Multi-agent Decision Support System Research Articles

Integrated optimization of marine oil spill response and liquid oily waste management using mathematical programming and evolutionary metaheuristic techniques

Marine oil spill response is a time sensitive and complex task, in which the management of oily wastewater generated from response operations could be a bottleneck limiting the response capacity and efficiency. This study developed a multi-agent decision support system to effectively coordinate mechanical containment and recovery (MCR) of spilt oil and oily wastewater management (OWM) operations. The system aims to minimize the overall response time, cost, and the volume of weathered oil by applying evolutionary optimization, oil weathering process, and response operational agents. The multi-agent system was used to manage an incident through a hypothetical case study in Canada. The system enables the scheduling of available response assets considering all complex operational details of MCR and OWM, such as available oily wastewater storage and transportation capacities and the competitive skimming process of various response vessels. Implementing the evolutionary multi-objective optimization led to a considerable reduction in response time (about 13.6% or 67 hr), cost (about 1.4% or CA$24,800), and volume of weathered oil (about 9.2% or 19 m3) compared to the shortest distance approach. The developed multi-agent system can facilitate decision-making in complex marine oil spill response tasks for maximizing response efficiency.

Ontologies to Enable Interoperability of Multi-Agent Electricity Markets Simulation and Decision Support

This paper presents the AiD-EM Ontology, which provides a semantic representation of the concepts required to enable the interoperability between multi-agent-based decision support systems, namely AiD-EM, and the market agents that participate in electricity market simulations. Electricity markets’ constant changes, brought about by the increasing necessity for adequate integration of renewable energy sources, make them complex and dynamic environments with very particular characteristics. Several modeling tools directed at the study and decision support in the scope of the restructured wholesale electricity markets have emerged. However, a common limitation is identified: the lack of interoperability between the various systems. This gap makes it impossible to exchange information and knowledge between them, test different market models, enable players from heterogeneous systems to interact in common market environments, and take full advantage of decision support tools. To overcome this gap, this paper presents the AiD-EM Ontology, which includes the necessary concepts related to the AiD-EM multi-agent decision support system, to enable interoperability with easier cooperation and adequate communication between AiD-EM and simulated market agents wishing to take advantage of this decision support tool.

Multi-agent maintenance scheduling based on the coordination between central operator and decentralized producers in an electricity market

Condition-based and predictive maintenance enable early detection of critical system conditions and thereby enable decision makers to forestall faults and mitigate them. However, decision makers also need to take the operational and production needs into consideration for optimal decision-making when scheduling maintenance activities. Particularly in network systems, such as power grids, decisions on the maintenance of single assets can affect the entire network and are, therefore, more complex.This paper proposes a bi-level multi-agent decision support system for the generation maintenance decision (GMS) of power grids in an electricity market in the context of predictive maintenance. The GMS plays an important role in increasing the reliability at the network level. The aim of the GMS is to minimize the generation cost while maximizing the system reliability. The proposed framework integrates a central coordination system, i.e. the transmission system operator (TSO), and distributed agents representing power generation units that act to maximize their profit and decide about the optimal maintenance time slots while ensuring the energy balance. In the proposed bi-level approach, n upper levels and one lower level (i.e. sub-problems) are solved by the independent agents and by the TSO, respectively. We derive the optimal strategy of the agents that are subject to predictive maintenance via a distributed algorithm, through which agents make optimal maintenance decisions and communicate them to the central coordinator, i.e. the TSO. The TSO decides whether to accept the agents’ decisions by considering market reliability aspects and power supply constraints. To solve this coordination problem, we propose a negotiation algorithm using an incentive signal to coordinate the agents’ and the central system’s decisions, such that all the agents’ decisions can be accepted by the central system. We demonstrate the effectiveness of the proposed algorithm with reference to the IEEE 39 bus system.

