Open Multi-agent Systems Research Articles

Random Coordinate Descent for Resource Allocation in Open Multiagent Systems

Random Coordinate Descent for Resource Allocation in Open Multiagent Systems

Counter abstraction for regular open teams

We study open swarms and teams of multi-agent systems where agents may join or leave the system at runtime. Kouvaros et al. [1] defined the verification problem for such systems and showed it to be undecidable, in general. Also, they have found one decidable class of open multi-agent systems and provided a partial decision procedure for another. In the same vein we present a subclass of open teams called regular open teams for which reachability is decidable. This is shown by employing a counter abstraction technique wherein the regular open team is abstracted into a finite state multi-counter system.

Prescribed-time consensus of time-varying open multi-agent systems with delays on time scales

In this study, we focus on prescribed-time leader-following consensus problems for a class of time-varying open multi-agent systems (OMASs) with time delays on time scales. To achieve this objective, we propose a novel segmented state feedback control protocol which contains a time-varying scalar function. It ensures prescribed-time consensus can be achieved while the monotonicity of Lyapunov function is unknown. On this basis, impulsive signals dependent on the leader are further introduced at each opening instant to avoid the possibility of the controller norm being too large. During the process of theoretical analysis, we further innovatively extend Halanay-like inequalities on time scales to resolve theoretical analysis difficulties caused by time delays. Based on time scale theory and Lyapunov stability theory, sufficient conditions depending on system parameters and controller parameters for achieving prescribed-time consensus can be derived. Besides, considering absolute information of each agent cannot be completely obtained, we further design one observer system to reconstruct information of the original system, guaranteeing it can still reach consensus within the pre-set time. Finally, two simulation examples are used to illustrate the validity of our proposed theory.

FE4NorOMAS: A Distributed Fuzzy Enforcement Approach for Normative Open Multi-Agent Systems

FE4NorOMAS: A Distributed Fuzzy Enforcement Approach for Normative Open Multi-Agent Systems

Formation tracking control in open multiagent systems with general linear dynamics

This paper studies the formation tracking problem of the so-called open multiagent systems. In these systems, agents can freely join or leave at any time, i.e. the system sizes are changeable. To achieve formation tracking control in these systems, we propose a general theoretical analysis framework that consists of a formation adjustment strategy and a formation tracking protocol. Therein, the former is used to adjust the predefined task-oriented formation shape to the varying size of the system. This strategy is built on the existing scalable self-healing idea and then modified by a size estimation method. The latter is used to drive the present followers to form the adjusted formation and track a leader. To accommodate undirected and directed graphs, two distributed formation tracking protocols are designed respectively. Due to possible agent migration, the designed tracking protocols are endowed with self-adaptive ability, i.e. without depending on global topology information. Then, their convergence properties are analysed using the Lyapunov theory. Finally, two simulations are presented to verify the effectiveness of the proposed framework.

Towards a Docker-based architecture for open multi-agent systems

<p>In open multi-agent systems (OMAS), heterogeneous agents in different environments or models can migrate from one system to another, taking their attributes and knowledge and increasing developing complexity compared to conventional multi-agent systems (MAS). Furthermore, the complexity of opening may be due to the uncertainties and dynamic behavior that the change of agents entails, needing to formulate techniques to analyze this complexity and understand the system’s global behavior. We used Docker to approach these problems and make the architecture flexible to handle distinct types of programming languages and frameworks of agents. This paper presents a Docker-based architecture to aid OMAS development, acting on agent migration between different models running in heterogeneous hardware and software scenarios. We present a simulation scenario with NetLogo’s Open Sugarscape 2 Constant Growback and JaCaMo’s Gold Miners to verify the proposal’s feasibility.</p>

A Sum-of-States Preservation Framework for Open Multiagent Systems With Nonlinear Heterogeneous Coupling

A Sum-of-States Preservation Framework for Open Multiagent Systems With Nonlinear Heterogeneous Coupling

Gamekeeper: Online Learning for Admission Control of Networked Open Multiagent Systems

Gamekeeper: Online Learning for Admission Control of Networked Open Multiagent Systems

Engineering IoT-Based Open MAS for Large-Scale V2G/G2V

In this paper, we aimed to demonstrate how to engineer Internet of Things (IoT)-based open multiagent systems (MASs). Specifically, we put forward an IoT/MAS architectural framework, along with a case study within the important and challenging-to-engineer vehicle-to-grid (V2G) and grid-to-vehicle (G2V) energy transfer problem domain. The proposed solution addresses the important non-functional requirement of scalability. To this end, we employed an open multiagent systems architecture, arranging agents as modular microservices that were interconnected via a multi-protocol Internet of Things platform. Our approach allows agents to view, offer, interconnect, and re-use their various strategies, mechanisms, or other algorithms as modular smart grid services, thus enabling their seamless integration into our MAS architecture, and enabling the solution of the challenging V2G/G2V problem. At the same time, our IoT-based implementation offers both direct applicability in real-world settings and advanced analytics capabilities via enabling digital twin models for smart grid ecosystems. We have described our MAS/IoT-based architecture in detail; validated its applicability via simulation experiments involving large numbers of heterogeneous agents, operating and interacting towards effective V2G/G2V; and studied the performance of various electric vehicle charging scheduling and V2G/G2V-incentivising electricity pricing algorithms. To engineer our solution, we used ASEME, a state-of-the-art methodology for multiagent systems using the Internet of Things. Our solution can be employed for the implementation of real-world prototypes to deliver large-scale V2G/G2V services, as well as for the testing of various schemes in simulation mode.

