No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Virtual worlds as fuzzy cognitive maps
Abstract
Fuzzy cognitive maps (FCMs) can structure virtual worlds. FCMs
link causal events, values, goals, and trends in a fuzzy feedback
dynamical system. They direct actors in virtual worlds as the actors
react to events and to one another. In nested FCMs each causal concept
can control its own FCM. This combines levels of fuzzy systems that can
choose goals or move objects. Adaptive FCMs change as causal patterns
change. They adapt with differential Hebbian learning. FCMs are applied
to an undersea virtual world of dolphins
... These CMs help with the identification of key concepts and their interactions in a system of interest, however, no dynamical analysis of the system through time is possible due to the qualitative nature and uncertainty in numerical data obtained, which makes the formulation of mathematical models difficult [3]. Nearly a decade later, Kosko [4] introduced Fuzzy Cognitive Maps (FCMs) as a tool to model the behaviour of qualitative systems [3,5,6]. Like CMs, FCMs are a graphical representation of a system with nodes representing concepts and weighted links between these nodes explaining their relationship. ...
... FCMs intended goal is to model qualitative SD. However, FCMs proposed by Kosko [4] and his later works [5,6,31,38,39,40,41] have inherent limitations, which, several scholar has tried to address. Papageorgiou and Salmeron [8] has reviewed these advances from a SD perspective while Carvalho [3] and Nair et al. [11] have reviewed these from the perspective of modelling complex qualitative SD. ...
... The unit-time the system is modelled is six hours. The system or each perceptron layer is simulated using the Equation 5. This is similar to Equation 1, however, this method of simulation was designed by Yesil et al. [7] and uses fuzzy algebra (or fuzzy multiplication) to retain fuzziness and ambiguity during simulations [2]. ...
Fuzzy Cognitive Maps (FCMs) have been used to quantitatively model the dynamics of complex systems and predict their behaviours. However, they are usually unable to address the issues arising from time lags between causes and effects. Accordingly, Generalized Fuzzy Cognitive Maps (GFCMs) have been introduced to overcome this problem. This article deals with a breed of GFCMs that addresses time lags between cause(s) and effect(s), demonstrated by a case-study that deals with the social, economic and technological consequences of heavy rainfall in Kampala, Uganda. The results show that the inclusion of time lags alters both, the final steady-state values of the social, economic and technological consequences of heavy rainfall and the time taken to stabilise. Thus, the inclusion of time lags increases the reliability of GFCMs as a means to quantitatively model the dynamics of complex systems.
... Нечеткие когнитивные карты изучались и использовались в различных областях техники и точных наук [2]. Их роль особенно важна при исследовании поведения сложных динамических систем [3], [18], [5]. Это связано с тем, что неопределенность человеческих знаний влияет на определение и обработку данных в таких системах [6]. ...
... ( , ∨, ∧) -решетка; 3 Векторные решетки еще называются -пространствами, которые были введены Л. В. Канторовичем и др. в [13] 4 возможен вариант полукольца 5 В кольце без делителей нуля это условие превращается в условие изотонности: ...
Multi-valued neural networks and cognitive maps generalize similar fuzzy concepts to the case of lattice values of weights and variables. In this case, the formulas use lattice operations instead of the sum, product, and T-norm. However, on the lattice it is additionally possible to define the multiplication and/or the sum externally. The article discusses various general methods for such a definition and the application of their particular cases in modeling the operation of a photo-wind generator using a cognitive map and predicting the stability of a model process using associative memory. (in Russian)
... Differential Hebbian Learning (DHL) was suggested in [61,9] based on the Hebbian theory [62]; it is the first Hebbian-Based algorithm proposed by Dickerson and Kosko. In DHL, the weight matrices are modified when the value of corresponding concepts changes. ...
... Differential Hebbian Learning (DHL) [61,9] 10 Balance Differential Algorithm (BDA) [ ...
Among various soft computing approaches for time series forecasting, Fuzzy Cognitive Maps (FCM) have shown remarkable results as a tool to model and analyze the dynamics of complex systems. FCM have similarities to recurrent neural networks and can be classified as a neuro-fuzzy method. In other words, FCMs are a mixture of fuzzy logic, neural network, and expert system aspects, which act as a powerful tool for simulating and studying the dynamic behavior of complex systems. The most interesting features are knowledge interpretability, dynamic characteristics and learning capability. The goal of this survey paper is mainly to present an overview on the most relevant and recent FCM-based time series forecasting models proposed in the literature. In addition, this article considers an introduction on the fundamentals of FCM model and learning methodologies. Also, this survey provides some ideas for future research to enhance the capabilities of FCM in order to cover some challenges in the real-world experiments such as handling non-stationary data and scalability issues. Moreover, equipping FCMs with fast learning algorithms is one of the major concerns in this area.
... Differential Hebbian Learning (DHL) was suggested in [61,9] based on the Hebbian theory [62]; it is the first Hebbian-Based algorithm proposed by Dickerson and Kosko. In DHL, the weight matrices are modified when the value of corresponding concepts changes. ...
... Differential Hebbian Learning (DHL) [61,9] 10 Balance Differential Algorithm (BDA) [ ...
Fuzzy Cognitive Maps (FCMs) have emerged as an interpretable signed weighted digraph method consisting of nodes (concepts) and weights which represent the dependencies among the concepts. Although FCMs have attained considerable achievements in various time series prediction applications, designing an FCM model with time-efficient training method is still an open challenge. Thus, this paper introduces a novel univariate time series forecasting technique, which is composed of a group of randomized high order FCM models labeled R-HFCM. The novelty of the proposed R-HFCM model is relevant to merging the concepts of FCM and Echo State Network (ESN) as an efficient and particular family of Reservoir Computing (RC) models, where the least squares algorithm is applied to train the model. From another perspective, the structure of R-HFCM consists of the input layer, reservoir layer, and output layer in which only the output layer is trainable while the weights of each sub-reservoir components are selected randomly and keep constant during the training process. As case studies, this model considers solar energy forecasting with public data for Brazilian solar stations as well as Malaysia dataset, which includes hourly electric load and temperature data of the power supply company of the city of Johor in Malaysia. The experiment also includes the effect of the map size, activation function, the presence of bias and the size of the reservoir on the accuracy of R-HFCM method. The obtained results confirm the outperformance of the proposed R-HFCM model in comparison to the other methods. This study provides evidence that FCM can be a new way to implement a reservoir of dynamics in time series modelling.
