Optimal Supervisory Control of Two Phases Intersection Using Hybrid-based Model Approach

A critical issue in patient planning is to determine whether the medical processes of a patient can be completed by a time constraint based on the available resources in hospitals. The problem is a Temporal Constraint Satisfaction Problem (TCSP). The objectives of this paper are to propose a viable and systematic approach to develop a distributed cooperative problem solver for TCSP and estimate the shortest and the longest completion time for handling a patient in the presence of uncertainty based on Multi-agent systems (MAS) architecture. Our approach combines MAS with a subclass of time Petri net (TPN) models to solve TCSP. Existing analysis methods of TPN based on state classes cannot be applied directly due to distributed architecture of MAS. In this paper, a temporal analysis method based on MAS architecture is proposed. Our temporal analysis method efficiently deduces the earliest and latest completion time of a patient based on interaction between agents.

Multiagent systems in which independent software agents interact with each other to achieve common goals, complete distributed tasks concurrently under autonomous control. Agent communication has been shown to be an important factor in coordinating efficient group behavior in agents. Most research on training or evolving group behavior in multiagent systems used predefined agent communication protocols. Designing agent communication becomes a complex problem in dynamic and large-scale systems. In order to solve this problem, in our previous research we proposed a method applying genetic programming techniques, in particular Automatically Defined Function Genetic Programming (ADF-GP) (K. Mackin and E. Tazaki, 1999), to allow agents to autonomously learn effective agent communication messaging. For this research we take this approach further and combine training of the agent behavior as well as the communication protocol. By training both behavior and communication we expect to further optimize the system performance. A software simulation of a multiagent transaction system is used to observe the effectiveness of the proposed method.

Multiagent systems, in which independent software agents interact with each other to achieve common goals, complete concurrent distributed tasks under autonomous control. Agent Communication has been shown to be an important factor in coordinating efficient group behavior in agents. Most researches on training or evolving group behavior in multiagent systems used predefined agent communication protocols. Designing agent communication becomes a complex problem in dynamic and large‐scale systems. In order to solve this problem, in this paper we propose a new application of existing training methods. By applying Genetic Programming techniques, namely Automatically Defined Function Genetic Programming (ADF‐GP), in combination with pheromone communication features, we allowed the agent system to autonomously learn effective agent communication messaging for coordinated group behavior. A software simulation of a multiagent transaction system aiming at e‐commerce usage will be used to observe the effectiveness of the proposed method in the targeted environment. Using the proposed method, automatic training of a compact and efficient agent communication protocol for the multiagent system was observed.

This study proposes a hybrid approach to construct amulti-agent learning system that is applied to RoboCup (Robot world cup tournament) soccer game. RoboCup competitive games involve the complicated system behavior of multiple agents, which makes it a popular research domain in recent years. The goal of RoboCup is to promote AI and robotics, and some researchers even hope that RoboCup robots can eventually defeat human soccer players. A hybrid approach called the Case Based Reasoning-Genetic Algorithm (CBR-GA) was used to provide a better strategy for robots in their planning under all kinds of conditions and to store experiences learned during the game for further use. In addition, the Rule Based Reasoning (RBR) method was integrated to improve the defect of CBR-GA. Using the hybrid approach without pre-defined knowledge and a complicated mathematical basis, robots can learn and accumulate experience to become smarter. The robots not only learn how to score but also figure out how to avoid makin...

The use of norms is widely accepted as an effective approach to control and regulate the behaviour of agents in multi-agent systems. Existing work on normative multi-agent systems has mainly focussed on how norms can influence the behaviour of agents by assuming that the agents' behaviours are perfectly monitored. In this paper we focus on monitoring mechanisms, propose different types of monitors, provide a logical analysis of monitors, study the relations between monitors and norms to be monitored, and finally explore computational aspects of norm monitoring.

