Type Of Multi-agent System Research Articles

Consensus of Fixed and Adaptive Coupled Multi-Agent Systems with Communication Delays

This paper aims to solve the consensus problem of three different types of multi-agent systems under fixed communication delay. For these three different types of multi-agent systems, three new control protocols are constructed to solve the consensus issue of multi-agent systems with coupling weights, which is rarely considered in other related articles. For fixed coupled multi-agent systems, a new control strategy is designed by Lyapunov theory, matrix theory, and the inequality method to ensure the consensus of multi-agent systems. An adaptive coupling weight updating scheme is proposed for adaptive coupling multi-agent systems to achieve the consensus state of the multi-agent systems. Finally, experimental results show the effectiveness of the proposed three different algorithms.

A generalized proportional Caputo fractional model of multi-agent linear dynamic systems via impulsive control protocol

This paper deals with multi-agent systems that, due to using the generalized proportional Caputo fractional derivative, possess memories. The information exchange between agents does not occur continuously but only at fixed given update times, and the lower limit of the fractional derivative changes according to the update times. Two types of multi-agent systems are studied, namely systems without a leader and systems with a leader. For a generalized proportional Caputo fractional model of multi-agent linear dynamic systems, sufficient conditions for exponential stability via impulsive control are obtained. In the case of the presence of a leader in the multi-agent system, we derive sufficient conditions for the leader-following consensus via impulsive control based on the leader’s influence. Simulation results are provided to verify the essential role of the generalized proportional Caputo fractional derivative and impulsive control in realizing the consensus of multi-agent systems.

Modeling Collective Behaviors: A Moment-Based Approach

In this work we introduce an approach for modeling and analyzing collective behavior of a group of agents using moments. We represent the group of agents via their distribution and derive a method to estimate the dynamics of the moments. We use this to predict the evolution of the distribution of agents by first computing the moment trajectories and then use this to reconstruct the distribution of the agents. In the latter an inverse problem is solved in order to reconstruct a nominal distribution and to recover the macro-scale properties of the group of agents. The proposed method is applicable for several types of multi-agent systems, e.g., leader-follower systems. We derive error bounds for the moment trajectories and describe how to take these error bounds into account for computing the moment dynamics. The convergence of the moment dynamics is also analyzed for cases with monomial moments. To illustrate the theory, two numerical examples are given. In the first we consider a multi-agent system with interactions and compare the proposed methods for several types of moments. In the second example we apply the framework to a leader-follower problem for modeling pedestrian crowd dynamics.

Fuzzy Adaptive Leader-Following Consensus Control for Nonlinear Multi-Agent Systems with Unknown Control Directions

Two new fuzzy adaptive leader-following consensus control algorithms are proposed for nonlinear multi-agent systems with unknown identical and nonidentical control directions. In this paper, agents are modeled as first-order nonlinear systems with uncertain dynamics and unknown control directions. Two types of multi-agent systems are considered: i.e. all the agents have the same control direction and different agents have different control directions. The objective of this paper is to design distributed controllers for follower agents, such that all the follower agents can keep consensus with the leader even if only a fraction of followers can receive the information from the leader. The fuzzy neural network (FNN) is constructed to approximate the unknown dynamics by its powerful learning performance and exemption from prior knowledge of the systems. Finally, the numerical examples are provided to illustrate the effectiveness of the designed controller.

Leader-following consensus for fractional multi-agent systems

A leader-following consensus for Caputo fractional multi-agent systems with nonlinear intrinsic dynamics is investigated. The second Lyapunov method is used to design a control protocol ensuring a consensus for two types of multi-agent systems. Contrary to the previous studies on leader-following consensus, the investigation covers systems with bounded and unbounded time-dependent Lipschitz coefficients in the intrinsic dynamics. Moreover, coupling strength describing the interactions between agents is considered to be a function of time.

Sliding Mode Control for a Class of Nonlinear Multi-agent System With Time Delay and Uncertainties

In this paper, a type of multi-agent system is adopted in the practical project with time delay, uncertainties, and linear feedback. In addition, sliding mode control is used to ensure the robust stability of the system since it is insensitive to parameter change and interference. For the system in three different conditions, namely fixed structure, every agent being only influenced by single time delay and each agent being affected by multiple time delay, the corresponding sliding surface and the control law are improved. The reaction factors are proven by Lyapunov functions and the linear matrix inequality approach is taken to guarantee the robust stability of the sliding surface. To prove the effectiveness of the conclusion, experiments on each condition are conducted. Besides, in the last part, a simple application is applied and proved to be effective.