Fundamental Performance Limitations for Average Consensus in Open Multi-Agent Systems

We derive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fundamental performance limitations</i> for intrinsic average consensus problems in open multi-agent systems, which are systems subject to frequent arrivals and departures of agents. Each agent holds a value, and the objective of the agents is to collaboratively estimate the average of the values of the agents presently in the system. Algorithms solving such problems in open systems are poised to never converge because of the permanent variations in the composition, size, and objective pursued by the agents of the system. We provide lower bounds on the expected mean squared error achievable by any averaging algorithms in open systems of fixed size. Our derivation is based on the analysis of a conceptual algorithm that would achieve optimal performance for a given model of replacements. We obtain a general bound that depends on the properties of the model defining the interactions between the agents, and instantiate that result for all-to-one and one-to-one interaction models. A comparison between those bounds and algorithms implementable with those models is then provided to highlight their validity.

Cooperative Learning for Adversarial Multi-Armed Bandit on Open Multi-Agent Systems

This paper considers a cooperative decision-making method for an adversarial bandit problem on open multi-agent systems. In an open multi-agent system, the network configuration changes dynamically as agents freely enter and leave the network. We propose a distributed Exp3 policy in which a group of agents exchanges the estimation of the expected reward of each arm with active neighboring agents. Then, each agent updates the probability distribution of choosing arms by combining the estimated rewards of neighboring agents. We derive a sufficient condition for a sublinear bound of a pseudo regret. The numerical example shows that active agents can cooperatively find the optimal arm by the proposed Exp3 policy algorithm.

Interoperable AI: Evolutionary Race Toward Sustainable Knowledge Sharing

The advancement and deployment of artificial intelligent agents brought numerous benefits in knowledge and data gathering and processing. However, one of the key challenges in deploying such agents in an open environment like the Web is their interoperability as they currently mostly run in silos. In this article, we report on a simulation and evaluation based on evolutionary agent-based modeling to empirically test how sustainable different strategies are for knowledge sharing in open multiagent systems. Our results show the importance of translation-based approaches and the need for incentives to support these.

Stability of multi-dimensional switched systems with an application to open multi-agent systems

A multi-dimensional switched system or multi-mode multi-dimensional (M3D) system extends the classic switched system by allowing different subsystem dimensions. The stability problem of the M3D system, whose state transitions at switching instants can be discontinuous due to the dimension-varying feature, is studied. The discontinuous state transition is formulated by an affine map that captures both the dimension variations and the state impulses, with no extra constraint imposed. In the presence of unstable subsystems, the general criteria featuring a series of Lyapunov-like conditions for the practical and asymptotic stability properties of the M3D system are provided under the proposed slow/fast transition-dependent average dwell time framework. Then, by considering linear subsystems, we propose a class of parametric multiple Lyapunov functions to verify the obtained Lyapunov-like stability conditions and explicitly reveal a connection between the practical/asymptotic stability property and the non-vanishing/vanishing property of the impulsive effects in the state transition process. Further, the obtained stability results for the M3D system are applied to the consensus problem of the open multi-agent system (MAS), whose network topology can be switching and size-varying due to the migrations of agents. It shows that through a proper transformation, the seeking of the (practical) consensus performance of the open MAS with disconnected digraphs boils down to that of the (practical) stability property of an M3D system with unstable subsystems.

Ensuring trustworthy and ethical behaviour in intelligent logical agents

Abstract Autonomous intelligent agents are employed in many applications upon which the life and welfare of living beings and vital social functions may depend. Therefore, agents should be trustworthy. A priori certification techniques (i.e. techniques applied prior to system’s deployment) can be useful, but are not sufficient for agents that evolve, and thus modify their epistemic and belief state, and for open multi-agent systems, where heterogeneous agents can join or leave the system at any stage of its operation. In this paper, we propose/refine/extend dynamic (runtime) logic-based self-checking techniques, devised in order to be able to ensure agents’ trustworthy and ethical behaviour.

Distributed Mode Computation in Open Multi-Agent Systems

Allowing Multi-Agent Systems (MAS) to compute the mode of the agents initial values (i.e., the value with largest cardinality) represents a highly valuable building block for the development of complex decision-making tasks, as it allows agents to identify the central tendency of data or to implement majority voting processes while considering categorical opinions for which average or median values might not be possible to compute. This is especially challenging in the context of Open Multi-Agent Systems (OMAS), where agents are free to join or leave the network, as in this case the outcome of the mode computation process may vary depending on the current participants to the network. In this paper, we propose a novel OMAS mode computation framework where agents select a value from a finite set of alternatives, and compute the mode via the execution in parallel of a novel average-preserving distributed consensus procedure for each of the different alternatives. We complement the paper with simulation results that numerically demonstrate the effectiveness of the proposed approach.