... FCMs like CMs is a graphical representation of a system with nodes representing concepts and weighted links between these nodes explaining their relationship. FCMs, as explained in [5,12,13,14,15,16] are increasingly been used to model and analyse the behaviour of qualitative systems [17,18,19,20,21,22,23,24,25,26,27,28,29,30]. Over the last 30 years, this fuzzy cognitive mapping (FCM) approach has become increasingly popular due to the ease of design and the low computational requirements for simulating qualitative SD [31,32], largely using two forms of application, and connected data use and generation: 1) the deductive approach-employing knowledge that is gathered by interviewing experts from the area of application; 2) the inductive approach-an automated and semi-automated approach designed for learning FCM rules based on historical data [21,33,24,34,35]. ...
... Kosko [7] introduced fuzzy cognitive maps (FCMs) as a tool for capturing and explaining the behaviour of dynamic qualitative systems [7][8][9]. FCMs, as explained in [7,[10][11][12] are increasingly been used to model and analyse the behaviour of qualitative systems [13][14][15][16][17][18][19][20]. Over the last 30 years, this fuzzy cognitive mapping (FCM) approach has become increasingly popular due to the ease of design and the low computational requirements for simulating social system dynamics [21,22] , largely using two forms of application, and connected data use and generation: (1) the deductive approachemploying knowledge that is gathered by interviewing experts from the area of application; (2) the inductive approach-an automated and semi-automated approach designed for learning FCM rules based on historical data [13,16,19,20,[23][24][25][26][27][28]. ...
... biological system [1][2][3], climate models [4], traffic systems [5]), multi-agent system [6,7]; social simulations [8][9][10] and fuzzy cognitive maps (FCM) (e.g. modeling of virtual worlds [11], economic & business [12,13] medicine [14][15][16][17], production system [18][19][20]. In this paper, since a FCM is considered a useful tool to modeling dynamical system as a decision support and forecast tool, our focus is on FCM. ...
... (5a), the rule could be represented as in eqs. (10)(11). ...
In the search of modeling methodologies for complex systems various attempts have been made, and so far, all have been inadequate in one thing or another leading the pathway open for the next better tool. Fuzzy cognitive maps have been one of such tools, although mainly used for decision making in what-if scenarios, they can also be used to represent complex systems. In this paper, we define an approach of fuzzy inference system based fuzzy cognitive map for modeling dynamic systems, where the complex model is defined by means of fuzzy IF-THEN rules which represent the behavior of the system in an easy to understand format, therefore facilitating a tool for complex system design. Various examples of dynamic systems are shown used as a means to demonstrate the ease of use, design and capability of the proposed approach.
... This was introduced to model the intensity of the change when event occurs only to some degree (Kosko, 1986). More specifically, a FCM model is a graph-based knowledge representation (Dickerson & Kosko, 1993) which models a static or dynamic system using causal dependencies between a set of n nodes V = (v 1 , v 2 , …, v n ). The intensity of causal connection between pair of nodes < v i , v j > is evaluated by assigning fuzzy weights (w i → j ∈ [− 1, 1]), where v i is the pre-synaptic (causal) node and v j is the post-synaptic (effect) node. ...
... In the augmented FCM method, the adjacency matrices provided by all experts are added to obtain the final diagraph-based FCM model (Dickerson & Kosko, 1993). This approach does not need that experts change their former opinions to obtain a consensus as in the Delphi methodology (Salmeron, 2009). ...
The on-going offshore outsourcing processes have resulted in complex, global and more vulnerable supply chain to disruptions. However, a good supplier choice would preserve or even improve supply chain resilience. Despite this critical potential effect, this topic remains relatively underdeveloped in the literature. Accordingly, this study proposes a coupled method based on Fuzzy Cognitive Maps (FCM) and Analytic Hierarchy Process (AHP). The final model shows the impact of locational decision in offshore outsourcing process on supply chain resilience. Moreover, it allows simulating locations scenarios over time through an inference process. The simulations foresee the impacts of three alternative locations on capabilities required in a resilient supply chain. The sensitivity analysis of the findings reveals that one location would improve supply chain resilience meanwhile the others would damage it. This FCM-AHP analysis enhances the understanding of academics and practitioners about the importance of locations criteria and their influence in the supply chain resilience capabilities.
... First, the Hebbian rule-based learning algorithms. These methods [23][24][25] share a com-mon feature is that they use iterative methods to learn the weight matrix of FCMs. Second, the population-based algorithms. ...
Fuzzy cognitive maps (FCMs) learning is a hot topic in recent years. However, as the number of concepts increases in FCMs, it is difficult to learn the sparse and robust FCMs from a small amount of data, especially from noise data. In this paper, a new large-scale FCMs learning method based on the sparse regression of adaptive loss function is presented, marked as AQP-FCM. Adaptive loss function and L1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_1$$\end{document}-norm are introduced in the model to deal with noise data. We solve the model by ADMM method and quadratic programming method to learn the FCMs better. Moreover, the convergence of model is proved. We did a series of experiments under the synthetic data of time series and noise synthesis data. AQP-FCM is also applied to reconstruct gene regulatory network (GRNs). The results of the experiments show that the proposed AQP-FCM method has good performance.
... One option is to have expert-defined weights. The other option is to infer the casual relationships of an FCM using a wide range of learning methods [22], including Hebbian methods [23]- [25], evolutionary learning methods [26]- [29], and various combinations of the two [30]- [32]. In addition to the above methods, FCM causal relationships can be determined using inverse function [33], pseudoinverse matrix [34], and backpropagation algorithm [35]. ...
One drawback of using the existing one-step forecasting models for long-term time series prediction is the cumulative errors caused by iterations. In order to overcome this shortcoming, this article proposes a trend-fuzzy-granulation-based adaptive fuzzy cognitive map (FCM) for long-term time series forecasting. Different from the original FCM-based forecasting models, a class of trend fuzzy information granules is built to represent the trend, fluctuation range, and trend persistence of various segments of time series, which are more instrumental and comprehensive than simple magnitude information. Thus, the proposed forecasting model is a granular model according to the form of its inputs and outputs. In an original FCM-based forecasting model, the causal relationships among concepts remain unchanged throughout the training of the whole dataset, however, in reality, the causal relationships may change with the state of concepts. Therefore, it is unreasonable to use the invariable causal relationships which often result in poor predictions. In view of this, we construct an adaptive FCM where different causal relationships are built to forecast concepts of different states. This is the first time to forecast trend fuzzy information granules using an adaptive FCM. Compared with the existing classical forecasting models, the proposed forecasting model achieves superior performance which is verified through a series of experimental studies.