Возможности создания многоагентных систем в сфере услуг

The paper seeks to examine opportunities to create multi-agent systems (MAS) in the service sector. Using methods of theoretical analysis and synthesis, the author attempts to apply a multi-agent technology to description of the socio-economic system, such as the service sector. As a result, the author identifies three types of MAS in the service sector based on different types of architecture of intelligent information systems. The research shows that the problem posed by the author requires further study and clarification of results

Computation of the target state and feedback controls for time optimal consensus in multi-agent systems

ABSTRACTN identical agents with bounded inputs aim to reach a common target state (consensus) in the minimum possible time. Algorithms for computing this time-optimal consensus point, the control law to be used by each agent and the time taken for the consensus to occur, are proposed. Two types of multi-agent systems are considered, namely (1) coupled single-integrator agents on a plane and, (2) double-integrator agents on a line. At the initial time instant, each agent is assumed to have access to the state information of all the other agents. An algorithm, using convexity of attainable sets and Helly's theorem, is proposed, to compute the final consensus target state and the minimum time to achieve this consensus. Further, parts of the computation are parallelised amongst the agents such that each agent has to perform computations of O(N2) run time complexity. Finally, local feedback time-optimal control laws are synthesised to drive each agent to the target point in minimum time. During this part of the operation, the controller for each agent uses measurements of only its own states and does not need to communicate with any neighbouring agents.

Exploiting Anonymity in Approximate Linear Programming: Scaling to Large Multiagent MDPs

Many solution methods for Markov Decision Processes (MDPs) exploit structure in the problem and are based on value function factorization. Especially multiagent settings, however, are known to suffer from an exponential increase in value component sizes as interactions become denser, restricting problem sizes and types that can be handled. We present an approach to mitigate this limitation for certain types of multiagent systems, exploiting a property that can be thought of as "anonymous influence" in the factored MDP. We show how representational benefits from anonymity translate into computational efficiencies, both for variable elimination in a factor graph and for the approximate linear programming solution to factored MDPs. Our methods scale to factored MDPs that were previously unsolvable, such as the control of a stochastic disease process over densely connected graphs with 50 nodes and 25 agents.

Recent Results in Fractional-Order Modeling in Multi-Agent Systems and Linear Friction Welding

This abstract outlines recent results in two new applications for fractional-order modeling of engineering systems. First, fractional-order dynamics present in two types of multiagent systems are discussed; namely, a specific tree graph structure for a robotic formation control problem and fractional-order dynamics of a scale-free network of agents. Second, fractional-order dynamics present in modeling of the linear friction welding process are discussed.

Path planning for first responders in the presence of moving obstacles

Path planning for first responders in the presence of moving obstacles

Path planning for first responders in the presence of moving obstacles

Path planning for first responders in the presence of moving obstacles

Path planning for first responders in the presence of moving obstacles

Path planning for first responders in the presence of moving obstacles

Formation control of impulsive networked autonomous underwater vehicles under fixed and switching topologies

Abstract In this paper, a novel impulsive networked scheme is investigated to solve the formation control problem of a multi-autonomous underwater vehicle (multi-AUV) system. More specifically, instead of requiring continuous-time communication, in this scheme, information exchanges among the AUVs are conducted via impulse time sequences, which is more practical in the unreliable and narrow banded underwater acoustic communication environment. In particular, the proposed scheme has significant advantages in energy conservation and improved robustness and can be widely applied in many other types of multi-agent systems. Sufficient conditions are derived to guarantee that the desired formation of the multiple AUVs can be achieved under either fixed or switching communication topologies. Finally, two numerical examples are provided to demonstrate the effectiveness of the theoretical results.