Fuzzy cognitive mapping to support multi-agent decisions in development of urban policymaking

The awareness about environmental complexity involves real-time knowledge and demands urban planning initiatives. Knowledge is multiform, multi-agent and mirrors environmental complexity. Problems characterizing urban sustainability particularly claim non-expert knowledge, being informal, puzzling, uncertain, incomplete, hard to be handled, formalized, modelled. This study utilizes Fuzzy cognitive maps to explore such complexity and support multiagent decisions. It concerns the scenario-building process of the new plan of Taranto (Italy), a paradigmatic example of decaying industrial area, heavily characterized by social fragmentation and environment degradation. This approach aims at structuring environmental problems, modelling future strategies and contributing to build a multi-agent decision support system for complex urban planning contexts.

Unibot, a Universal Agent Architecture for Robots

Today there are numerous robots in different applications domains despite the fact that they still have limitations in perception, actuation and decision process. Consequently, robots usually have limited autonomy, they are domain specific or have difficulty to adapt on new environments. Learning is the property that makes an agent intelligent and the crucial property that a robot should have to proliferate into the human society. Embedding the learning ability into the robot may simplify the development of the robot control mechanism. The motivation for this research is to develop the agent architecture of the universal robot – Unibot. In our approach the agent is the robot i.e. Unibot that acts in the physical world and is capable of learning. The Unibot conducts several simultaneous simulations of a problem of interest like path-finding. The novelty in our approach is the Multi-Agent Decision Support System which is developed and integrated into the Unibot agent architecture in order to execute simultaneous simulations. Furthermore, the Unibot calculates and evaluates between multiple solutions, decides which action should be performed and performs the action. The prototype of the Unibot agent architecture is described and evaluated in the experiment supported by the Lego Mindstorms robot and the NetLogo.

Multi-agent decision support system for missile defense based on improved PSO algorithm

Ballistic missile defense system (BMDS) is important for its special role in ensuring national security and maintaining strategic balance. Research on modeling and simulation of the BMDS beforehand is essential as developing a real one requires lots of manpower and resources. BMDS is a typical complex system for its nonlinear, adaptive and uncertainty characteristics. The agent-based modeling method is well suited for the complex system whose overall behaviors are determined by interactions among individual elements. A multi-agent decision support system (DSS), which includes missile agent, radar agent and command center agent, is established based on the studies of structure and function of BMDS. Considering the constraints brought by radar, intercept missile, offensive missile and commander, the objective function of DSS is established. In order to dynamically generate the optimal interception plan, the variable neighborhood negative selection particle swarm optimization (VNNSPSO) algorithm is proposed to support the decision making of DSS. The proposed algorithm is compared with the standard PSO, constriction factor PSO (CFPSO), inertia weight linear decrease PSO (LDPSO), variable neighborhood PSO (VNPSO) algorithm from the aspects of convergence rate, iteration number, average fitness value and standard deviation. The simulation results verify the efficiency of the proposed algorithm. The multi-agent DSS is developed through the Repast simulation platform and the constructed DSS can generate intercept plans automatically and support three-dimensional dynamic display of missile defense process.

Agent-based Decision Support System for Technology Recommendation

This paper presents an idea of a multi-agent decision support system. Agent-based technology allows for decentralized problem solving and creating complex decision support systems, mixing various processing techniques, such as simulation, reasoning and machine learning and allows for distributed knowledge. Our main contribution is an agent-based architecture for decision support systems which is an agent-based implementation of a labeled deductive system. Such approach allows to decompose an inference algorithm into separate modules and distribute knowledge base into parts. The system is tested on a domain of material choice support for casting.

Using Semantic Components to Represent Dynamics of an Interdisciplinary Healthcare Team in a Multi-Agent Decision Support System.

