A Natural Formalism and a MultiAgent Algorithm for Integrative Multidisciplinary Design Optimization

International audienceMultiDisciplinary Optimization (MDO) problems represent one of the hardest and broadest domains of continuous optimization. By involving both the models and criteria of different disciplines, MDO problems are often too complex to be tackled by classical optimization methods. We propose an approach which takes into account this complexity using a new representation (NDMO - Natural Domain Modeling for Optimization) and a self-adaptive multi-agent algorithm. Our method agentifies the different elements of the problem (such as the variables, the models, the objectives). Each agent is in charge of a small part of the problem and cooperates with others to find equilibrium on conflicting values. Despite the fact that no agent of the system has a complete view of the entire problem, the mechanisms we provide allow the emergence of a coherent solution. Evaluations on several academic and industrial test cases are provided

Agent-Based Natural Domain Modeling for Cooperative Continuous Optimization

International audienceWhile multi-agent systems have been successfully applied to combinatorial optimization, very few works concern their applicability to continuous optimization problems. In this article we propose a framework for modeling a continuous optimization problems as multi-agent system,which we call NDMO, by representing the problem as an agent graph, and complemented with optimization solving behaviors. Some of the results we obtained with our implementation on several continuous optimization problems are presented

An adaptive multi-agent system for self-organizing continuous optimization

Cette thèse présente une nouvelle approche pour la distribution de processus d'optimisation continue dans un réseau d'agents coopératifs. Dans le but de résoudre de tels problèmes, le domaine de l'optimisation multidisciplinaire a été proposé. Les méthodes d'optimisation multidisciplinaire proposent de distribuer le processus d'optimisation, généralement en reformulant le problème original d'une manière qui réduit les interconnexions entre les disciplines. Cependant, ces méthodes présentent des désavantages en ce qui concerne la difficulté de les appliquer correctement, ainsi que leur manque de flexibilité. En se basant sur la théorie des AMAS (Adaptive Multi-Agent Systems), nous proposent une représentation générique à base d'agents des problèmes d'optimisation continue. A partir de cette représentation, nous proposons un comportement nominal pour les agents afin d'exécuter le processus d'optimisation. Nous identifions ensuite certaines configurations spécifiques qui pourraient perturber le processus, et présentons un ensemble de comportements coopératifs pour les agents afin d'identifier et de résoudre ces configurations problématiques. Enfin, nous utilisons les mécanismes de coopération que nous avons introduit comme base à des patterns de résolution coopérative de problèmes. Ces patterns sont des recommandations de haut niveau pour identifier et résoudre des configurations potentiellement problématiques qui peuvent survenir au sein de systèmes de résolution collective de problèmes. Ils fournissent chacun un mécanisme de résolution coopérative pour les agents, en utilisant des indicateurs abstraits qui doivent être instanciés pour le problème en cours.In an effort to tackle such complex problems, the field of multidisciplinary optimization methods was proposed. Multidisciplinary optimization methods propose to distribute the optimization process, often by reformulating the original problem is a way that reduce the interconnections between the disciplines. However these methods present several drawbacks regarding the difficulty to correctly apply them, as well as their lack of flexibility. Based on the AMAS (Adaptive Multi-Agent Systems) theory, we propose a general agent-based representation of continuous optimization problems. From this representation we propose a nominal behavior for the agents in order to do the optimization process. We then identify some specific configurations which would disturb this nominal optimization process, and present a set of cooperative behaviors for the agents to identify and solve these problematic configurations. At last, we use the cooperation mechanisms we introduced as the basis for more general Collective Problem Solving Patterns. These patterns are high-level guideline to identify and solve potential problematic configurations which can arise in distributed problem solving systems. They provide a specific cooperative mechanism for the agents, using abstract indicators that are to be instantiated on the problem at hand

Autonomous Pareto Front Scanning using an Adaptive Multi-Agent System for Multidisciplinary Optimization

Multidisciplinary Design Optimization (MDO) problems can have a unique objective or be multi-objective. In this paper, we are interested in MDO problems having at least two conflicting objectives. This characteristic ensures the existence of a set of compromise solutions called Pareto front. We treat those MDO problems like Multi-Objective Optimization (MOO) problems. Actual MOO methods suffer from certain limitations, especially the necessity for their users to adjust various parameters. These adjustments can be challenging, requiering both disciplinary and optimization knowledge. We propose the use of the Adaptive Multi-Agent Systems technology in order to automatize the Pareto front obtention. ParetOMAS (Pareto Optimization Multi-Agent System) is designed to scan Pareto fronts efficiently, autonomously or interactively. Evaluations on several academic and industrial test cases are provided to validate our approach

Optimisation sous contraintes de problèmes distribués par auto-organisation coopérative