A Rough Sets-based Agent Trust Management Framework

In a virtual society, which consists of several autonomous agents, trust helps agents to deal with the openness of the system by identifying the best agents capable of performing a specific task, or achieving a special goal. In this paper, we introduce ROSTAM, a new approach for agent trust management based on the theory of Rough Sets. ROSTAM is a generic trust management framework that can be applied to any types of multi agent systems. However, the features of the application domain must be provided to ROSTAM. These features form the trust attributes. By collecting the values for these attributes, ROSTAM is able to generate a set of trust rules by employing the theory of Rough Sets. ROSTAM then uses the trust rules to extract the set of the most trusted agents and forwards the user's request to those agents only. After getting the results, the user must rate the interaction with each trusted agent. The rating values are subsequently utilized for updating the trust rules. We applied ROSTAM to the domain of cross-language Web search. The resulting Web search system recommends to the user the set of the most trusted pairs of translator and search engine in terms of the pairs that return the results with the highest precision of retrieval.

An Analysis Architecture for Communications in Multi-agent Systems

Evaluation tools are significant from the Agent Oriented Software Engineering (AOSE) point of view. Defective designs of communications in Multi-agent Systems (MAS) may overload one or several agents, causing a bullying effect on them. Bullying communications have avoidable consequences, as high response times and low quality of service (QoS). Architectures that perform evaluation functionality must include features to measure the bullying activity and QoS, but it is also recommendable that they have reusability and scalability features. Evaluation tools with these features can be applied to a wide range of MAS, while minimizing designer�s effort. This work describes the design of an architecture for communication analysis, and its evolution to a modular version, that can be applied to different types of MAS. Experimentation of both versions shows differences between its executions.

Dynamic assignment of roles, rights and responsibilities in normative multi-agent systems

Open multiagent systems (MAS) typically require the participating agents to comply with system-level rules, or norms, and may punish non-compliance. An interesting challenge for designers of such systems is how to provide for dynamic, run-time allocation of norms. In this article, we present a novel, framework-independent approach enabling a norm architecture to be overlaid on any type of multiagent system. We achieve this by defining rights, responsibilities and sanctions in a manner which allows for their run-time assignment and re-assignment to agents in a normative multiagent system. The overlay architecture we propose requires the system to include a normative knowledge (KB) base containing explicit representation of norms as conditional rules, where the preconditions for instantiation of a norm are various run-time occurrences, such as the execution of some agent action, or the occurrence of some event. Our method has both wide generality and flexibility, allowing at one extreme few or no agent roles, each role having many conditional norms, or allowing, at the other extreme, many roles, each having only a small number of conditional norms, or allowing, indeed, any position between these two extremes.

Formal Specification and Verification of Multi-Agent Systems

Multi-agent systems are increasingly complex, and the problem of their verification and validation is acquiring increasing importance. In this paper we show how a well known and effective verification technique, model checking, can be generalized to deal with multi-agent systems. This paper explores a particular type of multi-agent system, in which each agent is viewed as having the three mental attitudes of belief (B), desire (D), and intention (I). We use a multi-modal branching-time logic BDICTL, with a semantics that is grounded in traditional decision theory and a possible-worlds framework. A preliminary implementation of the approach shows promising results.

A Framework for the Foundation of the Philosophy of Artificial Intelligence

The peculiarity of the relationship between philosophy and Artificial Intelligence (AI) has been evidenced since the advent of AI. This paper aims to put the basis of an extended and well founded philosophy of AI: it delineates a multi-layered general framework to which different contributions in the field may be traced back. The core point is to underline how in the same scenario both the role of philosophy on AI and role of AI on philosophy must be considered. Moreover, this framework is revised and extended in the light of the consideration of a type of multiagent system devoted to afford the issue of scientific discovery both from a conceptual and from a practical point of view.

Decision procedures for BDI logics

The study of computational agents capable of rational behaviour has received increasing attention in recent years. A number of theoretical formalizations for such multi-agent systems have been proposed. However, most of these formalizations do not have a strong semantic basis nor a sound and complete axiomatization. Hence, it has not been clear as to how these formalizations could assist in building agents in practice. This paper explores a particular type of multi-agent system, in which each agent is viewed as having the three mental attitudes of belief (B), desire (D), and intention (I). It provides a family of multi-modal branching-time BDI logics with a possible-worlds semantics, categorizes them, provides sound and complete axiomatizations, and gives constructive tableau-based decision procedures for testing the satisfiability and validity of formulas. The computational complexity of these decision procedures is no greater than the complexity of their underlying temporal logic component.