Evaluating linear-nonlinear thinking style for knowledge management education

The purpose of this paper is to present a new perspective of the linear-nonlinear thinking style and its critical role in knowledge management education. Previous works in this field identified linear thinking as being rational, logic and analytic, and nonlinear thinking as being based on intuition, insight and creativity. In this perspective, linear thinking is related mostly with cognitive intelligence, while nonlinear thinking is related mostly with emotional intelligence. These interpretations have a slight connection with the generic concepts of linearity and linear spaces developed in science. Our research changed the cognitiveemotional perspective into a new one based on the fundamental properties of linear spaces, as they are defined in Mathematics. Basically, a linear model is characterized from operational point of view by a linear equation. That means that outputs of this model should be proportional with inputs. For instance, the temperature level indicated by a familiar thermometer is proportional with the mercury dilation. If the operational model is based on a nonlinear equation, then the model is nonlinear. Thus, cognitive thinking can be linear or nonlinear, while emotional thinking is by its nature nonlinear. Based on this new theoretical construct we developed an investigation instrument to measure the linear-nonlinear thinking style, and applied it to our students in master programs of business administration where there is an important module of knowledge management and learning organizations. The initial sample consisted of 500 graduate students in attending courses in master programs at the Faculty of Business Administration, Academy of Economic Studies from Bucharest, the most important and best considered university for economics and business in Romania. The questionnaire contains 50 items, with answers evaluated on a Likert-type scale. Using the STATA program we performed various analyses, and interpreted the final results in connection with the educational curricula at the Bachelor and Master levels. Conclusions show a dominant role of the linear thinking style, which might constitute o severe limitation in knowledge management and business decision making process.Education, knowledge management, linear, mental models, nonlinear, thinking style.

An intelligent system for risk classification of stock investment projects

The proposed paper demonstrates that a hybrid fuzzy neural network can serve as a risk classifier of stock investment projects. The training algorithm for the regular part of the network is based on bidirectional incremental evolution proving more efficient than direct evolution. The approach is compared with other crisp and soft investment appraisal and trading techniques, while building a multimodel domain representation for an intelligent decision support system. Thus the advantages of each model are utilised while looking at the investment problem from different perspectives. The empirical results are based on UK companies traded on the London Stock Exchange

Distributed Coupled Multi-Agent Stochastic Optimization

This work develops effective distributed strategies for the solution of constrained multi-agent stochastic optimization problems with coupled parameters across the agents. In this formulation, each agent is influenced by only a subset of the entries of a global parameter vector or model, and is subject to convex constraints that are only known locally. Problems of this type arise in several applications, most notably in disease propagation models, minimum-cost flow problems, distributed control formulations, and distributed power system monitoring. This work focuses on stochastic settings, where a stochastic risk function is associated with each agent and the objective is to seek the minimizer of the aggregate sum of all risks subject to a set of constraints. Agents are not aware of the statistical distribution of the data and, therefore, can only rely on stochastic approximations in their learning strategies. We derive an effective distributed learning strategy that is able to track drifts in the underlying parameter model. A detailed performance and stability analysis is carried out showing that the resulting coupled diffusion strategy converges at a linear rate to an $O(\mu)-$neighborhood of the true penalized optimizer

Unconstrained receding-horizon control of nonlinear systems

It is well known that unconstrained infinite-horizon optimal control may be used to construct a stabilizing controller for a nonlinear system. We show that similar stabilization results may be achieved using unconstrained finite horizon optimal control. The key idea is to approximate the tail of the infinite horizon cost-to-go using, as terminal cost, an appropriate control Lyapunov function. Roughly speaking, the terminal control Lyapunov function (CLF) should provide an (incremental) upper bound on the cost. In this fashion, important stability characteristics may be retained without the use of terminal constraints such as those employed by a number of other researchers. The absence of constraints allows a significant speedup in computation. Furthermore, it is shown that in order to guarantee stability, it suffices to satisfy an improvement property, thereby relaxing the requirement that truly optimal trajectories be found. We provide a complete analysis of the stability and region of attraction/operation properties of receding horizon control strategies that utilize finite horizon approximations in the proposed class. It is shown that the guaranteed region of operation contains that of the CLF controller and may be made as large as desired by increasing the optimization horizon (restricted, of course, to the infinite horizon domain). Moreover, it is easily seen that both CLF and infinite-horizon optimal control approaches are limiting cases of our receding horizon strategy. The key results are illustrated using a familiar example, the inverted pendulum, where significant improvements in guaranteed region of operation and cost are noted

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved