A general framework integrating techniques for scheduling under uncertainty

Ces dernières années, de nombreux travaux de recherche ont porté sur la planification de tâches et l'ordonnancement sous incertitudes. Ce domaine de recherche comprend un large choix de modèles, techniques de résolution et systèmes, et il est difficile de les comparer car les terminologies existantes sont incomplètes. Nous avons cependant identifié des familles d'approches générales qui peuvent être utilisées pour structurer la littérature suivant trois axes perpendiculaires. Cette nouvelle structuration de l'état de l'art est basée sur la façon dont les décisions sont prises. De plus, nous proposons un modèle de génération et d'exécution pour ordonnancer sous incertitudes qui met en oeuvre ces trois familles d'approches. Ce modèle est un automate qui se développe lorsque l'ordonnancement courant n'est plus exécutable ou lorsque des conditions particulières sont vérifiées. Le troisième volet de cette thèse concerne l'étude expérimentale que nous avons menée. Au-dessus de ILOG Solver et Scheduler nous avons implémenté un prototype logiciel en C++, directement instancié de notre modèle de génération et d'exécution. Nous présentons de nouveaux problèmes d'ordonnancement probabilistes et une approche par satisfaction de contraintes combinée avec de la simulation pour les résoudre. ABSTRACT : For last years, a number of research investigations on task planning and scheduling under uncertainty have been conducted. This research domain comprises a large number of models, resolution techniques, and systems, and it is difficult to compare them since the existing terminologies are incomplete. However, we identified general families of approaches that can be used to structure the literature given three perpendicular axes. This new classification of the state of the art is based on the way decisions are taken. In addition, we propose a generation and execution model for scheduling under uncertainty that combines these three families of approaches. This model is an automaton that develops when the current schedule is no longer executable or when some particular conditions are met. The third part of this thesis concerns our experimental study. On top of ILOG Solver and Scheduler, we implemented a software prototype in C++ directly instantiated from our generation and execution model. We present new probabilistic scheduling problems and a constraintbased approach combined with simulation to solve some instances thereof

BLOCKS, a Component Framework with Checking Facilities for Knowledge-Based Systems

équipe PULSARInternational audienceBLOCKS is an answer to the software engineering needs of the design of knowledge-based system engines. It is a framework composed of reusable and adaptable software components. However , its safe and correct use is complex and we supply formal models and associated tools to assist using it. These models and tools are based on behavioral description of components and on model checking techniques. They ensure a safe reuse of the components, especially when extending them through inheritance, owing to the notion of behavioral refinement

Differential Evolution Algorithm in the Construction of Interpretable Classification Models

In this chapter, the application of a differential evolution-based approach to induce oblique decision trees (DTs) is described. This type of decision trees uses a linear combination of attributes to build oblique hyperplanes dividing the instance space. Oblique decision trees are more compact and accurate than the traditional univariate decision trees. On the other hand, as differential evolution (DE) is an efficient evolutionary algorithm (EA) designed to solve optimization problems with real-valued parameters, and since finding an optimal hyperplane is a hard computing task, this metaheuristic (MH) is chosen to conduct an intelligent search of a near-optimal solution. Two methods are described in this chapter: one implementing a recursive partitioning strategy to find the most suitable oblique hyperplane of each internal node of a decision tree, and the other conducting a global search of a near-optimal oblique decision tree. A statistical analysis of the experimental results suggests that these methods show better performance as decision tree induction procedures in comparison with other supervised learning approaches

Imprecise data fusion

Possibility theory offers a natural setting for representing imprecise data and poor information. This theory turns out to be quite useful for the purpose of pooling pieces of information stemming from several sources (for instance, several experts, sensors, or databases) . Indeed it looks more flexible than probability theory for the representation of aggregation modes that do not express averaging processes . This paper tentatively explains why possibility theory is appealing for the fusion of imprecise data, and it describes several aggregation modes it allows, along with their underlying assumptions . The existence of adaptive combination rules are pointed out, that take into account the level of conflict between the sources . This approach sounds natural in the pooling of expert opinions . It is suggested here that, under some assumptions, it might also be useful in sensor data fusion .La théorie des possibilités offre un cadre formel naturel pour la représentation de données imprécises, d'informations pauvres. Cette théorie prend tout son intérêt quand il s'agit d'agréger des informations issues de plusieurs sources (par exemple un groupe d'experts, un ensemble hétérogène de capteurs, plusieurs bases de données). En effet elle s'avère être beaucoup plus souple que la théorie des probabilités pour décrire des modes d'agrégation qui ne correspondent pas à des moyennes. Dans cet article on tente d'expliquer pourquoi la théorie des possibilités est intéressante dans le problème de fusion d'informations imprécises, et on décrit les modes d'agrégation qu'elle permet de représenter, avec les hypothèses qui les sous-tendent. On indique notamment l'existence d'opérations de combinaison adaptatives qui prennent en compte le niveau de conflit entre les sources. Cette approche semble justifiée pour l'agrégation d'opinions d'experts. On suggère ici qu'elle peut, dans certaines conditions, être utilisée pour la fusion multi-capteur

