A Temporal Distributed Group Decision Support System Based on Multi-Criteria Analysis

Decision support consists of proposing tasks and projects by taking into account temporal constraints and the use of resources with the aim of finding a compromise solution between several alternatives. Indeed, on the one hand, centralized resolution systems and methods are generally inappropriate to the real case because of the local unavailability of decision makers. On the other hand, the data of the decisional problem are generally poorly expressed in a negotiation environment. Other techniques and approaches treat the same decision-making problem and impose a distributed vision for coherent decisions. For this purpose, Multi-Agent Systems (MAS) allow modeling a distributed resolution of the group decision support problem. In this article, we propose a new model of a multi-criteria group decision support system based on a multi-agent system modeling a spatial problem. We consider that each decision maker is assimilated to an agent that has a decision-making autonomy, in which he interacts with other agents in the debate through a negotiation process in order to reach an acceptable compromise. In this study, we propose coordination mechanisms among agents to highlight the simulated negotiation. Therefore, the proposed system finds a solution before fixed deadlines’ time expire. We experiment the suggested negotiation model to solve the decisional problem of spatial localization in territory planning

Information fusion as input source for improving multi-agent system autonomous decision-making in maritime surveillance scenarios

Decision making problems are usually referred as a cognitive process resulting in the selection of an action among several alternatives. Autonomous entities taking these actions like multi-agent systems, needs to process and understand its environment state to frequently update its beliefs, and then, select an optimal action. As an environment can be composed by several sources of information, it is useful for a multi-agent system, a way to process integrated information of multiple data which represents the same real-world object. This information can improve the agents knowledge and let select better actions than processing simple raw data. Most information fusion research has had a technical and algorithmic focus, and takes little attention to high level decision making, although some studies relate fusion to human decision making. However, in this paper is proposed the use of fused information as an input source for supporting and improving the decision making capabilities of autonomous agents in maritime surveillance scenarios.This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485

Automating virtual power plant decision making with fuzzy logic and human psychology

This paper presents a Virtual Power Plant (VPP) decision making approach which uses fuzzy logic and a novel “insecurity” metric, based on human psychology. The VPP approach is modelled as a multi-agent system, which aims to minimize carbon emissions and/or energy cost, using an aggregation structure similar to energy or carbon markets. The “insecurity factor” reflects the operational flexibility of micro-generators, translated to a numerical value through fuzzy logic. The system was able to create a functional internal VPP market, where the micro-generators were trading autonomously according to external price signals and taking into account their own needs and limitations, as well as short-term forecasts

Star-shaped mediation in influence games

We are interested in analyzing the properties of multi-agent systems [13] where a set of agents have to take a decision among two possible alternatives with the help of the social environment or network of the system itself. The ways in which people influence each other through their interactions in a social network and, in particular, the social rules that can be used for the spread of influence have been proposed in an alternative simple game model [11]. However not all individuals play the same role in the process of taking a decision. In this paper we are interested in formalizing and analyzing the simple game model that results in a mediation system. In this scenario we have a social network together with an external participant, the mediator. The mediator can interact, in different degrees, with the agents and thus help to reach a decision.Peer ReviewedPostprint (published version

AI for Explaining Decisions in Multi-Agent Environments

Explanation is necessary for humans to understand and accept decisions made by an AI system when the system's goal is known. It is even more important when the AI system makes decisions in multi-agent environments where the human does not know the systems' goals since they may depend on other agents' preferences. In such situations, explanations should aim to increase user satisfaction, taking into account the system's decision, the user's and the other agents' preferences, the environment settings and properties such as fairness, envy and privacy. Generating explanations that will increase user satisfaction is very challenging; to this end, we propose a new research direction: xMASE. We then review the state of the art and discuss research directions towards efficient methodologies and algorithms for generating explanations that will increase users' satisfaction from AI system's decisions in multi-agent environments.Comment: This paper has been submitted to the Blue Sky Track of the AAAI 2020 conference. At the time of submission, it is under review. The tentative notification date will be November 10, 2019. Current version: Name of first author had been added in metadat

Time Critical Mass Evacuation Simulation Combining A Multi- Agent System and High-Performance Computing