Gradient Descent for Resource Allocation with Packet Loss*

This paper studies the effect of packet loss during the application of the weighted gradient descent to solve a resource allocation problem with piecewise quadratic cost functions in a multi-agent system. We define two performance metrics that measure, respectively, the deviation from the constraint and the error on the expected cost function. We derive upper bounds on both metrics: both bounds are proportional to the difference between the initial cost function and the cost function evaluated at the minimizer. Then, we extend the analysis of the constraint violation to open multi-agent systems where agents are replaced: based on a preliminary result and simulations we show that the combination of replacements and losses makes the constraint violation error diverge with time.

Dynamic max-consensus with local self-tuning

This work describes a novel control protocol for multi-agent systems to solve the dynamic max-consensus problem. In this problem, each agent has access to an external time-varying scalar signal and has the objective to estimate and track the maximum among all these signals by exploiting only local communications. The main strength of the proposed protocol is that it is able to self-tune its internal parameters in order to achieve an arbitrary small steady-state error without significantly affecting the convergence time. We employ the proposed protocol in the context of distributed graph parameter estimations, such as size, diameter, and radius, and provide simulations in the scenario of open multi-agent systems.

Anytime and Efficient Multi-agent Coordination for Disaster Response

The Coalition Formation with Spatial and Temporal constraints Problem (CFSTP) is a multi-agent task allocation problem where the tasks are spatially distributed, with deadlines and workloads, and the number of agents is typically much smaller than the number of tasks. To maximise the number of completed tasks, the agents may have to schedule coalitions. The state-of-the-art CFSTP solver, the Coalition Formation with Look-Ahead (CFLA) algorithm, has two main limitations. First, its time complexity is exponential with the number of agents. Second, as we show, its look-ahead technique is not effective in real-world scenarios, such as open multi-agent systems, where new tasks can appear at any time. In this work, we study its design and define a variant, called Coalition Formation with Improved Look-Ahead (text {CFLA}2), which achieves better performance. Since we cannot eliminate the limitations of CFLA in text {CFLA}2, we also develop a novel algorithm to solve the CFSTP, the first to be simultaneously anytime, efficient and with convergence guarantee, called Cluster-based Task Scheduling (CTS). In tests where the look-ahead technique is highly effective, CTS completes up to 30% (resp. 10%) more tasks than CFLA (resp. text {CFLA}2) while being up to 4 orders of magnitude faster. We also propose S-CTS, a simplified but parallel variant of CTS with even lower time complexity. Using scenarios generated by the RoboCup Rescue Simulation, we show that S-CTS is at most 10% less performing than high-performance algorithms such as Binary Max-Sum and DSA, but up to 2 orders of magnitude faster. Our results affirm CTS as the new state-of-the-art algorithm to solve the CFSTP.

Multi-Agents Collaboration in Open System

Share constrained resources, accomplish complex tasks and achieve shared or individual goals are examples requiring collaboration between agents in multi-agent systems. The collaboration necessitates an effective team composed of a set of agents that do not have conflicting goals and express their willingness to cooperate. In such a team, the complex task is split into simple tasks, and each agent performs its assigned task to contribute to the fulfilment of the complex task. Nevertheless, team formation is challenging, especially in an open system that consists of self-interested agents performing tasks to achieve several simultaneous goals, usually clashing, by sharing constrained resources. The clashing goals obstruct the collaboration's success since the self-interested agent prefers its individual goals to the team’s shared goal. In open systems, the collaboration team construction process is impacted by the Multi-Agent System (MAS) model, the collaboration’s target, and dependencies between agents’ goals. This study investigates how to allow agents to build collaborative teams to realize a set of goals concurrently in open systems with constrained resources. This paper proposes a fully distributed approach to model the Collaborative Team Construction Model (CTCM). CTCM modifies the social reasoning model to allow agents to achieve their individual and shared goals concurrently by sharing resources in an open MAS by constructing collaborative teams. Each agent shares partial information (to preserve privacy) and models its goal relationships. The proposed team construction approach supports a distributed decision-making process. In CTCM, the agent adapts its self-interest level and adjusts its willingness to form an effective collaborative team.

Agent organisations: from independent agents to virtual organisations and societies of agents

Real world applications using agent-based solutions can include many agents that needs communicate and interact each other in order to meet their objectives. In open multi-agent systems, the problems may include the organisation of a large number of agents that may be heterogeneous, of unpredictable provenance and where competitive behaviours or conflicting objectives may occur. An overview of the alternatives for dealing with these problems is presented, highlighting the way they try to solve or mitigate these problems.