63 research outputs found

    Architecting system of systems: artificial life analysis of financial market behavior

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    This research study focuses on developing a framework that can be utilized by system architects to understand the emergent behavior of system architectures. The objective is to design a framework that is modular and flexible in providing different ways of modeling sub-systems of System of Systems. At the same time, the framework should capture the adaptive behavior of the system since evolution is one of the key characteristics of System of Systems. Another objective is to design the framework so that humans can be incorporated into the analysis. The framework should help system architects understand the behavior as well as promoters or inhibitors of change in human systems. Computational intelligence tools have been successfully used in analysis of Complex Adaptive Systems. Since a System of Systems is a collection of Complex Adaptive Systems, a framework utilizing combination of these tools can be developed. Financial markets are selected to demonstrate the various architectures developed from the analysis framework --Introduction, page 3

    Evolutionary Optimization of ZIP60: A Controlled Explosion in Hyperspace

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    The “ZIP” adaptive trading algorithm has been demonstrated to out-perform human traders in experimental studies of continuous double auction (CDA) markets. The original ZIP algorithm requires the values of eight control parameters to be set correctly. A new extension of the ZIP algorithm, called ZIP60, requires the values of 60 parameters to be set correctly. ZIP60 is shown here to produce significantly better results than the original ZIP (called “ZIP8” hereafter), for negligable additional computational costs. A genetic algorithm (GA) is used to search the 60-dimensional ZIP60 parameter space, and it finds parameter vectors that yield ZIP60 traders with mean scores significantly better than those of ZIP8s. This paper shows that the optimizing evolutionary search works best when the GA itself controls the dimensionality of the search-space, so that the search commences in an 8-d space and thereafter the dimensionality of the search-space is gradually increased by the GA until it is exploring a 60-d space. Furthermore, the results from ZIP60 cast some doubt on prior ZIP8 results concerning the evolution of new ‘hybrid’ auction mechanisms that appeared to be better than the CDA

    Distributed and Centralized Task Allocation: When and Where to Use Them

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    Self-organisation is frequently advocated as the solution for managing large, dynamic systems. Distributed algorithms are implicitly designed for infinitely large problems, while small systems are regarded as being controllable using traditional, centralised approaches. Many real-world systems, however, do not fit conveniently into these "small" or "large" categories, resulting in a range of cases where the optimal solution is ambiguous. This difficulty is exacerbated by enthusiasts of either approach constructing problems that suit their preferred control architecture. We address this ambiguity by building an abstract model of task allocation in a community of specialised agents. We are inspired by the problem of work distribution in distributed satellite systems, but the model is also relevant to the resource allocation problems in distributed robotics, autonomic computing and wireless sensor networks. We compare the behaviour of a self-organising, market-based task allocation strategy to a classical approach that uses a central controller with global knowledge. The objective is not to prove one mechanism inherently superior to the other; instead we are interested in the regions of problem space where each of them dominates. Simulation is used to explore the trade-off between energy consumption and robustness in a system of intermediate size, with fixed communication costs and varying rates of component failure. We identify boundaries between regions in the parameter space where one or the other architecture will be favoured. This allows us to derive guidelines for system designers, thus contributing to the development of a disciplined approach to controlling distributed systems using self-organising mechanisms

    Using MapReduce Streaming for Distributed Life Simulation on the Cloud

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    Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

    Graduated embodiment for sophisticated agent evolution and optimization.

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    XCS Algorithms for a Linear Combination of Discounted and Undiscounted Reward Markovian Decision Processes

