Optimization under Uncertainty with Applications to Multi-Agent Coordination

In this thesis several approaches for optimization and decision-making under uncertainty with a strong focus on applications in multi-agent systems are considered. These approaches are chance constrained optimization, random convex programs, and partially observable Markov decision processes

Artificial Intelligence in the Context of Human Consciousness

Artificial intelligence (AI) can be defined as the ability of a machine to learn and make decisions based on acquired information. AI’s development has incited rampant public speculation regarding the singularity theory: a futuristic phase in which intelligent machines are capable of creating increasingly intelligent systems. Its implications, combined with the close relationship between humanity and their machines, make achieving understanding both natural and artificial intelligence imperative. Researchers are continuing to discover natural processes responsible for essential human skills like decision-making, understanding language, and performing multiple processes simultaneously. Artificial intelligence attempts to simulate these functions through techniques like artificial neural networks, Markov Decision Processes, Human Language Technology, and Multi-Agent Systems, which rely upon a combination of mathematical models and hardware

A Robust Decision-Making Framework Based on Collaborative Agents

Making decisions under uncertainty is very challenging but necessary as most real-world scenarios are plagued by disturbances that can be generated internally, by the hardware itself, or externally, by the environment. Hence, we propose a general decision-making framework which can be adapted to optimally address the most heterogeneous real-world domains without being significantly affected by undesired disturbances. Our paper presents a multi-agent based structure in which agents are capable of individual decision-making but also interact to perform subsequent, and more robust, collaborative decision-making processes. The complexity of each software agent can be kept quite low without a deterioration of the performance since an intelligent and robust-to-uncertainty decision-making behaviour arises when their locally produced measure of support are shared and exploited collaboratively. We show that by equipping agents with classic computational intelligence techniques, to extract features and generate measure of supports, complex hybrid multi-agent software structures capable of handling uncertainty can be easily designed. The resulting multi-agent systems generated with this approach are based on a two-phases decision-making methodology which first runs parallel local decision making processes to then aggregate the corresponding outputs to improve upon the accuracy of the system. To highlight the potential of this approach, we provided multiple implementations of the general framework and compared them over four different application scenarios. Results are promising and show that having a second collaborative decision-making process is always beneficial

ANALYSIS AND DESIGN FOR AN WORKING PLAN COORDINATION USING JADE MULTI-AGENT SYSTEM IN DECISION MAKING PROCES

Systems composed of interacting autonomous agents offer new ways in developing applicationsin complex domains. Using a multi-agent platform to coordinate an information systemis an appropriate choice because of the complexity and dynamism required. Data fluxof an economical system is generally built to follow document movements. On the otherhand, decision making and disseminating processes are complex and must be flexible andnetwork distributed. Our goal is to build a Decision Support System (DSS) using JADEMulti-agent system. This paper reflects a small part of this goal so we are emphasizing theworking plan coordination

Development of an Automated System for Analysis, Modeling, and Decision-Making for Metallurgical Enterprise

The paper presents development of an automated system for analysis, modeling, and decision-making for metallurgical production. When operating information systems at various levels of automation, there is a problem of system integration at the level of information exchange to support timely decision-making. Problem solutions are aimed at implementing a typical business process for changing (improving) the technological, logistic, and organizational processes of the enterprise. The paper describes a scheme for integrating the processes of metallurgical production improving based on the use of a multi-agent approach in development of modules of the automated system. The use of the multi-agent approach allows solving the problem of automating the processes of approval and decision-making through the communication of hybrid agents implementing the functionality of individual modules of the system. © 2022 American Institute of Physics Inc.. All rights reserved.Government Council on Grants, Russian FederationA decision support method based on a multi-agent approach was used when developing the AMD system. This method is supported by the BPsim software package [18], which allows describing hybrid agents using production and frame knowledge bases. The BPsim tool includes the following integrated products: systems for dynamic situation modeling and decision support, CASE tool

Agent collaboration in a multi-agent-system for analysis and optimization of mechanical engineering parts

In mechanical engineering, designers have to review a designed artefact iteratively with different domain experts, e.g. from manufacturing, to avoid later changes and find a robust, optimized design. To support the designer, knowledge-based engineering offers a set of approaches and techniques that formalize and implement engineering knowledge into generic product models or decision support systems. An implementation which satisfies especially the concurrent nature of today's design processes and allow for multi-objective decision-making is multi-agent systems. Such systems consist of entities that are capable of autonomous action, interact intelligently with their environment, communicate and collaborate. In this paper, such a multi-agent system is discussed as extension for a computer-aided design software where the agents take the role of domain experts, like e.g. manufacturing technologists and make suggestions for the optimization of the design of mechanical engineering parts. A focal point is set on the collaboration concept of the single agents. Therefore, the paper proposes the use of an action-item-list as central information and knowledge sharing platform. © 2020 The Authors. Published by Elsevier B.V

COACHES Cooperative Autonomous Robots in Complex and Human Populated Environments

Public spaces in large cities are increasingly becoming complex and unwelcoming environments. Public spaces progressively become more hostile and unpleasant to use because of the overcrowding and complex information in signboards. It is in the interest of cities to make their public spaces easier to use, friendlier to visitors and safer to increasing elderly population and to citizens with disabilities. Meanwhile, we observe, in the last decade a tremendous progress in the development of robots in dynamic, complex and uncertain environments. The new challenge for the near future is to deploy a network of robots in public spaces to accomplish services that can help humans. Inspired by the aforementioned challenges, COACHES project addresses fundamental issues related to the design of a robust system of self-directed autonomous robots with high-level skills of environment modelling and scene understanding, distributed autonomous decision-making, short-term interacting with humans and robust and safe navigation in overcrowding spaces. To this end, COACHES will provide an integrated solution to new challenges on: (1) a knowledge-based representation of the environment, (2) human activities and needs estimation using Markov and Bayesian techniques, (3) distributed decision-making under uncertainty to collectively plan activities of assistance, guidance and delivery tasks using Decentralized Partially Observable Markov Decision Processes with efficient algorithms to improve their scalability and (4) a multi-modal and short-term human-robot interaction to exchange information and requests. COACHES project will provide a modular architecture to be integrated in real robots. We deploy COACHES at Caen city in a mall called “Rive de l’orne”. COACHES is a cooperative system consisting of ?xed cameras and the mobile robots. The ?xed cameras can do object detection, tracking and abnormal events detection (objects or behaviour). The robots combine these information with the ones perceived via their own sensor, to provide information through its multi-modal interface, guide people to their destinations, show tramway stations and transport goods for elderly people, etc.... The COACHES robots will use different modalities (speech and displayed information) to interact with the mall visitors, shopkeepers and mall managers. The project has enlisted an important an end-user (Caen la mer) providing the scenarios where the COACHES robots and systems will be deployed, and gather together universities with complementary competences from cognitive systems (SU), robust image/video processing (VUB, UNICAEN), and semantic scene analysis and understanding (VUB), Collective decision-making using decentralized partially observable Markov Decision Processes and multi-agent planning (UNICAEN, Sapienza), multi-modal and short-term human-robot interaction (Sapienza, UNICAEN