... Fuzzy Cognitive Maps (FCMs) are soft computing tools that have proven their adequate performance to analyze the interrelationships between variables in processes. FCMs are a combination of fuzzy logic and neural networks, which are graphical representations used to illustrate causal reasoning with a structure that allows backward or forward progress and performing direct and inverse correlation analysis between related events (Dickerson & Kosko, 1994). ...
Meeting quality characteristics of products and processes is an important issue for customer satisfaction and business competitiveness. It is necessary to integrate new techniques and tools that improve and complement traditional process variables analysis. This paper proposes a new methodological approach to analyze process quality control variables using Fuzzy Cognitive Maps.
... However, causal relationships among concepts are unknown in some cases and need to be learned from experimental data. The literature offers a wide range of FCM learning methods [26], including Hebbian methods [27]- [29], evolutionary learning methods [30]- [33], and various combinations of these two methods [34]- [36]. In the Hebbian method, FCMs are obtained by the iterative optimization scheme, which is very fast. ...
A fuzzy cognitive map (FCM) is a graph-based knowledge representation model wherein the connections of the nodes (edges) represent casual relationships between the knowledge items associated with the nodes. This model has been applied to solve various modeling tasks including forecasting time series. In the original FCM-based forecasting model, causal relationships among concepts of the FCM remain unchanged. However, causal relationships may change in time. Therefore, we propose a new learning method for training an FCM resulting in an adaptive FCM which consists of several sub-FCMs. It can select different sub-FCMs at different moments. In an active processing scenario, in which we deal with a large-scale time series with new data being continuously generated, a forecasting model built on the old data should be updated when the new data arrive. Furthermore, retraining an FCM from scratch entails increasing computing overhead that will become a serious obstacle in many practical scenarios. To overcome the above-mentioned shortcomings, this study offers an original design setting in which the FCM is updated by knowledge-guidance learning mechanism for the first time. Compared with the existing classical forecasting models, the proposed model shows higher accuracy and efficiency. Its increased performance is demonstrated through a series of reported experimental studies.
... Слід зазначити, що в англомовних джерелах математично-кібернетичного характеру іноді зустрічається й ширше тлумачення когнітивних картяк умовних схематичних моделей, на які спирається суб'єкт у процесі інтелектуального вирішення завдань, що передають причиннонаслідкові зв'язки між певними об'єктами, і які можуть бути зображені у вигляді систем пов'язаних графів [14; 18]. Зокрема, Б. Коско запропонував термін «нечіткі когнітивні карти» (FCM-fizzy cognitive maps) -своєрідні моделі віртуальних світів, у яких каузальними зв'язками поєднуються певні події, діючі сторони, цінності, цілі, напрямки (тенденції) тощо [14]. Відомо, що FCM використовувались західними політологами та політичними діячами (зокрема, Г. Кіссінджером) при аналізі геополітичних проблем [18]. ...
The results of theoretical analysis of the phenomenon of mental (cognitive) maps are presented. The essence, structure and features of these forms of spatial representations are considered. Different views among scientists regarding the structural components of mental maps, their similarity with geographical maps are described. There is conclusion the perspective of using this theoretical concept in the researches of number applied psychological problems.
... Papageorgiou, Stylios, & Groumpos, 2003), es el algoritmo de tipo Hebbiano (sin uso de datos históricos) con mejores resultados. Stach, Kurgan y Pedrycz comparan el algoritmo NHL contra los algoritmos: Algoritmo Hebbiano Diferencial (DHL) de (Dickerson & Kosko, 1993), Algoritmo Diferencial Balanceado (BDA) de (Vázquez-Huerga, 2002), Algoritmo Hebbiano Activo (AHL) de (E.I. Papageorgiou, Stylios, & Groumpos, 2004) y Hebbiano No-Lineal dirigido por dato (DDNHL) de (Stach, Kurgan, & Pedrycz, 2008). ...
This research presents a methodology for the extraction of knowledge, based on the Essence standard charts, which is based on the modeling of the casual relationships Tbetween the different characteristics in software projects. It uses a framework of software project features (MCPS-R) created by our research group using Fuzzy Cognitive Maps (FCM)
... It has a structure that can express the structure of systems with related events. It allows receiving feedback on the status of the system over time [37]. FCM, when first proposed by Bart Kosko in 1986, suggests that the relationships between concepts must be fuzzified, and the developments in this area have been initiated. ...
... Therefore, qualitative systems analysis or qualitative modelling [6] is increasingly being used for analysing the dynamics of complex systems. Kosko [7] introduced fuzzy cognitive maps (FCMs) as a tool for capturing and explaining the behaviour of dynamic qualitative systems [7][8][9]. FCMs, as explained in [7,[10][11][12] are increasingly been used to model and analyse the behaviour of qualitative systems [13][14][15][16][17][18][19][20]. Over the last 30 years, this fuzzy cognitive mapping (FCM) approach has become increasingly popular due to the ease of design and the low computational requirements for simulating social system dynamics [21,22] , largely using two forms of application, and connected data use and generation: (1) the deductive approachemploying knowledge that is gathered by interviewing experts from the area of application; (2) the inductive approach-an automated and semi-automated approach designed for learning FCM rules based on historical data [13,16,19,20,[23][24][25][26][27][28]. ...
Fuzzy Cognitive Maps (FCMs) were developed as a tool for capturing and modelling the behaviour of qualitative system dynamics. However, several drawbacks have been identified that limit FCMs ability in simulating the behaviour of qualitative system. This paper addresses the limitations of FCMs in modelling complex qualitative system dynamics and proposes a generalised Fuzzy Cognitive Mapping (FCM) approach that is able to overcome those limitations. This approach uses fuzzy rules to represent the dynamics of concepts and relations, including time dynamics of relations and introduces a multistep simulation approach that can use several single layer perceptrons to simulate the dynamics of concepts and relations overtime. This approach also incorporates the fuzziness and ambiguity widely associated with expert knowledge when representing and simulating the dynamics of concepts and relations. In this paper, the design of the proposed generalised FCM approach is explained and demonstrated for a real-world case of the consequences of high intensity rainfall in Kampala City, Uganda. This generalised FCM approach creates a new perspective and an alternative approach to model the behaviour of complex qualitative system dynamics using FCMs.
... In our work we borrow the problem statement proposed in [12]: hence, we apply a Hebbian-based algorithm called differential Hebbian learning (DHL) algorithm [36]. The main drawback of this method is that the formula updates weights between each pair of concepts without considering the influence of other concepts. ...