Logic-based Technologies for Multi-agent Systems: A Systematic Literature Review

Precisely when the success of artificial intelligence (AI) sub-symbolic techniques makes them be identified with the whole AI by many non-computerscientists and non-technical media, symbolic approaches are getting more and more attention as those that could make AI amenable to human understanding. Given the recurring cycles in the AI history, we expect that a revamp of technologies often tagged as “classical AI” – in particular, logic-based ones will take place in the next few years. On the other hand, agents and multi-agent systems (MAS) have been at the core of the design of intelligent systems since their very beginning, and their long-term connection with logic-based technologies, which characterised their early days, might open new ways to engineer explainable intelligent systems. This is why understanding the current status of logic-based technologies for MAS is nowadays of paramount importance. Accordingly, this paper aims at providing a comprehensive view of those technologies by making them the subject of a systematic literature review (SLR). The resulting technologies are discussed and evaluated from two different perspectives: the MAS and the logic-based ones

Contrôle intelligent de la domotique à partir d'informations temporelles multi sources imprécises et incertaines

La Maison Intelligente est une résidence équipée de technologie informatique qui assiste ses habitant dans les situations diverses de la vie domestique en essayant de gérer de manière optimale leur confort et leur sécurité par action sur la maison. La détection des situations anormales est un des points essentiels d'un système de surveillance à domicile. Ces situations peuvent être détectées en analysant les primitives générées par les étages de traitement audio et par les capteurs de l'appartement. Par exemple, la détection de cris et de bruits sourds (chute d'un objet lourd) dans un intervalle de temps réduit permet d'inférer l'occurrence d'une chute. Le but des travaux de cette thèse est la réalisation d'un contrôleur intelligent relié à tous les périphériques de la maison capable de réagir aux demandes de l'habitant (par commande vocale) et de reconnaître des situations à risque ou détresse. Pour accomplir cet objectif, il est nécessaire de représenter formellement et raisonner sur des informations, le plus souvent temporelles, à des niveaux d'abstraction différents. Le principale défi est le traitement de l'incertitude, l'imprécision, et incomplétude, qui caractérisent les informations dans ce domaine d'application. Par ailleurs, les décisions prises par le contrôleur doivent tenir compte du contexte dans lequel une ordre est donné, ce qui nous place dans l'informatique sensible au contexte. Le contexte est composé des informations de haut niveau tels que la localisation, l'activité en cours de réalisation, la période de la journée. Les recherches présentées dans ce manuscrit peuvent être divisés principalement en trois axes: la réalisation des méthodes d'inférence pour acquérir les informations du contexte(notamment, la localisation de l'habitant y l'activité en cours) à partir des informations incertains, la représentation des connaissances sur l'environnement et les situations à risque, et finalement la prise de décision à partir des informations contextuelles. La dernière partie du manuscrit expose les résultats de la validation des méthodes proposées par des évaluations amenées à la plateforme expérimental Domus.A smart home is a residence featuring ambient intelligence technologies in order to help its dwellers in different situations of common life by trying to manage their comfort and security through the execution of actions over the effectors of the house. Detection of abnormal situations is paramount in the development of surveillance systems. These situations can be detected by the analysis of the traces resulting from audio processing and the data provided by the network of sensors installed in the smart home. For instance, detection of cries along with thuds(fall of a heavy object) in a short time interval can help to infer that the resident has fallen. The goal of the research presented in this thesis is the implementation of an intelligence controller connected with the devices in the house that is able to react to user's commands(through vocal interfaces) and recognize dangerous situations. In order to fulfill this goal, it is necessary to create formal representation and to develop reasoning mechanism over informations that are often temporal and having different levels of abstraction. The main challenge is the processing the uncertainty, imprecision, and incompleteness that characterise this domain of application. Moreover, the decisions taken by the intelligent controller must consider the context in which a user command is given, so this work is made in the area of Context Aware Computing. Context includes high level information such as the location of the dweller, the activity she is making, and the time of the day. The research works presented in this thesis can be divided mainly in three parts: the implementation of inference methods to obtain context information(namely, location and activity) from uncertain information, knowledge representation about the environment and dangerous situations, and finally the development of decision making models that use the inferred context information. The last part of this thesis shows the results from the validation of the proposed methods through experiments performed in an experimental platform, the Domus apartment.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Modified bargaining protocols for automated negotiation in open multi-agent systems

