Real-time Tactical and Strategic Sales Management for Intelligent Agents Guided By Economic Regimes

Many enterprises that participate in dynamic markets need to make product pricing and inventory resource utilization decisions in real-time. We describe a family of statistical models that address these needs by combining characterization of the economic environment with the ability to predict future economic conditions to make tactical (short-term) decisions, such as product pricing, and strategic (long-term) decisions, such as level of finished goods inventories. Our models characterize economic conditions, called economic regimes, in the form of recurrent statistical patterns that have clear qualitative interpretations. We show how these models can be used to predict prices, price trends, and the probability of receiving a customer order at a given price. These “regime†models are developed using statistical analysis of historical data, and are used in real-time to characterize observed market conditions and predict the evolution of market conditions over multiple time scales. We evaluate our models using a testbed derived from the Trading Agent Competition for Supply Chain Management (TAC SCM), a supply chain environment characterized by competitive procurement and sales markets, and dynamic pricing. We show how regime models can be used to inform both short-term pricing decisions and longterm resource allocation decisions. Results show that our method outperforms more traditional shortand long-term predictive modeling approaches.dynamic pricing;trading agent competition;agent-mediated electronic commerce;dynamic markets;economic regimes;enabling technologies;price forecasting;supply-chain

Real-time Tactical and Strategic Sales Management for Intelligent Agents Guided By Economic Regimes

Many enterprises that participate in dynamic markets need to make product pricing and inventory resource utilization decisions in real-time. We describe a family of statistical models that address these needs by combining characterization of the economic environment with the ability to predict future economic conditions to make tactical (short-term) decisions, such as product pricing, and strategic (long-term) decisions, such as level of finished goods inventories. Our models characterize economic conditions, called economic regimes, in the form of recurrent statistical patterns that have clear qualitative interpretations. We show how these models can be used to predict prices, price trends, and the probability of receiving a customer order at a given price. These “regime” models are developed using statistical analysis of historical data, and are used in real-time to characterize observed market conditions and predict the evolution of market conditions over multiple time scales. We evaluate our models using a testbed derived from the Trading Agent Competition for Supply Chain Management (TAC SCM), a supply chain environment characterized by competitive procurement and sales markets, and dynamic pricing. We show how regime models can be used to inform both short-term pricing decisions and longterm resource allocation decisions. Results show that our method outperforms more traditional shortand long-term predictive modeling approaches

Algoritmos de deteção de comportamento de indivíduos com autismo: análise comparativa

A elevada prevalência de doenças relacionadas com o autismo e a necessidade de descoberta desta patologia de difícil diagnóstico e convivência, levou a que seja imprescindível o desenvolvimento de mecanismos de deteção e avaliação da atividade de pacientes. Esta tese tem como principal objetivo colmatar estas necessidades. Os algoritmos testados são direcionados à melhoria da monitorização de doentes com autismo e com necessidades especiais. Estes têm como principal objetivo a utilização numa casa direcionada à terapia e convivência de doentes com autismo. Através destas técnicas será possível melhorar a qualidade de vida dos pacientes e responder a emergências que possam surgir. Os dados provenientes de sensores como a câmara Kinect vão servir para detetar movimentos estereotipados característicos de doenças do espectro do autismo. Assim podem ser monitorizadas atividades anormais e por consequência antecipar algum tipo de anomalia. Por outro lado será possível avaliar a evolução do doente ao longo de uma série de tratamentos e terapias. Neste projeto foi avaliada a performance de vários algoritmos de deteção e avaliação de movimentos adaptados às necessidades presentes. Foi também conduzido um estudo sobre as tecnologias de reconhecimento de expressões faciais e identificação de pessoas pela face. Os algoritmos foram testados com movimentos de pessoas sem doenças do espetro do autismo devido à maior facilidade de trabalhar neste contexto. Posteriormente transpõem-se as tecnologias para onde forem necessárias

Towards adaptive anomaly detection systems using boolean combination of hidden Markov models