Future networks are expected to provide improved support for several different kinds of applications and services. All these services will have diverse characteristics and requirements to be satisfied. A potential technology to upgrade efficiently and effectively current generation networks is virtualisation via network ’softwarization’. This approach requires the combination of software-defined networking and network function virtualisation. Nevertheless, such a new complex network structure will raise further issues and challenges to be solved both reactively and proactively, without human intervention. In order to achieve that, academia and industry have identified the solution in the implementation and deployment of machine learning. Hence, very likely, 5G (and especially beyond 5G) networks will be cognitive virtualised networks. In that context, this article proposes a cognitive software-defined networking architecture based on Fuzzy Cognitive Maps. First, specific design modifications of Fuzzy Cognitive Maps are proposed to overcome some well-known issues of this learning paradigm. Second, the efficient integration with a software-defined networking architecture is presented and analysed. Finally, the emulation of a sample network scenario via Mininet is provided to validate the effectiveness and the potential of the new cognitive system and its capability to act and to adapt independently of human intervention.
... FCMs can also apply driving agents' behaviors or decisions in agent-based models (ABMs). Prior work (Dickerson & Kosko, 1993) shows how lone or combined FCMs can govern the behavior of agents or virtual actors. These behaviors can be simple or complex ...
Fuzzy cognitive maps (FCMs) model feedback causal relations in interwoven webs of causality and policy variables. FCMs are fuzzy signed directed graphs that allow degrees of causal influence and event occurrence. Such causal models can simulate a wide range of policy scenarios and decision processes. Their directed loops or cycles directly model causal feedback. Their nonlinear dynamics permit forward‐chaining inference from input causes and policy options to output effects. Users can add detailed dynamics and feedback links directly to the causal model or infer them with statistical learning laws. Users can fuse or combine FCMs from multiple experts by weighting and adding the underlying fuzzy edge matrices. The combined FCM tends to better represent domain knowledge as the expert sample size increases if the expert sample approximates a random sample. Many causal models use more restrictive directed acyclic graphs (DAGs) and Bayesian probabilities. DAGs do not model causal feedback because they do not contain closed loops. Combining DAGs also tends to produce cycles and thus tends not to produce a new DAG. Combining DAGs can produce an FCM. FCMs trade the numerical precision of Bayesian causal systems for pattern approximation, fast and scalable computation, and rich feedback representation. We show how FCMs can apply to the social scientific problem of public support for insurgency and terrorism and to US–China relations in Graham Allison's Thucydides' trap framework.
... FCM is used in many real-world applications, such as in international relations and political developments [60], [61]. The cognitive model has been extensively studied in the system control to enhance the control environment [62], to build failure models, to carry out effect analysis [63], and to model system supervisors [64] to model actors' intelligence and decision-making support [65]. Fig. 4 shows an example of FCM. ...
Unmanned vehicle systems are often teleoperated, semiautonomous, and strictly dependent on human operators. In complex and dynamic environments, unmanned vehicles should be autonomous in a stricter sense, which means they should exhibit a human-like behavior to be capable of accurately perceiving the environment; understanding the situation, locating and interacting with environmental elements; and reporting solutions to humans. In order to address these desiderata, a modeling of a proactive, context-aware unmanned system is presented. Precisely, the system framework is designed for an unmanned aerial vehicle (UAV) that flies over an area, and collects data in the form of video frames, sensor values, etc. It recognizes situations, senses scene object and environment data, acquires the awareness about the evolving scenes, and, finally, takes a decision based on the perception of the overall scenario. The system design is based on two primary building blocks: 1) the semantic web technologies that provide the high-level object description in the tracked scenario, and 2) the fuzzy cognitive map model that provides the cognitive accumulation of spatial knowledge in order to discern specific situations that need a decision. Although the paper presents a UAV-based surveillance system model, its applicability is shown based on a realistic case study (viz., broken car on the highway); moreover, several possible scenario configurations have been simulated to assess the criticality level perceived by the system (UAV) in a given situation and to validate the effective response/decision in the case of critical situations.
... This level of the augmentation is often more challenging. However, a simple mathematical average of the values of a connection as proposed by experts in input FCMs is often sufficient [28]. ...
... Его основной инструменткогнитивная карта ситуации, т.е. математическая модель, представленная в виде ориентированного взвешенного (или функционального) графа [Kosko, 1986;Dickerson, Kosko, 1994]. Примерами применения когнитивного подхода для исследования социально-экономических систем могут служить работы российских авторов [Лавреш, Миронов, Смирнов, 2011; Солохин, 2009], но в отличие от них построенная нами модель агрегированным образом охватывает всю социально-экономическую систему выбранной для исследования территории. ...
Book is devoted to the analyses of the resources regions development in a period of fast transforming as of technological as of institutional conditions accompanied by prices decline.
This paper applies Fuzzy Cognitive Maps (FCMs) to understand the diverse behavior of municipal governments in Ecuador to find common elements that influence the well-being of citizens in the short and long term. Information gathering was conducted in two stages: in the first one, a group of 16 national experts was consulted to develop the initial FCM; in the second stage, local experts from 220 municipalities were interviewed to collect information on the general validity of initial FCMs and specific values given to concepts and relationships in their municipalities. Results show the importance of certain concepts for long-term municipal performance, such as the need for a competitive entrepreneurial sector, improving human resources in the municipality, and, particularly, having a competent mayor with leadership skills and a forward-looking vision that enables the development of municipal projects required to reach an efficient and equitable coverage of goods and services throughout the city. Through the application of genetic algorithms, the FCM was calibrated to ascertain the long-term dynamics of municipal development and the optimal values of the concepts that would optimize the attainment of the set objectives. The derived outcomes suggest the desirability of the maintenance of, in principle, unwanted structures like financial transfers from the central government and the need to exploit natural resources to attain urban development.
Introduction
Cultural safety, interculturality and antiracism are crucial concepts in addressing health disparities of minority and diverse groups. Measuring them is challenging, however, due to overlapping meanings and their highly contextual nature. Community engagement is essential for evaluating these concepts, yet the methods for social inclusion and protocols for participation remain unclear. This review identifies experimental studies that measure changes resulting from culturally safe, intercultural or antiracist healthcare. The review will describe outcomes and additional factors addressed in these studies.