Retraction: Modeling and Supervisory Control of Urban Traffic Flows in Medium-sized Cities Using Hybrid Approach

Analysis and control of saddle-node bifurcation based on continuum model with headway fluctuation

Cooperative multi-agent reinforcement learning methods aim to learn effective collaborative behaviours of multiple agents performing complex tasks. However, existing MARL methods are commonly proposed for fairly small-scale multi-agent benchmark problems, wherein both the number of agents and the length of the time horizons are typically restricted. My initial work investigates hierarchical controls of multi-agent systems, where a unified overarching framework coordinates multiple smaller multi-agent subsystems, tackling complex, long-horizon tasks that involve multiple objectives. Addressing another critical need in the field, my research introduces a comprehensive benchmark for evaluating MARL methods in long-horizon, multi-agent, and multi-objective scenarios. This benchmark aims to fill the current gap in the MARL community for assessing methodologies in more complex and realistic scenarios. My dissertation would focus on proposing and evaluating methods for scaling up multi-agent systems in two aspects: structural-wise increasing the number of reinforcement learning agents and temporal-wise extending the planning horizon and complexity of problem domains that agents are deployed in.

Strategies and prediction in multi-agent systems

Chapter 17 - Strategies and Prediction in Multi-Agent Systems

Social and organizational aspects of agency have become a major issue in the Multi-Agent Systems (MAS) domain. Recent applications of MAS on Web and Ambient Computing enforce the need of using these dimensions in the programming of MAS. The aim is to ensure the governance of such systems while preserving their decentralization and openness. In this talk, we present how multi-agent organisations provide software abstractions, models and tools that contribute to this aim. We focus on the MOISE framework that we have developped during these last years. This framework proposes an organisation programming language for defining multi-agent organisations, that are managed and supported by organisational artifacts at the system programming level and by organisation-awareness mechanisms at the agent programming level. This framework is included in the JaCaMo platform, integration of Jason Agent Programming Language, CarTaGo environment platform and MOISE. We illustrate different features of Organisation Oriented Programming of MAS using different application examples. We will highlight also how it integrates in a broader perspective of multi-agent oriented programming of decentralized and open systems.

Q-learning solution for optimal consensus control of discrete-time multiagent systems using reinforcement learning

The need for handling the increasing complexity in software systems has allowed the introduction and establishment of an organisational paradigm as an alternative in software modelling and development. Especially within the multi-agent systems community, organisational concepts are enjoying increasing popularity for efficiently structuring multi-agent behaviour. Organisational specifications and their implementation as multi-agent systems lack however a streamlined transition between each other. In this paper we address this problem by introducing a software tool capable of creating and editing organisation models as well as deploying such models as multi-agent systems. The tool is built on SONAR, a formal organisational specification based on Petri nets. By unifying in one tool the organisational specification and deployment process quick reaction cycles to incremental changes of system design become possible.

The flocking of multiple intelligent agents, inspired by the swarm behavior of natural phenomena, has been widely used in the engineering fields such as in unmanned aerial vehicle (UAV) and robots system. However, the performance of the system (such as response time, network throughput, and resource utilization) may be greatly affected while the intelligent agents are engaged in cooperative work. Therefore, it is concerned to accomplish the distributed cooperation while ensuring the optimal performance of the intelligent system. In this paper, we investigated the optimal control problem of distributed multiagent systems (MASs) with finite-time group flocking movement. Specifically, we propose two optimal group flocking algorithms of MASs with single-integrator model and double-integrator model. Then, we study the group consensus of distributed MASs by using modern control theory and finite-time convergence theory, where the proposed optimal control algorithms can drive MASs to achieve the group convergence in finite-time while minimizing the performance index of the intelligence system. Finally, experimental simulation shows that MASs can keep the minimum energy function under the effect of optimal control algorithm, while the intelligent agents can follow the optimal trajectory to achieve group flocking in finite time.

<div class="htmlview paragraph">Recently, the control of traffic flow has been proposed using several types of criteria (e.g. minimum-time control, minimum fuel control and so on). Most recently, an environmental noise pollution problem caused by the road traffic is being aggravated more and more by the consolidation and expansion of roadway system particularly in urban areas. However, the objective of this paper is to control road traffic flow by regulating traffic noise propagation in an urban area in Cairo city. The results of traffic noise prediction obtained by trending of the experimental data collecting by systematic noise measurement and the evaluation of the traffic noise which is in close connection with physical parameters of traffic flow and noise propagation characteristics is presented. The analysis of road traffic flow noise control is based on the mixed integer non-linear programming technique, where the optimal control strategy is used. The results indicate that significant information gained for controlling road traffic in urban areas.</div>