In healthcare organizations, clinical workflows are executed by interdisciplinary healthcare teams (IHTs) that operate in ways that are difficult to manage. Responding to a need to support such teams, we designed and developed the MET4 multi-agent system that allows IHTs to manage patients according to presentation-specific clinical workflows. In this paper, we describe a significant extension of the MET4 system that allows for supporting rich team dynamics (understood as team formation, management and task-practitioner allocation), including selection and maintenance of the most responsible physician and more complex rules of selecting practitioners for the workflow tasks. In order to develop this extension, we introduced three semantic components: (1) a revised ontology describing concepts and relations pertinent to IHTs, workflows, and managed patients, (2) a set of behavioral rules describing the team dynamics, and (3) an instance base that stores facts corresponding to instances of concepts from the ontology and to relations between these instances. The semantic components are represented in first-order logic and they can be automatically processed using theorem proving and model finding techniques. We employ these techniques to find models that correspond to specific decisions controlling the dynamics of IHT. In the paper, we present the design of extended MET4 with a special focus on the new semantic components. We then describe its proof-of-concept implementation using the WADE multi-agent platform and the Z3 solver (theorem prover/model finder). We illustrate the main ideas discussed in the paper with a clinical scenario of an IHT managing a patient with chronic kidney disease.

An agent-based distributed collaborative decision support system

The agent-based technology has emerged as a new paradigm for conceptualizing, designing, and implementing software systems. It has been considered as an important approach for developing industrial distributed systems. In this paper, we propose a Multi-agent view of a distributed collaborative decision support systems. These systems refer to a group of logically decentralized participants facilitator and decision-makers that cooperate to achieve objectives that are typically beyond the capabilities of any individual participant, and where the facilitator acts as a manager of the overall collaborative decision making activities that requires among those participant collaborating on decision making The main aims of the proposed multi-agent distributed collaborative decision support system are a distributed work support by enabling the flow of information and interoperation among distributed decision makers, and an efficient management of the information circulated in a distributed environment by providing content related aid. Specifically, agents were used to support a geographically dispersed collaborative decision making to solve jointly a problem and make a decision. In such systems, intelligent agents offer tremendous potential in supporting all stages of collaborative decision process such as idea generation, alternatives organizing and evaluation that would allow the group members focus on solutions that were found to be significant. They enhance overall system performance, in particular along such dimensions as computational efficiency, reliability, extensibility, responsiveness, maintainability reuse, and flexibility. In this way, we take advantage of the capacities of both the decision-maker and the machine. We apply the multi-agent system paradigm to decision support in a global contingency management.

The Realization on Cooperative Decision in Multi-Agent Intelligent Decision Support System

The Realization on Cooperative Decision in Multi-Agent Intelligent Decision Support System

Realization on Multi-Agent Intelligent Decision Support System Based on Blackboard

The majority of work is completed by collaboration of various components in a particular group environment, and the architecture of Multi-Agent Intelligent Decision Support System based on blackboard is designed using blackboard system and Agent. At the same time, implementation technique on Agent in system is provided, that includes abstract structure of Agent and modeling of Agent action. In addition to, mechanism of breakdown the task on blackboard control Agent is described. The system is helpful to resolve the complicated works though Multi-Agent.

Decision Approach of Multi-Agent Intelligent Decision Support System Based on Blackboard

Multi-Agent Intelligent Decision Support System based on blackboard is designed that uses of blackboard system and Agent, it describes the framework and gives the detail about the each Agent. At the same time, decision algorithm based on rough set of system is provided, and its application is described. The system can help to cooperate in Multi-Agent about complicated works.

A multi-agent-based decision support system for bankruptcy contagion effects

With the increasing interdependence of marketing participants, distress experienced by a specific entity may cause other connecting firms to encounter financial difficulties, leading to a negative impact on their stock valuations. At the same time, individual investors have a great need to gain relevant information for portfolio risk management. The monitoring vision cannot be limited to investors’ portfolios but must take into account any potential candidates affected. Based on the ontological knowledge model of inter-firm relationships, the proposed multi-agent decision support system continuously observes real-time news reports and forecasts their potential impact on the corresponding stock price. After identifying relating companies for which significant market reactions can be expected, a wireless push-based message service promptly supplies information. A case study is used to illustrate the multi-agent-based decision support system (MAB-DSS) implementation and its use. The example shows that the MAB-DSS can automate the solution for intricate and dynamic valuation effects among interdependent firms and provide constructive advice for individual investors.