Quotidiennement, divers problèmes d'optimisation : minimiser un coût de production, optimiser le parcours d'un véhicule, etc sont à résoudre. Ces problèmes se caractérisent par un degré élevé de complexité dû à l'hétérogénéité et la diversité des acteurs en jeu, à la masse importante des données ainsi qu'à la dynamique des environnements dans lesquels ils sont plongés. Face à la complexité croissante de ces applications, les approches de résolution classiques ont montré leurs limites. Depuis quelques années, la communauté scientifique s'intéresse aux développements de nouvelles solutions basées sur la distribution du calcul et la décentralisation du contrôle plus adaptées à ce genre de problème. La théorie des AMAS (Adaptive Multi-Agents Systems) propose le développement de solutions utilisant des systèmes multi-agents auto-adaptatifs par auto-organisation coopérative. Cette théorie a montré son adéquation pour la résolution de problèmes complexes et dynamiques, mais son application reste à un niveau d'abstraction assez élevé. L'objectif de ce travail est de spécialiser cette théorie pour la résolution de ce genre de problèmes. Ainsi, son utilisation en sera facilitée. Pour cela, le modèle d'agents AMAS4Opt avec des comportements et des interactions coopératifs et locaux a été défini. La validation s'est effectuée sur deux problèmes clés d'optimisation : le contrôle manufacturier et la conception de produit complexe. De plus, afin de montrer la robustesse et l'adéquation des solutions développées, un ensemble de critères d'évaluation permettant de souligner les points forts et faibles des systèmes adaptatifs et de les comparer à des systèmes existants a été défini.We solve problems and make decisions all day long. Some problems and decisions are very challenging: What is the best itinerary to deliver orders given the weather, the traffic and the hour? How to improve product manufacturing performances? etc. Problems that are characterized by a high level of complexity due to the heterogeneity and diversity of the participating actors, to the increasing volume of manipulated data and to the dynamics of the applications environments. Classical solving approaches have shown their limits to cope with this growing complexity. For the last several years, the scientific community has been interested in the development of new solutions based on computation distribution and control decentralization. The AMAS (Adaptive Multi-Agent-Systems) theory proposes to build solutions based on self-adaptive multi-agent systems using cooperative self-organization. This theory has shown its adequacy to solve different complex and dynamic problems, but remains at a high abstraction level. This work proposes a specialization of this theory for complex optimization problem solving under constraints. Thus, the usage of this theory is made accessible to different non-AMAS experts' engineers. Thus, the AMAS4Opt agent model with cooperative, local and generic behaviours and interactions has been defined.This model is validated on two well-known optimization problems: scheduling in manufacturing control and complex product design. Finally, in order to show the robustness and adequacy of the developed solutions, a set of evaluation criteria is proposed to underline the advantages and limits of adaptive systems and to compare them with already existing systems

An Open Source Digital Twin Framework

In this thesis, the utility and ideal composition of high-level programming frameworks to facilitate digital twin experiments were studied. Digital twins are a specific class of simulation artefacts that exist in the cyber domain parallel to their physical counterparts, reflecting their lives in a particularly detailed manner. As such, digital twins are conceived as one of the key enabling technologies in the context of intelligent life cycle management of industrial equipment. Hence, open source solutions with which digital twins can be built, executed and evaluated will likely see an increase in demand in the coming years. A theoretical framework for the digital twin is first established by reviewing the concepts of simulation, co-simulation and tool integration. Based on the findings, the digital twin is formulated as a specific co-simulation class consisting of software agents that interact with one of two possible types of external actors, i.e., sensory measurement streams originating from physical assets or simulation models that make use of the mentioned streams as inputs. The empirical part of the thesis consists of describing ModelConductor, an original Python library that supports the development of digital twin co-simulation experiments in presence of online input data. Along with describing the main features, a selection of illustrative use cases are presented. From a software engineering point of view, a high-level programmatic syntax is demonstrated through the examples that facilitates rapid prototyping and experimentation with various types of digital twin setups. As a major contribution of the thesis, object-oriented software engineering approach has been demonstrated to be a plausible means to construct and execute digital twins. Such an approach could potentially have consequences on digital twin related tasks being increasingly performed by software engineers in addition to domain experts in various engineering disciplines. In particular, the development of intelligent life cycle services such as predictive maintenance, for example, could benefit from workflow harmonization between the communities of digital twins and artificial intelligence, wherein high-level open source solutions are today used almost exclusively

Using spatiotemporal patterns to qualitatively represent and manage dynamic situations of interest : a cognitive and integrative approach