Methods and analysis
The study focuses on epidemiological experiments with counterfactual comparisons and explicit interventions involving culturally safe, intercultural or antiracist healthcare. The search strategy covers PubMed, CINAHL, Scopus, Web of Science, ProQuest, LILACS and WHO IRIS databases. We will use critical appraisal tools from the Joanna Briggs Institute to assess the quality of randomised and non-randomised experimental studies. Two researchers will screen references, select studies and extract data to summarise the main characteristics of the studies, their approach to the three concepts under study and the reported effect measures. We will use fuzzy cognitive mapping models based on the causal relationships reported in the literature. We will consider the strength of the relationships depicted in the maps as a function of the effect measure reported in the study. Measures of centrality will identify factors with higher contributions to the outcomes of interest. Illustrative intervention modelling will use what-if scenarios based on the maps.
Ethics and dissemination
This review of published literature does not require ethical approval. We will publish the results in a peer-reviewed journal and present them at conferences. The maps emerging from the process will serve as evidence-based models to facilitate discussions with Indigenous communities to further the dialogue on the contributing factors and assessment of cultural safety, interculturality and antiracism.
PROSPERO registration number
CRD42023418459.
This chapter gives a short introduction to Fuzzy Cognitive Maps (FCMs). It starts with the origin and first applications of Cognitive Maps, then describes the theoretical background of FCMs. Based on the cognitive model, simulations can be performed in order to predict the dynamic behavior of the system and support decision making tasks. The widely applied variations of implementation details are also covered, including their effect on model properties and behavior. A simple example is given to help understanding the theoretical parts, and a short outlook is provided to the possible ways of model creation, too.
When diagnosing complex technical systems (CTS), one of the main tasks is selection of significant diagnostic parameters that characterize the technical condition (TC) of CTS. A lot of factors have a great impact on the CTS operation, so neglecting to take them into account when diagnosing can lead to making wrong decisions concerning their operation. To solve this problem we propose an approach to the selection of significant parameters for diagnosing TC of CTS with the use of “soft computing” under conditions of incomplete and fuzzy information. The novelty of the approach is in the combination of two technologies: cognitive modeling methodology (relations between the parameters in the form of fuzzy, verbal description) and the theory of fuzzy sets (relations between the parameters in the form of verbal description). The statement of the problem is considered and an algorithm for the selection of significant parameters with a detailed description of each step is proposed. An example of application of the developed approach to the selection of significant diagnostic parameters, affecting the TC of asynchronous electric motor (AM) of BAO5К−450, 560 series is considered. The approach proposed will make it possible to formalize the knowledge of the personnel on duty, which may be incomplete or subjective, as well as to increase the speed of decision-making about the TC equipment under conditions of incomplete and fuzzy information.
Currently the drinking water supply service system of the Mexico City Metropolitan Area (MCMA) faces serious problems in its operation, which generate highly negative impacts on the impacts on the environmental, social and provider sectors of this system. Given the presence of climate change, we consider the possible scenario in which a decrease in average annual precipitation occurs in the area. To evaluate its impact on each of the sectors, the fuzzy cognitive map (FCM) associated with each of them was constructed. This framework allowed the study of a system whose available information presented large ranges of uncertainty, in addition to being highly complex. Based on the results obtained, we present a mitigation measure for each sector, in order to provide efficient actions to decisions makers. The mathematical simulation was carried out programming in Python.
Among various soft computing approaches for time series forecasting, fuzzy cognitive maps (FCMs) have shown remarkable results as a tool to model and analyze the dynamics of complex systems. FCMs have similarities to recurrent neural networks and can be classified as a neuro-fuzzy method. In other words, FCMs are a mixture of fuzzy logic, neural network, and expert system aspects, which act as a powerful tool for simulating and studying the dynamic behavior of complex systems. The most interesting features are knowledge interpretability, dynamic characteristics and learning capability. The goal of this survey paper is mainly to present an overview on the most relevant and recent FCM-based time series forecasting models proposed in the literature. In addition, this article considers an introduction on the fundamentals of FCM model and learning methodologies. Also, this survey provides some ideas for future research to enhance the capabilities of FCM in order to cover some challenges in the real-world experiments such as handling non-stationary data and scalability issues. Moreover, equipping FCMs with fast learning algorithms is one of the major concerns in this area.
The users of mobile phone are exponentially increasing. The applications are developed every day in a variety of domains to enhance the Quality of User Experience (QoUE) along with utility determinants. The design of the mobile application impacts the QoUE. QoUE in mobile applications is a measure that describes the appropriateness of the purpose of the application and the need for user retention. However, the challenge is to identify, understand, focus and interconnect the variety of determinants influencing the QoUE based on mobile application design. These determinants are based on the diversity of users and the related functional needs, user-specific needs, and background functioning of the application. The modelling and analysis help mobile application developers to improve, increase and retain user engagement on the app based on improved QoUE. To do so, a qualitative analytical method is employed in the following steps. The first ever Fuzzy Cognitive Map (FCM) is proposed to show the causal-effect links of the interdependent determinants in mobile applications based on QoUE. In our model, the existence of relationships between determinants relies on a thorough literature review. The weight of these links is estimated by users of different ages and lines of work. This is performed by an empirical study based on a questionnaire filled by experts. The questionnaire is based on the formal utility and perceived QoUE-based topics. Finally, scenario-based analysis on formed FCM based on these inputs is performed. We show that small changes in cases using different direct determinants can be used to enhance QoUE. These changes can be studied before launching an application for the user, thereby limiting the need to rework the improvements based on QoUE and providing useful guidance for the possible increase in user base and behaviour change.
A fuzzy cognitive map (FCM) is an analysis tool that uses a graph structure to show the causal relationships of influence factors in a decision-making system. During the decision-making process, it is reasonable for experts to use linguistic variables to express their subjective opinions. However, few studies have discussed methods for aggregating the linguistic opinions of experts to reach a group consensus in FCM. In addition, the interaction weights among the factors in FCM will usually change over time in a real environment. Therefore, we applied the learning algorithm to adjust the interaction weights among the factors in the steps of FCM, after which we proposed a dynamic fuzzy cognitive map model with group consensus based on the linguistic evaluations in this study. Finally, we presented a case study using the proposed model to illustrate the development possibility of the Internet of Things (IoT) industry in Taiwan. According to the analysis results, we found that the development prospects for the IoT industry in Taiwan are optimistic. The four key factors for IoT industry development were found to be the degree of authorization and trust, the development of application technologies, the complexity of systems and equipment and cross-platform possibility.