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    RÉSUMÉ : Plusieurs Ă©tudes ont montrĂ© que combiner certains prĂ©dicteurs ensemble peut amĂ©liorer la justesse de la prĂ©diction dans certains domaines comme la psychologie, les statistiques ou les sciences du management. Toutefois, aucune de ces Ă©tudes n'ont testĂ© la combinaison de techniques d'apprentissage par renforcement. Notre Ă©tude vise Ă  dĂ©velopper un algorithme basĂ© sur deux algorithmes qui sont des formes approximatives d'apprentissage par renforcement rĂ©pĂ©tĂ©s dans XCS. Cet algorithme, MIXCS, est une combinaison des techniques de Q-learning et de R-learning pour calculer la combinaison linĂ©aire du payoff rĂ©sultant des actions de l'agent, et aussi la correspondance entre la prĂ©diction au niveau du systĂšme et la valeur rĂ©elle des actions de l'agent. MIXCS fait une prĂ©vision du payoff espĂ©rĂ© pour chacune des actions disponibles pour l'agent. Nous avons testĂ© MIXCS dans deux environnements Ă  deux dimensions, Environment1 et Environment2, qui reproduisent les actions possibles dans un marchĂ© financier (acheter, vendre, ne rien faire) pour Ă©valuer les performances d'un agent qui veut obtenir un profit espĂ©rĂ©. Nous avons calculĂ© le payoff optimal moyen dans nos deux environnements et avons comparĂ© avec les rĂ©sultats obtenus par MIXCS. Nous avons obtenu deux rĂ©sultats. En premier, les rĂ©sultats de MIXCS sont semblables au payoff optimal moyen pour Environments1, mais pas pour Environment2. DeuxiĂšmement, l'agent obtient le payoff optimal moyen quand il prend l'action "vendre" dans les deux environnements.----------ABSTRACT : Many studies have shown that combining individual predictors improved the accuracy of predictions in different domains such as psychology, statistics and management sciences. However, these studies have not tested the combination of reinforcement learning techniques. This study aims to develop an algorithm based on two iterative approximate forms of reinforcement learning algorithm in XCS. This algorithm, named MIXCS, is a combination of Q-learning and R-learning techniques to compute the linear combination payoff and the correspondence between the system prediction and the action value. As such, MIXCS predicts the payoff to be expected for each possible action. We test MIXCS in two two-dimensional grids called Environment1 and Environment2, which represent financial markets actions of buying, selling and holding to evaluate the performance of an agent as a trader to gain the desired profit. We calculate the optimum average payoff to predict the value of the next movement in both Environment1 and Environment2 and compare the results with those obtained with MIXCS. The results show that the performance of MIXCS is close to optimum average reward in Environment1, but not in Environment2. Also, the agent reaches the maximum reward by taking selling actions in both Environments

    CellEVAC: an adaptive guidance system for crowd evacuation through behavioral optimization

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    A critical aspect of crowds' evacuation processes is the dynamism of individual decision making. Identifying optimal strategies at an individual level may improve both evacuation time and safety, which is essential for developing efficient evacuation systems. Here, we investigate how to favor a coordinated group dynamic through optimal exit-choice instructions using behavioral strategy optimization. We propose and evaluate an adaptive guidance system (Cell-based Crowd Evacuation, CellEVAC) that dynamically allocates colors to cells in a cellbased pedestrian positioning infrastructure, to provide efficient exit-choice indications. The operational module of CellEVAC implements an optimized discrete-choice model that integrates the influential factors that would make evacuees adapt their exit choice. To optimize the model, we used a simulation?optimization modeling framework that integrates microscopic pedestrian simulation based on the classical Social Force Model. In the majority of studies, the objective has been to optimize evacuation time. In contrast, we paid particular attention to safety by using Pedestrian Fundamental Diagrams that model the dynamics of the exit gates. CellEVAC has been tested in a simulated real scenario (Madrid Arena) under different external pedestrian flow patterns that simulate complex pedestrian interactions. Results showed that CellEVAC outperforms evacuation processes in which the system is not used, with an exponential improvement as interactions become complex. We compared our system with an existing approach based on Cartesian Genetic Programming. Our system exhibited a better overall performance in terms of safety, evacuation time, and the number of revisions of exit-choice decisions. Further analyses also revealed that Cartesian Genetic Programming generates less natural pedestrian reactions and movements than CellEVAC. The fact that the decision logic module is built upon a behavioral model seems to favor a more natural and effective response. We also found that our proposal has a positive influence on evacuations even for a low compliance rate (40%).Ministerio de EconomĂ­a y Competitivida
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