Les situations spatio-temporelles dynamiques sont des situations qui évoluent dans l’espace et dans le temps. L’être humain peut identifier des configurations de situations dans son environnement et les utilise pour prendre des décisions. Ces configurations de situations peuvent aussi être appelées « situations d’intérêt » ou encore « patrons spatio-temporels ». En informatique, les situations sont obtenues par des systèmes d’acquisition de données souvent présents dans diverses industries grâce aux récents développements technologiques et qui génèrent des bases de données de plus en plus volumineuses. On relève un problème important dans la littérature lié au fait que les formalismes de représentation utilisés sont souvent incapables de représenter des phénomènes spatiotemporels dynamiques et complexes qui reflètent la réalité. De plus, ils ne prennent pas en considération l’appréhension cognitive (modèle mental) que l’humain peut avoir de son environnement. Ces facteurs rendent difficile la mise en œuvre de tels modèles par des agents logiciels. Dans cette thèse, nous proposons un nouveau modèle de représentation des situations d’intérêt s’appuyant sur la notion des patrons spatiotemporels. Notre approche utilise les graphes conceptuels pour offrir un aspect qualitatif au modèle de représentation. Le modèle se base sur les notions d’événement et d’état pour représenter des phénomènes spatiotemporels dynamiques. Il intègre la notion de contexte pour permettre aux agents logiciels de raisonner avec les instances de patrons détectés. Nous proposons aussi un outil de génération automatisée des relations qualitatives de proximité spatiale en utilisant un classificateur flou. Finalement, nous proposons une plateforme de gestion des patrons spatiotemporels pour faciliter l’intégration de notre modèle dans des applications industrielles réelles. Ainsi, les contributions principales de notre travail sont : Un formalisme de représentation qualitative des situations spatiotemporelles dynamiques en utilisant des graphes conceptuels. ; Une approche cognitive pour la définition des patrons spatio-temporels basée sur l’intégration de l’information contextuelle. ; Un outil de génération automatique des relations spatiales qualitatives de proximité basé sur les classificateurs neuronaux flous. ; Une plateforme de gestion et de détection des patrons spatiotemporels basée sur l’extension d’un moteur de traitement des événements complexes (Complex Event Processing).Dynamic spatiotemporal situations are situations that evolve in space and time. They are part of humans’ daily life. One can be interested in a configuration of situations occurred in the environment and can use it to make decisions. In the literature, such configurations are referred to as “situations of interests” or “spatiotemporal patterns”. In Computer Science, dynamic situations are generated by large scale data acquisition systems which are deployed everywhere thanks to recent technological advances. Spatiotemporal pattern representation is a research subject which gained a lot of attraction from two main research areas. In spatiotemporal analysis, various works extended query languages to represent patterns and to query them from voluminous databases. In Artificial Intelligence, predicate-based models represent spatiotemporal patterns and detect their instances using rule-based mechanisms. Both approaches suffer several shortcomings. For example, they do not allow for representing dynamic and complex spatiotemporal phenomena due to their limited expressiveness. Furthermore, they do not take into account the human’s mental model of the environment in their representation formalisms. This limits the potential of building agent-based solutions to reason about these patterns. In this thesis, we propose a novel approach to represent situations of interest using the concept of spatiotemporal patterns. We use Conceptual Graphs to offer a qualitative representation model of these patterns. Our model is based on the concepts of spatiotemporal events and states to represent dynamic spatiotemporal phenomena. It also incorporates contextual information in order to facilitate building the knowledge base of software agents. Besides, we propose an intelligent proximity tool based on a neuro-fuzzy classifier to support qualitative spatial relations in the pattern model. Finally, we propose a framework to manage spatiotemporal patterns in order to facilitate the integration of our pattern representation model to existing applications in the industry. The main contributions of this thesis are as follows: A qualitative approach to model dynamic spatiotemporal situations of interest using Conceptual Graphs. ; A cognitive approach to represent spatiotemporal patterns by integrating contextual information. ; An automated tool to generate qualitative spatial proximity relations based on a neuro-fuzzy classifier. ; A platform for detection and management of spatiotemporal patterns using an extension of a Complex Event Processing engine

The Human Affectome

Over the last decades, theoretical perspectives in the interdisciplinary field of the affective sciences have proliferated rather than converged due to differing assumptions about what human affective phenomena are and how they work. These metaphysical and mechanistic assumptions, shaped by academic context and values, have dictated affective constructs and operationalizations. However, an assumption about the purpose of affective phenomena can guide us to a common set of metaphysical and mechanistic assumptions. In this capstone paper, we home in on a nested teleological principle for human affective phenomena in order to synthesize metaphysical and mechanistic assumptions. Under this framework, human affective phenomena can collectively be considered algorithms that either adjust based on the human comfort zone (affective concerns) or monitor those adaptive processes (affective features). This teleologically-grounded framework offers a principled agenda and launchpad for both organizing existing perspectives and generating new ones. Ultimately, we hope the Human Affectome brings us a step closer to not only an integrated understanding of human affective phenomena, but an integrated field for affective research

The Human Affectome

Over the last decades, the interdisciplinary field of the affective sciences has seen proliferation rather than integration of theoretical perspectives. This is due to differences in metaphysical and mechanistic assumptions about human affective phenomena (what they are and how they work) which, shaped by academic motivations and values, have determined the affective constructs and operationalizations. An assumption on the purpose of affective phenomena can be used as a teleological principle to guide the construction of a common set of metaphysical and mechanistic assumptions—a framework for human affective research. In this capstone paper for the special issue “Towards an Integrated Understanding of the Human Affectome”, we gather the tiered purpose of human affective phenomena to synthesize assumptions that account for human affective phenomena collectively. This teleologically-grounded framework offers a principled agenda and launchpad for both organizing existing perspectives and generating new ones. Ultimately, we hope Human Affectome brings us a step closer to not only an integrated understanding of human affective phenomena, but an integrated field for affective research