Access to financial resources is the main constraint for sustainable economic growth in Kazakhstan. Kazakhstan’s financial institutions, being relatively small in size and unbalanced, cannot finance development through market mechanisms. The model that reflects the system’s sustainable development was constructed based on the fuzzy cognitive map to consolidate 31 indicators of three types of the subsystem: economic, financial institutions, and financial markets to reflect the viability and balance of the system development. The time-series data of Kazakhstan’s economic and financial development of Kazakhstan from 2014 to 2019 with prognosis to 2025 was used. Based on the results of our research, we concluded that the implementation of policies for sustainable development should include not only specific aims for the financial market but also for financial institutions and the economy. Moreover, sustainability is reinforced by the development of integration, innovativeness, and accessibility, which forms the key aspects of financial system state policy development.
This chapter draws on the methodology for e-service customization presented in chapter 5 and presents a modeling approach for realizing the service customization strategies. The described models are a combination of object-oriented business process and task models, and of fuzzy cognitive maps (FCM) that represent the interrelationships between business process performance and service customization. The modeling approach breaks down business process(es) and e-services components into customization activities and components respectively, and associates their performance indicators with customization strategic objectives. This chapter describes the concepts and models that are developed in each step in order to capture the strategic factors, service characteristics, process tasks and data entities, which are assumed in the customization process, and outlines the advantages of the modeling approach.
The Failure Mode and Effects Analysis (FMEA) technique due to its proactive nature can identify failures and their causes as well as potential effects, and provide preventive/controlling measures before they occur. Nevertheless, some of the shortcomings of the FMEA technique like lack of a mental framework for considering the relationships between risks, lack of systematic perspective in confronting with risks, and weakness of Risk Priority Number (RPN) score in mathematical basis and disregarding the uncertainty of problem reduce the reliability of the outputs. In this study, an approach based on the Multi-Stage Fuzzy Cognitive Map and the Z-number theory (Z-MSFCM) is proposed to simultaneously consider the concept of uncertainty and reliability in quantities of risk factors and the weights of causal relationships in the MSFCM. Besides, a novel learning approach for Z-MSFCM has been applied based on the combination of the Particle Swarm Optimization (PSO) and S-shaped transfer function (PSO-STF) to preserve the uncertain environment of the problem. The proposed approach has been applied in a manufacturing automotive parts company and results indicate that: first, Z-MSFCM by considering the causal relationships between risks and their uncertainty and reliability in comparison with traditional RPN can provide better process-oriented insight into the impact of risks on the system; and second, the PSO-STF has high potential in generating solutions with high separability compared to Nonlinear Hebbian Learning and PSO algorithms. To put it differently, the mentioned advantages of the proposed approach can help decision-makers to analyze the problem with high reliability.
Representation and evaluation of knowledge are key concepts for the management of the domain module within an intelligent tutoring system. However, in order to develop more efficient education tools, not only should knowledge representation be considered as a base. One should go in depth into something more complex known as the representation of dependency between key knowledge concepts in a given domain, in such a way that the model is more robust and can be used to make decisions in didactic electronic devices, whose operation is similar to that of an intelligent tutoring system; which are connected, by the moment, to a local network, and later, the objective is to implement the concept of the internet of things in educational classrooms. The representation of dependency between key knowledge concepts in a given domain is supported by different pedagogical theories such as the Cognitive Load Theory (CLT), which computationally can be modeled through Fuzzy Cognitive Maps (FCM). FCMs help in the decision making process and allow us to represent such dependencies. The decision-making process is that the system can determine the types of problems that the student must solve, so that he can understand each of the topics of the domain. In this paper the results of the combination of fuzzy models (inference models and FCM) to identify cognitive skills and types of problems that help the student reach the appropriate levels in the domain of algebra topics and the differential calculus, are presented. The objective is to implement fuzzy models in electronic devices based on tangible interfaces. This tool is in an initial testing phase with students. Therefore, the results presented in this paper are experimental and are related, execution-wise, to map efficiency.
Any shortage and deviation in the implementation of logistics processes can lead to a poor performance and losing the market share of the manufacturer. To overcome this problem, the identification of existing risks in all logistics sub-processes, and taking corrective/preventive measures, is of great importance. In this study, a risk prioritization approach is presented based on a sequential multi-stage fuzzy cognitive map (SMFCM) and process failure mode and effects analysis (PFMEA) to prioritize logistics processes risks. This approach, in addition to considering a process-oriented view, prioritizes the logistics risks according to the amount of each risk’s impact on other risks through the internal and external-stage causal relationships as well as the values of risk factors. The score obtained from this approach can create enough distinction among the priorities of different risks in comparison with conventional risk priority number (RPN). Also, the results based on this score have a lower dependency on experts’ opinions by applying the Extended Delta Rule (EDR) learning algorithm. In fact, the intelligent nature of this approach enables decision-makers to identify critical risks and examine the status of the system at any time. The results of the implementation of the SMFCM-PFMEA approach in a manufacturing company active in the automotive spare parts industry confirm the superiority of this compared with the conventional score.
Using empirical data from the Kuwaiti mobile telecommunications sector, this study models a fuzzy cognitive map (FCM) to investigate the reciprocal effects of customer loyalty and its antecedents in an emerging market context. This study investigates the effect of perceived service quality, perceived service value and brand equity on customer loyalty and the simultaneous analysis of the reverse causality of these variables. Data pertaining to 350 subscribers were analysed. According to the results, the model reaches the equilibrium when brand equity and customer loyalty are increased and reach an optimal level. Based on these findings, the authors provide implications for managers in the mobile telecom industry.
Fuzzy cognitive maps (FCMs) are generally applied to model and analyze complex dynamical systems. Recently, many evolutionary-based algorithms are proposed to learn FCMs from historical data by optimizing Data_Error, which is used to evaluate the difference between available response sequences and generated response sequences. However, when Data_Error is adopted as the fitness function for learning FCMs, two problems arise. One is that the optimization reaches the desired result slowly; the other is that the learned FCMs have high link density. To solve these problems, we propose another objective named as convergence error, which is inspired by the convergence of FCMs, to evaluate the difference between the convergent value of available response sequences and that of generated response sequences. In addition, a multi-agent genetic algorithm (MAGA), which is effective for large-scale global numerical optimization, is adopted to optimize convergence error for learning FCMs. To this end, a novel learning approach, a multi-agent genetic algorithm based on the convergence error (MAGA-Convergence), is proposed for learning FCMs. MAGA-Convergence needs less data, because the only initial value and convergent value of the available response sequences are needed for learning FCMs. In the experiments, MAGA-Convergence is applied to learn the FCMs of synthetic data and the benchmarks DREAM3 and DREAM4 for gene regulatory network reconstruction. The experimental results show that the learned FCMs are sparse and could be learned in much fewer generations than other learning algorithms.