This chapter presents an application of multi-agent systems to simulate tsunami-triggered mass evacuations of large urban areas. The main objective is to quantitatively evaluate various strategies to accelerate evacuation in case of a tsunami with a short arrival time, taking most influential factors into account. Considering the large number of lives in fatal danger, instead of widely used simple agents in 1D networks, we use a high-resolution model of environment and complex agents so that wide range of influencing factors can be taken into account. A brief description of the multi-agent system is provided using a mathematical framework as means to easily and unambiguously refer to the main components of the system. The environment of the multi-agent system, which mimics the physical world of evacuees, is modelled as a hybrid of a high-resolution grid and a graph connecting traversable spaces. This hybrid of raster and vector data structures enables modelling large domain in a scalable manner. The agents, which mimic the heterogeneous crowd of evacuees, are composed of different combinations of basic constituent functions for modelling interaction with each other and environment, decision-making, etc. The results of tuning and validating of constituent functions for pedestrian-pedestrian, car-car and car-pedestrian interactions are presented. A scalable high-performance computing (HPC) extension to address the high-computational demand of complex agents and high-resolution model of environment is briefly explained. Finally, demonstrative applications that highlight the need for including sub-meter details in the environment, different modes of evacuation and behavioural differences are presented

Human–agent collaboration for disaster response

In the aftermath of major disasters, first responders are typically overwhelmed with large numbers of, spatially distributed, search and rescue tasks, each with their own requirements. Moreover, responders have to operate in highly uncertain and dynamic environments where new tasks may appear and hazards may be spreading across the disaster space. Hence, rescue missions may need to be re-planned as new information comes in, tasks are completed, or new hazards are discovered. Finding an optimal allocation of resources to complete all the tasks is a major computational challenge. In this paper, we use decision theoretic techniques to solve the task allocation problem posed by emergency response planning and then deploy our solution as part of an agent-based planning tool in real-world field trials. By so doing, we are able to study the interactional issues that arise when humans are guided by an agent. Specifically, we develop an algorithm, based on a multi-agent Markov decision process representation of the task allocation problem and show that it outperforms standard baseline solutions. We then integrate the algorithm into a planning agent that responds to requests for tasks from participants in a mixed-reality location-based game, called AtomicOrchid, that simulates disaster response settings in the real-world. We then run a number of trials of our planning agent and compare it against a purely human driven system. Our analysis of these trials show that human commanders adapt to the planning agent by taking on a more supervisory role and that, by providing humans with the flexibility of requesting plans from the agent, allows them to perform more tasks more efficiently than using purely human interactions to allocate tasks. We also discuss how such flexibility could lead to poor performance if left unchecked

Appuis et obstacles dans l'usage didactique des modélisations d'accompagnement pour une éducation au développement durable

International audienceCompanion modelling1 associates simulation using a Multi agent system, a Geographical Information System and role playing in order to create a model and to simulate dynamics of eco-socio-systems. Their main aims are to help taking decision about complex problems related to management of resources, natural patrimonies and biodiversity. We have enlightened different supports and obstacles for their didactic transposition in vocational teaching in agriculture. The companion modellings can allow to sensitize to eco-socio-system dynamics ; their internal transposition is necessary to make them considered by the learner as a mirror of the reality and to allow him to be involved and to change during the game.Les modélisations d'accompagnement, en tentant de prendre en compte la dynamique d'écosocio-systèmes, se veulent être un outil d'aide à la décision dans le cadre de problématiques complexes telles que la gestion de la biodiversité. Conçues par et avec les acteurs du territoire, elles associent un système multi-agent, un jeu de rôle et un système d'information géographique qui visualise l'évolution du territoire en fonction des choix pris par les acteurs durant le jeu. Nous avons mis en évidence différents appuis et obstacles à la transposition didactique dont elles font l'objet dans l'enseignement agricole. Si les modélisations peuvent permettre de répondre à des objectifs de sensibilisation relatifs au fonctionnement d'écosocio-systèmes complexes, une transposition interne se justifie pour leur permettre d'être conçues comme miroir d'une réalité, pouvant permettre l'implication et l'évolution de l'élève dans le jeu

A human centred hybrid MAS and meta-heuristics based system for simultaneously supporting scheduling and plant layout adjustment

Manufacturing activities and production control are constantly growing. Despite this, it is necessary to improve the increasing variety of scheduling and layout adjustments for dynamic and flexible responses in volatile environments with disruptions or failures. Faced with the lack of realistic and practical manufacturing scenarios, this approach allows simulating and solving the problem of job shop scheduling on a production system by taking advantage of genetic algorithm and particle swarm optimization algorithm combined with the flexibility and robustness of a multi-agent system and dynamic rescheduling alternatives. Therefore, this hybrid decision support system intends to obtain optimized solutions and enable humans to interact with the system to properly adjust priorities or refine setups or solutions, in an interactive and user-friendly way. The system allows to evaluate the optimization performance of each one of the algorithms proposed, as well as to obtain decentralization in responsiveness and dynamic decisions for rescheduling due to the occurance of unexpected events.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019