Anomaly detection monitors for significant deviations from normal system behavior. Hidden Markov Models (HMMs) have been successfully applied in many intrusion detection applications, including anomaly detection from sequences of operating system calls. In practice, anomaly detection systems (ADSs) based on HMMs typically generate false alarms because they are designed using limited representative training data and prior knowledge. However, since new data may become available over time, an important feature of an ADS is the ability to accommodate newly-acquired data incrementally, after it has originally been trained and deployed for operations. Incremental re-estimation of HMM parameters raises several challenges. HMM parameters should be updated from new data without requiring access to the previously-learned training data, and without corrupting previously-learned models of normal behavior. Standard techniques for training HMM parameters involve iterative batch learning, and hence must observe the entire training data prior to updating HMM parameters. Given new training data, these techniques must restart the training procedure using all (new and previously-accumulated) data. Moreover, a single HMM system for incremental learning may not adequately approximate the underlying data distribution of the normal process, due to the many local maxima in the solution space. Ensemble methods have been shown to alleviate knowledge corruption, by combining the outputs of classifiers trained independently on successive blocks of data. This thesis makes contributions at the HMM and decision levels towards improved accuracy, efficiency and adaptability of HMM-based ADSs. It first presents a survey of techniques found in literature that may be suitable for incremental learning of HMM parameters, and assesses the challenges faced when these techniques are applied to incremental learning scenarios in which the new training data is limited and abundant. Consequently, An efficient alternative to the Forward-Backward algorithm is first proposed to reduce the memory complexity without increasing the computational overhead of HMM parameters estimation from fixed-size abundant data. Improved techniques for incremental learning of HMM parameters are then proposed to accommodate new data over time, while maintaining a high level of performance. However, knowledge corruption caused by a single HMM with a fixed number of states remains an issue. To overcome such limitations, this thesis presents an efficient system to accommodate new data using a learn-and-combine approach at the decision level. When a new block of training data becomes available, a new pool of base HMMs is generated from the data using a different number of HMM states and random initializations. The responses from the newly-trained HMMs are then combined to those of the previously-trained HMMs in receiver operating characteristic (ROC) space using novel Boolean combination (BC) techniques. The learn-and-combine approach allows to select a diversified ensemble of HMMs (EoHMMs) from the pool, and adapts the Boolean fusion functions and thresholds for improved performance, while it prunes redundant base HMMs. The proposed system is capable of changing its desired operating point during operations, and this point can be adjusted to changes in prior probabilities and costs of errors. During simulations conducted for incremental learning from successive data blocks using both synthetic and real-world system call data sets, the proposed learn-and-combine approach has been shown to achieve the highest level of accuracy than all related techniques. In particular, it can sustain a significantly higher level of accuracy than when the parameters of a single best HMM are re-estimated for each new block of data, using the reference batch learning and the proposed incremental learning techniques. It also outperforms static fusion techniques such as majority voting for combining the responses of new and previously-generated pools of HMMs. Ensemble selection techniques have been shown to form compact EoHMMs for operations, by selecting diverse and accurate base HMMs from the pool while maintaining or improving the overall system accuracy. Pruning has been shown to prevents pool sizes from increasing indefinitely with the number of data blocks acquired over time. Therefore, the storage space for accommodating HMMs parameters and the computational costs of the selection techniques are reduced, without negatively affecting the overall system performance. The proposed techniques are general in that they can be employed to adapt HMM-based systems to new data, within a wide range of application domains. More importantly, the proposed Boolean combination techniques can be employed to combine diverse responses from any set of crisp or soft one- or two-class classifiers trained on different data or features or trained according to different parameters, or from different detectors trained on the same data. In particular, they can be effectively applied when training data is limited and test data is imbalanced

Entwicklung eines intelligenten kognitiven Assistenzsystems für dynamische Produktionsumgebungen - am Beispiel eines Assistenzsystems zur Unterstützung von Mitarbeitern in der Nacharbeit