Creation of complex technical systems (CTS) is complicated iterative process, which is connected with considerable expenses of material, labor and financial resources together with many arising risk situations caused by constructive defects, industrial releases of unproven technologies, staff faults, inadequate skill level, etc. It can lead to sufficient backlog or fall of CTS creation project. Therefore modeling of the risk situations arising during creation of complex technical systems in the conditions of fuzzy input data is the relevant. Two modern approaches are proposed to detect and predict risk situations: fuzzy cognitive modeling and situation modeling. The convolution algorithm is presented for situation graph generalization. The construction and impulse modeling for fuzzy cognitive model of risk detection in nuclear industry is considered. The results of modeled scenarios of possible risk evolution and their analysis are shown. Proposed approaches allow to identify and analyze the facts impacting on risk situation, obtain possible scenarios of emergence, find the decision ways in modeled situations. It can be used a basis in the production of scientifically proven management actions.
Problem statement: SEMS is a complex dynamic object in the operational phase of which it is likely that abnormal situations may occur in which the state of the equipment goes beyond the normal functioning, which can subsequently lead to an accident. Despite the importance of the need and importance of effectively solving the problems of diagnostics of SEMS, at the present time there is no single approach to solving similar problems taking into account the variety of emergent contingencies. Therefore, the actual task is the development of methods, algorithms and special diagnostic tools that allow predicting the development of defects, diagnose processes and recognize violations of normal operation at an early stage of their development to ensure the efficiency, reliability and safety of the operation of SEMS in real time. Results: cognitive to diagnose SEMS in conditions of interval uncertainty and fuzzy initial data, cognitive and fuzzy cognitive modeling is used to reflect the problems of SEMS in a simplified form (in the model), to investigate possible scenarios for the emergence of risk situations at an early stage of their development, and to find ways to resolve them in the model of the situation. As an example, a fuzzy cognitive model of SEMS diagnostics is proposed. Pessimistic and optimistic scenarios of possible development of risk situations, developed with the help of impulse simulation, are given and their brief analysis is given. The system indices of the fuzzy cognitive model are calculated, allowing to identify which of the factors have the greatest impact on SEMS and vice versa; To search for the best values of factors reflecting the normal operation of SEMS. Practical significance: the ability to systematically take into account the long-term consequences of possible abnormal situations and identify side effects that allow to take into account the multifactority of the process of diagnosing SEMS in the process of operation. The task of identifying possible risks in general, and at the operational stage in particular, should be an important part of the diagnostics of SEMS equipment.
Design of complex technical systems (CTS) is a difficult iterative process. It involves large capital investments, considerable expenses of resources (financial, labor, time), development of a large number of various documentations and also a set of the emerging risk situations. The intelligent control system and preventions of risk situations is necessary for reliable creation of CTS. Such a system would allow to process and analyse quickly large volumes of diverse information, to reveal risk situations at a design stage. Such a system would also be capable of resolving a common question "what would happen with the project of CTS in case of some changes made at the design stage...?" This article is dedicated to creation of the knowledge base of the intelligent control and prevention of risk situations system in the conditions of diverse information environment for design of CTS for a critical infrastructure. The analysis of the main types of models of knowledge representation commonly applied for creation of knowledge bases is carried out. The offered knowledge base is built on the modular principle and uses the generalized scheme of methodology of cognitive and fuzzy cognitive modeling. Novelty is that knowledge in the offered knowledge base is presented in the form of accurate or fuzzy cognitive models and also scenarios of development of the risk situations connected with design of complex technical systems.
The negative impacts of change orders on construction projects are well documented in the literature. Effective management of change orders requires a comprehensive approach to identify and analyze their causes. A change order in construction projects is usually an outcome of combining several interrelated causes, rather than a single cause. Most of the previous studies have considered the causes of change orders as independent causes. However, the causes of change orders have a complicated causal structure, which makes it difficult to independently analyze and prioritize them. Therefore, this study contributes to the body of knowledge in change order management by proposing a fuzzy cognitive map (FCM) approach, which has the capability to analyze the causes of change orders considering their entire causal interactions. The root causes of change orders, as well as the direct ones, can be identified and prioritized by the proposed approach. The manageability of the causes can also be analyzed by the FCM approach. The performance of the proposed approach is evaluated by implementing it in a real construction project. A modeling-validating approach is used to construct and validate the FCM model. It is believed that the proposed FCM approach presents a powerful tool for analyzing the causes of change orders. In addition, this research contributes to knowledge in the area of FCMs by proposing a new inference law.
The paper presents a novel supervised learning method for fuzzy cognitive maps adapted from the theory of artificial neural networks. The main objective in designing the method was to pay closer attention to the distinctions that exist between fuzzy cognitive maps, and the original model for which the method was intended – whether it was a feedforward neural network, a recurrent network, or an energy-based model. The augmented version strives to properly build upon the various strengths of fuzzy cognitive maps – particularly on their interpretability, which arises from the close coupling that exists between their nodes and particular concepts. It is shown that the augmented method is able to outperform existing approaches. Notably, the ability of the learned model to generalize correctly, and to faithfully reconstruct the original system is studied.
Over the course of three to four decades, most well-established companies lose their dominating position in the market or fail entirely. Their failure occurs even though they have resources for sensing shifting market trends, skills and assets to develop next-generation technologies, and the financial means to fill skill gaps and afford risky investments. Nevertheless, incumbents obviously find it very difficult to invest in innovation that takes attention and resources away from a highly successful core business. A solution to this “innovator’s dilemma” is the concept of “organizational ambidexterity”, which has garnered considerable attention among researchers in organization and innovation. According to empirical findings and emergent theory, companies can improve their financial performance and ensure their long-term survival by balancing their innovation activities, so that they are equally focused on exploratory (discontinuous) and exploitative (incremental, continuous) innovations. But how can such a balance be achieved? The literature on the organizational theory and related fields (product innovation, knowledge management, creativity, etc.) identifies more than 300 contributing factors to innovation and ambidexterity: many are interdependent so that their impacts compound or cancel each other. Moreover, for many factors, there is limited empirical data and the size of impacts is unknown. To understand which managerial actions lead to ambidexterity, this dissertation develops a novel approach to the
study and analysis of complex casual systems with high uncertainty: exploratory fuzzy cognitive mapping.
Fuzzy Cognitive Mapping (FCM) is a semi-quantitative system modeling and simulation technique. It is used to represent qualitative information about complex systems as networks of casual relationships that can be studied computationally. Exploratory modeling and analysis (EMA) is a new approach to modeling and simulation of complex systems when there is high uncertainty about the structural properties of the system. This work is the first to combine both approaches.
