3,979 research outputs found
Generalised additive multiscale wavelet models constructed using particle swarm optimisation and mutual information for spatio-temporal evolutionary system representation
A new class of generalised additive multiscale wavelet models (GAMWMs) is introduced for high dimensional spatio-temporal evolutionary (STE) system identification. A novel two-stage hybrid learning scheme is developed for constructing such an additive wavelet model. In the first stage, a new orthogonal projection pursuit (OPP) method, implemented using a particle swarm optimisation(PSO) algorithm, is proposed for successively augmenting an initial coarse wavelet model, where relevant parameters of the associated wavelets are optimised using a particle swarm optimiser. The resultant network model, obtained in the first stage, may however be a redundant model. In the second stage, a forward orthogonal regression (FOR) algorithm, implemented using a mutual information method, is then applied to refine and improve the initially constructed wavelet model. The proposed two-stage hybrid method can generally produce a parsimonious wavelet model, where a ranked list of wavelet functions, according to the capability of each wavelet to represent the total variance in the desired system output signal is produced. The proposed new modelling framework is applied to real observed images, relative to a chemical reaction exhibiting a spatio-temporal evolutionary behaviour, and the associated identification results show that the new modelling framework is applicable and effective for handling high dimensional identification problems of spatio-temporal evolution sytems
Towards adaptive multi-robot systems: self-organization and self-adaptation
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugÀnglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible
Neuroplasticity, neural reuse, and the language module
What conception of mental architecture can survive the
evidence of neuroplasticity and neural reuse in the human brain?
In particular, what sorts of modules are compatible with this
evidence? I aim to show how developmental and adult
neuroplasticity, as well as evidence of pervasive neural reuse,
forces us to revise the standard conception of modularity and
spells the end of a hardwired and dedicated language module. I
argue from principles of both neural reuse and neural redundancy
that language is facilitated by a composite of modules (or
module-like entities), few if any of which are likely to be
linguistically special, and that neuroplasticity provides
evidence that (in key respects and to an appreciable extent) few
if any of them ought to be considered developmentally robust,
though their development does seem to be constrained by features
intrinsic to particular regions of cortex (manifesting as
domain-specific predispositions or acquisition biases). In the
course of doing so I articulate a schematically and
neurobiologically precise framework for understanding modules and
their supramodular interactions
Assessing architectural evolution: A case study
This is the post-print version of the Article. The official published can be accessed from the link below - Copyright @ 2011 SpringerThis paper proposes to use a historical perspective on generic laws, principles,
and guidelines, like Lehmanâs software evolution laws and Martinâs design principles, in order to achieve a multi-faceted process and structural assessment of a systemâs architectural evolution. We present a simple structural model with associated historical metrics and
visualizations that could form part of an architectâs dashboard. We perform such an assessment for the Eclipse SDK, as a case study of a large, complex, and long-lived system for which sustained effective architectural evolution is paramount. The twofold aim of checking generic principles on a well-know system is, on the one hand,
to see whether there are certain lessons that could be learned for best practice of architectural evolution, and on the other hand to get more insights about the applicability of such principles. We find that while the Eclipse SDK does follow several of the laws and principles, there are some deviations, and we discuss areas of architectural improvement and limitations of the assessment approach
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An agent-based fuzzy cognitive map approach to the strategic marketing planning for industrial firms
This is the post-print version of the final paper published in Industrial Marketing Management. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Industrial marketing planning is a typical example of an unstructured decision making problem due to the large number of variables to consider and the uncertainty imposed on those variables. Although abundant studies identified barriers and facilitators of effective industrial marketing planning in practice, the literature still lacks practical tools and methods that marketing managers can use for the task. This paper applies fuzzy cognitive maps (FCM) to industrial marketing planning. In particular, agent based inference method is proposed to overcome dynamic relationships, time lags, and reusability issues of FCM evaluation. MACOM simulator also is developed to help marketing managers conduct what-if scenarios to see the impacts of possible changes on the variables defined in an FCM that represents industrial marketing planning problem. The simulator is applied to an industrial marketing planning problem for a global software service company in South Korea. This study has practical implication as it supports marketing managers for industrial marketing planning that has large number of variables and their causeâeffect relationships. It also contributes to FCM theory by providing an agent based method for the inference of FCM. Finally, MACOM also provides academics in the industrial marketing management discipline with a tool for developing and pre-verifying a conceptual model based on qualitative knowledge of marketing practitioners.Ministry of Education, Science and Technology (Korea
Learning Representations in Model-Free Hierarchical Reinforcement Learning
Common approaches to Reinforcement Learning (RL) are seriously challenged by
large-scale applications involving huge state spaces and sparse delayed reward
feedback. Hierarchical Reinforcement Learning (HRL) methods attempt to address
this scalability issue by learning action selection policies at multiple levels
of temporal abstraction. Abstraction can be had by identifying a relatively
small set of states that are likely to be useful as subgoals, in concert with
the learning of corresponding skill policies to achieve those subgoals. Many
approaches to subgoal discovery in HRL depend on the analysis of a model of the
environment, but the need to learn such a model introduces its own problems of
scale. Once subgoals are identified, skills may be learned through intrinsic
motivation, introducing an internal reward signal marking subgoal attainment.
In this paper, we present a novel model-free method for subgoal discovery using
incremental unsupervised learning over a small memory of the most recent
experiences (trajectories) of the agent. When combined with an intrinsic
motivation learning mechanism, this method learns both subgoals and skills,
based on experiences in the environment. Thus, we offer an original approach to
HRL that does not require the acquisition of a model of the environment,
suitable for large-scale applications. We demonstrate the efficiency of our
method on two RL problems with sparse delayed feedback: a variant of the rooms
environment and the first screen of the ATARI 2600 Montezuma's Revenge game
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