes with a small case study to illustrate the use of the modeling framework. Abstract Multi Agent Systems (MAS) have become the key technology for decomposing complex problems in order to solve them more efficiently, or for problems distributed in nature. However, many industrial applications besides their distributed nature, also involve a large number of parameters and constraints among them, i.e. they are combinatorial. Solving such particularly hard problems efficiently requires programming tools that combine MAS technology with a programming schema that facilitates the modeling and solution of constraints. This paper presents MACLP (Multi Agent Constraint Logic Programming), a logic-programming platform for building, in a declarative way, multi agent systems with constraint-solving capabilities. MACLP extends CSPCONS, a logic programming system that permits distributed program execution through communicating sequential Prolog processes with constraints, by providing all the necessary facilities for communication between agents. These facilities abstract from the programmer all the low-level details of the communication and allow him to focus on the development of the agent itself. This paper is focused on the formation control of multi-agent systems (MAS). A new control law based on event-driven strategies is presented. The agents with any initial position can reach the desired formation and target under such control law. Formability of MAS depends on several key factors: the agents' dynamic structures, the connectivity topology, the properties of the desired formation and the admissible control set. The control actuation updates considered in this paper are event-driven, depending on the ratio of a certain measurement error with respect to the norm of a function of the state. Mobile agent technology is a promising paradigm for a myriad of real world applications. Owing to their tremendous capabilities, multiagent systems have been scoped in a large number of applications. However issues related to fault tolerance can hamper the suitability of mobile agents in these real world systems. In this paper we have proposed an infrastructure which provides agent fault tolerance. An algorithm similar to the sliding window model ensures a fault tolerant behavior. Different types of agents, work in collaboration to provide the desired system behavior by tolerating faults. The proposed infrastructure will be applicable in a variety of systems including, ecommerce, online banking etc. With the increasing market of electronic commerce it becomes an interesting aspect to use autonomous mobile agents for electronic business transactions. In this paper, we study the problem of reaching a consensus among all the agents in the networked control systems (NCS) in the presence of misbehaving agents. A reputation-based resilient distributed control algorithm is first proposed for the leader-follower consensus network. The proposed algorithm embeds a resilience mechanism that includes four phases (detection, mitigation, identification, and update), into the control process in a distributed manner. At each phase, every agent only uses local and one-hop neighbors' information to identify and isolate the misbehaving agents, and even compensate their effect on the system. We then extend the proposed algorithm to the leaderless consensus network by introducing and adding two recovery schemes (rollback and excitation recovery) into the current framework to guarantee the accurate convergence of the well-behaving agents in NCS. The effectiveness of the proposed method is demonstrated through case studies in multirobot formation control and wireless sensor networks. The paper presents a knowledge representation formalism, in the form of a high-level Action Description Language (ADL) for multi-agent systems, where autonomous agents reason and act in a shared environment. Agents are autonomously pursuing individual goals, but are capable of interacting through a shared knowledge repository. In their interactions through shared portions of the world, the agents deal with problems of synchronization and concurrency; the action language allows the description of strategies to ensure a consistent global execution of the agents’ autonomously derived plans. A distributed planning problem is formalized by providing the declarative specications of the portion of the problem pertaining a single agent. Each of these specications is executable by a stand-alone CLP-based planner. The coordination among agents exploits a Linda infrastructure. The proposal is validated in a prototype implementation developed in SICStus Prolog. To appear in Theory and Practice of Logic Programming (TPLP). Research partially funded by GNCS-INdAM projects, MUR-PRIN: Innovative and multidisciplinary approaches for constraint and preference reasoning project; NSF grants IIS-0812267 and HRD-0420407; and grants 2009.010.0336 and 2010.011.0403. Choosing when to communicate is a fundamental problem in multi-agent systems. This problem becomes particularly challenging when communication is constrained and each agent has different partial information about the overall situation. We take a decision-theoretic approach to this problem that balances the benefits of communication against the costs. Although computing the exact value of communication is intractable, it can be estimated using a standard myopic assumption—that communication is only possible at the present time. We examine specific situations in which this assumption leads to poor performance and demonstrate an alternative approach that relaxes the assumption and improves performance. The results provide an effective method for value-driven communication policies in multi-agent systems. This paper introduces the SHOMAS Multiagent System that provides guidance on leisure facilities and suggestions for shopping in malls. The multiagent architecture incorporates reactive and deliberative agents that take decisions automatically. The developed deliberative agent provides suggestions in execution time, with the help of case-based planners. This agent is described together with its guidance and suggestion mechanism. SHOMAS has been tested successfully, and the results obtained are presented in this paper. In this paper a novel approach to the problem of decentralized agreement toward a common point in space in a multi-agent system is proposed. Our method allows the agents to agree on the relative location of the network centroid respect to themselves, on a common reference frame and therefore on a common heading. Using this information a global positioning system for the agents using only local measurements can be achieved. In the proposed scenario, an agent is able to sense the distance between itself and its neighbors and the direction in which it sees its neighbors with respect to its local reference frame. Furthermore only point-to-point asynchronous communications between neighboring agents are allowed thus achieving robustness against random communication failures. The proposed algorithms can be thought as general tools to locally retrieve global information usually not available to the agents.