The work makes several contributions: First, it shows that only a small fraction of management interventions will actually lead to ambidexterity while most will, at best, improve one type of innovation at the expense of the other. Second, it provides a simulation tool to management researchers and practitioners that allows them to test ideas for improving ambidexterity against a model that reflects our current collective knowledge about innovation. And third, it develops a range of techniques (and software code) for exploratory FCM modeling, such as methods for transforming qualitative data to FCM, for exploratory simulation of large and complex FCM models, and for data visualization. They can be utilized to study other similarly complex and uncertain systems.
We believe that computer animation in the form of narrated animated simulations can provide an engaging, effective and flexible medium for instructing agents in the performance of tasks. However, we argue that the only way to achieve the kind of flexibility needed to instruct agents of varying capabilities to perform tasks with varying demands in work places of varying layout is to drive both animation and narration from a common representation that embodies the same conceptualization of tasks and actions as Natural Language itself. To this end, we are exploring the use of Natural Language instructions to drive animated simulations. In this paper, we discuss the relationship between instructions and behavior that underlie our work and the overall structure of our system. We then describe in some what more detail three aspects of the system - the representation used by the Simulator, the operation of the Simulator and the Motion Generators used in the system.
Most animals have significant behavioral expertise built in without having to explicitly learn it all from scratch. This expertise is a product of evolution of the organism; it can be viewed as a very long term form of learning which provides a structured system within which individuals might learn more specialized skills or abilities. This paper suggests one possible mechanism for analagous robot evolution by describing a carefully designed series of networks, each one being a strict augmentation of the previous one, which control a six legged walking machine capable of walking over rough terrain and following a person passively sensed in the infrared spectrum. As the completely decentralized networks are augmented, the robot's performance and behavior repertoire demonstrably improve. The rationale for such demonstrations is that they may provide a hint as to the requirements for automatically building massive networks to carry out complex sensory-motor tasks. The experiments with an actual robot ensure that an essence of reality is maintained and that no critical problems have been ignored.
Fuzzy cognitive maps (FCMs) are fuzzy-graph structures for representing causal reasoning. Their fuzziness allows hazy degrees of causality between hazy causal objects (concepts). Their graph structure allows systematic causal propagation, in particular forward and backward chaining, and it allows knowledge bases to be grown by connecting different FCMs. FCMs are especially applicable to soft knowledge domains and several example FCMs are given. Causality is represented as a fuzzy relation on causal concepts. A fuzzy causal algebra for governing causal propagation on FCMs is developed. FCM matrix representation and matrix operations are presented in the Appendix.
Complex social systems are difficult to represent. Relationships between social forces demand feedback. For example, the causal connection between commodity price and consumer demand is a feedback system. Price increase tends to decrease demand for some commodities. On the other hand, an increased demand tends to elevate price. A stable system settles into equilibrium. A dynamic system is needed to model shifts in equilibrium brought about by changes in the causal environment. The knowledge-based expert system lacks the intrinsic structure for modeling these effects. Internally, all expert systems depend on these representations. Some attempt to simulate unrestricted graphs with virtual registers and loop counters. The result is a semantic gap between the internal representation of the social system and the social system itself. This article presents the Fuzzy Cognitive Map (FCM) alternative to the expert system. We first describe the FCM. We then diagram several social systems. Finally, we show a method for combining credibility-weighted FCMs to achieve a single global knowledge base.
Uncertain causal knowledge is stored in fuzzy cognitive maps (FCMs). FCMs are fuzzy signed digraphs with feedback. The sign (+ or -) of FCM edges indicates causal increase or causal decrease. The fuzzy degree of causality is indicated by a number in [−1, 1]. FCMs learn by modifying their causal connections in sign and magnitude, structurally analogous to the way in which neural networks learn. An appropriate causal learning law for inductively inferring FCMs from time-series data is the differential Hebbian law, which modifies causal connections by correlating time derivatives of FCM node outputs. The differential Hebbian law contrasts with Hebbian output-correlation learning laws of adaptive neural networks.FCM nodes represent variable phenomena or fuzzy sets. An FCM node nonlinearly transforms weighted summed inputs into numerical output, again in analogy to a model neuron. Unlike expert systems, which are feedforward search trees, FCMs are nonlinear dynamical systems. FCM resonant states are limit cycles, or time-varying patterns. An FCM limit cycle or hidden pattern is an FCM inference. Experts construct FCMs by drawing causal pictures or digraphs. The corresponding connection matrices are used for inferencing. By additively combining augmented connection matrices, any number of FCMs can be naturally combined into a single knowledge network. The credibility wi in [0, 1] of the ith expert is included in this learning process by multiplying the ith expert's augmented FCM connection matrix by wi.Combining connection matrices is a simple type of adaptive inference. In general, connection matrices are modified by an unsupervised learning law, such as the differential Hebbian learning law. Under special conditions, differential Hebbian dynamical systems are proved globally stable: they resonate on fixed-point attractors.
The author describes a carefully designed series of networks, each one being a strict augmentation of the previous one, which control a six-legged walking machine capable of walking over rough terrain and following a person passively sensed in the infrared spectrum. As the completely decentralized networks are augmented, the robot's performance and behavior repertoire demonstrably improve. The rationale for such demonstrations is that they can help identify requirements for automatically building massive networks to carry out complex sensory-motor tasks. The experiments with an actual robot ensure that an essence reality is maintained and that no critical disabling problems have been ignored. The present work is based on the drawing of analogies between evolution in the animal world and robotic evaluation
An additive fuzzy system can uniformly approximate any real continuous function on a compact domain to any degree of accuracy. An additive fuzzy system approximates the function by covering its graph with fuzzy patches in the input-output state space and averaging patches that overlap. The fuzzy system computes a conditional expectation E|Y|X| if we view the fuzzy sets as random sets. Each fuzzy rule defines a fuzzy patch and connects commonsense knowledge with state-space geometry. Neural or statistical clustering systems can approximate the unknown fuzzy patches from training data. These adaptive fuzzy systems approximate a function at two levels. At the local level the neural system approximates and tunes the fuzzy rules. At the global level the rules or patches approximate the function
Arhficial Reality II, Second Edition
- M Kreuger
Kreuger, M., Arhficial Reality II, Second Edition, Addison-Wesley, 1991.
Animation from Instructions" in Making Them Move: Mechanics, Control, and Animation of Articulated Figures
- N I Badler
- B L Webber
- J Kalita
- J Esakovin