Current research in multi-agent systems (MAS) has advanced to the development of open MAS, which are characterized by the heterogeneity of agents, free exit/entry and decentralized control. Conflicts of interest among agents are inevitable, and hence automated negotiation to resolve them is one of the promising solutions. This thesis studies three modifications on alternating-offer bargaining protocols for automated negotiation in open MAS. The long-term goal of this research is to design negotiation protocols which can be easily used by intelligent agents in accommodating their need in resolving their conflicts. In particular, we propose three modifications: allowing non-monotonic offers during the bargaining (non-monotonic-offers bargaining protocol), allowing strategic delay (delay-based bargaining protocol), and allowing strategic ignorance to augment argumentation when the bargaining comprises argumentation (ignorance-based argumentation-based negotiation protocol). Utility theory and decision-theoretic approaches are used in the theoretical analysis part, with an aim to prove the benefit of these three modifications in negotiation among myopic agents under uncertainty. Empirical studies by means of computer simulation are conducted in analyzing the cost and benefit of these modifications. Social agents, who use common human bargaining strategies, are the subjects of the simulation. In general, we assume that agents are bounded rational with various degrees of belief and trust toward their opponents. In particular in the study of the non-monotonic-offers bargaining protocol, we assume that our agents have diminishing surplus. We further assume that our agents have increasing surplus in the study of delay-based bargaining protocol. And in the study of ignorance-based argumentation-based negotiation protocol, we assume that agents may have different knowledge and use different ontologies and reasoning engines. Through theoretical analysis under various settings, we show the benefit of allowing these modifications in terms of agents’ expected surplus. And through simulation, we show the benefit of allowing these modifications in terms of social welfare (total surplus). Several implementation issues are then discussed, and their potential solutions in terms of some additional policies are proposed. Finally, we also suggest some future work which can potentially improve the reliability of these modifications

Probabilistic Finite Domains

This thesis presents a set of programming constructs that are capable of modelling probabilistic concepts and of computing with such concepts. The main objectives are to provide: a theoretically sound, practically achievable and notationally intuitive formalism. The probabilistic programming constructs are presented in the form of a system called probabilistic finite domains, which enhances the Logic Programming paradigm with a novel constraint solver. In doing so, we are able to take advantage of the knowledge representation power of probability. In particular we investigate: first, the duality of the two interpretations of probability to the problems researchers face when wishing to create a probabilistic formalism and second, the use of probability as a unifying model for computational derivations. Some programming examples and a simple implementation are also described

Phishing website detection using intelligent data mining techniques. Design and development of an intelligent association classification mining fuzzy based scheme for phishing website detection with an emphasis on E-banking.

Phishing techniques have not only grown in number, but also in sophistication. Phishers might have a lot of approaches and tactics to conduct a well-designed phishing attack. The targets of the phishing attacks, which are mainly on-line banking consumers and payment service providers, are facing substantial financial loss and lack of trust in Internet-based services. In order to overcome these, there is an urgent need to find solutions to combat phishing attacks. Detecting phishing website is a complex task which requires significant expert knowledge and experience. So far, various solutions have been proposed and developed to address these problems. Most of these approaches are not able to make a decision dynamically on whether the site is in fact phished, giving rise to a large number of false positives. This is mainly due to limitation of the previously proposed approaches, for example depending only on fixed black and white listing database, missing of human intelligence and experts, poor scalability and their timeliness. In this research we investigated and developed the application of an intelligent fuzzy-based classification system for e-banking phishing website detection. The main aim of the proposed system is to provide protection to users from phishers deception tricks, giving them the ability to detect the legitimacy of the websites. The proposed intelligent phishing detection system employed Fuzzy Logic (FL) model with association classification mining algorithms. The approach combined the capabilities of fuzzy reasoning in measuring imprecise and dynamic phishing features, with the capability to classify the phishing fuzzy rules. Different phishing experiments which cover all phishing attacks, motivations and deception behaviour techniques have been conducted to cover all phishing concerns. A layered fuzzy structure has been constructed for all gathered and extracted phishing website features and patterns. These have been divided into 6 criteria and distributed to 3 layers, based on their attack type. To reduce human knowledge intervention, Different classification and association algorithms have been implemented to generate fuzzy phishing rules automatically, to be integrated inside the fuzzy inference engine for the final phishing detection. Experimental results demonstrated that the ability of the learning approach to identify all relevant fuzzy rules from the training data set. A comparative study and analysis showed that the proposed learning approach has a higher degree of predictive and detective capability than existing models. Experiments also showed significance of some important phishing criteria like URL & Domain Identity, Security & Encryption to the final phishing detection rate. Finally, our proposed intelligent phishing website detection system was developed, tested and validated by incorporating the scheme as a web based plug-ins phishing toolbar. The results obtained are promising and showed that our intelligent fuzzy based classification detection system can provide an effective help for real-time phishing website detection. The toolbar successfully recognized and detected approximately 92% of the phishing websites selected from our test data set, avoiding many miss-classified websites and false phishing alarms

Conférence Nationale d'Intelligence Artificielle Année 2020

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