A Multiagent Approach to the Dynamic Enactment of Semantic Transportation Services

Due to the inherent distribution of transportation management domains, multiagent approaches to the construction of decision support systems (DSSs) are popular. In this paper, we propose to complement multiagent DSS for transportation management by a service-oriented computing approach. In particular, we describe how organizational models can be used for service description and discovery. Our main contribution refers to the integration of agent organizations and services for transportation management to facilitate the on-the-fly adaptation, fault tolerance, and extensibility of the intelligent transport systems (ITS) architecture. We apply our approach to two real-world applications in the transportation management domain.

A data fusion framework with novel hybrid algorithm for multi-agent Decision Support System for Forest Fire

In this study Forest Fire Decision Support System (FOFDESS) which is a multi-agent Decision Support System for Forest Fire has been presented. Depending on the existing meteorological state and environmental observations, FOFDESS does the fire danger rating by predicting the forest fire and it can also approximate fire spread speed and quickly detect a started fire. Some data fusion algorithms such as Artificial Neural Network (ANN), Naive Bayes Classifier (NBC), Fuzzy Switching (FS) and image processing have been used for these operations in FOFDESS. These algorithms have been brought together by a designed data fusion framework and a novel hybrid algorithm called NABNEF (Naive Bayes Aided Neural-Fuzzy Algorithm) has been improved for fire danger rating in FOFDESS. In this state, FOFDESS is an integrated system which includes the dimensions of prediction, detection and management. As a result of the experiments, it was found out that FOFDESS helped determining the most accurate strategy for fire fighting by producing effective results.

Towards a Multiagent Decision Support System for Crisis Management

Crisis management is a complex problem raised by the scientific community currently. Decision support systems are a suitable solution for such issues, they are indeed able to help emergency managers to prevent and to manage crisis in emergency situations. However, they should be enough flexible and adaptive in order to be reliable to solve complex problems that are plunged in dynamic and unpredictable environments. The approach we propose in this paper addresses this challenge. We expose here a modelling of information for an emergency environment and an architecture of a multiagent decision support system that deals with these information in order to prevent and to manage the occur of a crisis in emergency situations. We focus on the first level of the system mechanism which intends to perceive and to reflect the evolution of the current situation. The general approach and experimentations are provided here.

Ontologies and agents for a bus fleet management system☆

This paper reports on the design and implementation of a multi-agent decision support system for the bus fleet management domain. In particular, we show a complete description of the proposed multi-agent architecture and focus mainly on knowledge and software engineering features. The system has been developed in collaboration with the Malaga urban bus company (EMT) and it is based on a faithful reproduction of the real operating conditions of three existing lines of this company.

Data representation layer in a MultiAgent decision support system

This article presents a system designed to help deciders manage cases of crisis. The system represents, characterises and interprets the dynamic evolution of a given situation and displays the results of its analysis. The core of the system is made o

Decision support for traffic management based on organisational and communicative multiagent abstractions

Modern decision support systems (DSS) not only store large amounts of decision-relevant data, but also aim at assisting decision-makers to explore the meaning of that data, and to take decisions based on understanding. In transportation domains, a multiagent approach to the construction of DSS is becoming increasingly popular, because it does not only reduce design complexity, but it also adequately supports a dialogue-based stance on decision support interactions. However, despite recent advances in the field of agent-oriented software engineering, a principled approach to the design of multiagent systems for decision support is still to come. In this paper, we outline a design method for the construction of agent-based DSS. Setting out from an organisational and communicative model of decision support environments, we present an abstract architecture for multiagent DSS. We then show how this architecture is instantiated for real-world problems by means of two prototypes for transportation management.

Pluralistic multi-agent decision support system: a framework and an empirical test

Recent research in decision support systems (DSSs) has focused on building active cooperative intelligent systems. Research in agent-based decision support is a promising stream in this direction. This paper proposes a framework for a pluralistic multi-agent decision support system (MADSS). The distinguishing feature of the proposed approach is its organization around human decision making process. The framework builds upon the decision support pyramid with agents organized into groups according to the phases of the problem solving model. We outline the design principles and develop architecture for MADSS. The framework is illustrated through an investment MADSS prototype. The results of the empirical test are presented.