Kognitive Assistenzsysteme in der Produktion unterstützen Mitarbeiter bei der Bewältigung manueller Tätigkeiten. Im laufenden (Montage-)Prozess versorgen sie den Mitarbeiter mit Informationen zur auszuführenden Tätigkeit. Fortschrittlichere Assistenzsysteme überprüfen zugleich den Prozess und melden etwaige Fehler zurück. Herkömmliche Kognitive Assistenzsysteme eignen sich für lineare Prozessabfolgen, wie sie bspw. in der Linienmontage vorzufinden sind. In Produktionsbereichen, wo Prozesse von unterschiedlichen Einflussfaktoren abhängig sind, eignen sich bisherige Assistenzsysteme kaum. In dieser Arbeit wird die Entwicklung eines Kognitiven Assistenzsystems für den Einsatz in dynamischen Produktionsbereichen am Beispiel der Nacharbeit beschrieben. Grundlage hierfür ist eine Graphenstruktur, die den Produktfortschritt abbildet. Diese wird mit unterschiedlichen Prozessdaten angereichert und erlaubt die Generierung einer Prozessliste mit variablem Ziel. Diese Liste steht in digitaler Form zur Verfügung und steuert das Kognitive Assistenzsystem an. Zudem werden am Assistenzsystem Daten aufgenommen. Diese ermöglichen den Rückschluss auf die Vertrautheit einer Person mit bestimmten Montageprozessen. Diese Information wird genutzt, um Aufträge den Mitarbeitern derart zuzuweisen, dass Auftragsbestände besser reduziert werden. Hierzu werden Metaheuristiken genutzt, da auch eine Vielzahl von Kombinationsmöglichkeiten (Auftrag an Mitarbeiter) zu berücksichtigen ist.Cognitive Assistance Systems used in production provide operators with valuable in-formation about the assembly process to assist in decision-making. Some systems can even check processes and give feedback in case of any errors. Commonly available assistance systems work well in sequentially organized processes like those of an as-sembly line. However, they are barely able to operate in more dynamic environments such as rework areas, where the processes depend on numerous factors. This work aims to describe a Cognitive Assistance System suitable for use in these dynamic pro-duction environments. A graph structure poses as the backbone for this Cognitive As-sistance System as it represents the production stream. This graph structure is then enriched with process-related data and is able to generate a digital process list that serves as the program for the Cognitive Assistance System. The data collected by the system is used to determine the operator’s familiarity with the task to be performed. The information is then used to assign a suitable rework job to the operator in order to reduce the amount of pending rework jobs. Meta-heuristics help to handle the huge number of combinations (job-to-operator) that this system would generate

Multi-classifier systems for off-line signature verification

Handwritten signatures are behavioural biometric traits that are known to incorporate a considerable amount of intra-class variability. The Hidden Markov Model (HMM) has been successfully employed in many off-line signature verification (SV) systems due to the sequential nature and variable size of the signature data. In particular, the left-to-right topology of HMMs is well adapted to the dynamic characteristics of occidental handwriting, in which the hand movements are always from left to right. As with most generative classifiers, HMMs require a considerable amount of training data to achieve a high level of generalization performance. Unfortunately, the number of signature samples available to train an off-line SV system is very limited in practice. Moreover, only random forgeries are employed to train the system, which must in turn to discriminate between genuine samples and random, simple and skilled forgeries during operations. These last two forgery types are not available during the training phase. The approaches proposed in this Thesis employ the concept of multi-classifier systems (MCS) based on HMMs to learn signatures at several levels of perception. By extracting a high number of features, a pool of diversified classifiers can be generated using random subspaces, which overcomes the problem of having a limited amount of training data. Based on the multi-hypotheses principle, a new approach for combining classifiers in the ROC space is proposed. A technique to repair concavities in ROC curves allows for overcoming the problem of having a limited amount of genuine samples, and, especially, for evaluating performance of biometric systems more accurately. A second important contribution is the proposal of a hybrid generative-discriminative classification architecture. The use of HMMs as feature extractors in the generative stage followed by Support Vector Machines (SVMs) as classifiers in the discriminative stage allows for a better design not only of the genuine class, but also of the impostor class. Moreover, this approach provides a more robust learning than a traditional HMM-based approach when a limited amount of training data is available. The last contribution of this Thesis is the proposal of two new strategies for the dynamic selection (DS) of ensemble of classifiers. Experiments performed with the PUCPR and GPDS signature databases indicate that the proposed DS strategies achieve a higher level of performance in off-line SV than other reference DS and static selection (SS) strategies from literature