A feasibility study of self-learning adaptive flight control for high performance aircraft

"Technical report AFFDL-TR-67-18"

Integrating science and management through collaborative learning and better information management

Achieving more sustainable land and water use depends on high-quality information and its improved use. In other words, better linkages are needed between science and management. Since many stakeholders with different relationships to the natural resources are inevitably involved, we suggest that collaborative learning environments and improved information management are prerequisites for integrating science and management. Case studies that deal with resource management issues are presented that illustrate the creation of collaborative learning environments through systems analyses with communities, and an integration of scientific and experiential knowledge of components of the system. This new knowledge needs to be captured and made accessible through innovative information management systems designed collaboratively with users, in forms which fit the users' 'mental models' of how their systems work. A model for linking science and resource management more effectively is suggested. This model entails systems thinking in a collaborative learning environment, and processes to help convergence of views and value systems, and make scientists and different kinds of managers aware of their interdependence. Adaptive management provides a mechanism for applying and refining scientists' and managers' knowledge. Copyright (C) 2003 John Wiley Sons, Ltd.

Costs of learning: the dynamics of mixed-prey exploitation by silver perch, Bidyanus bidyanus (Mitchell, 1838)

Although generalist predators have been reported to forage less efficiently than specialists, there is little information on the extent to which learning can improve the efficiency of mixed-prey foraging. Repeated exposure of silver perch to mixed prey (pelagic Artemia and benthic Chironomus larvae) led to substantial fluctuations in reward rate over relatively long (20-day) timescales. When perch that were familiar with a single prey type were offered two prey types simultaneously, the rate at which they captured both familiar and unfamiliar prey dropped progressively over succeeding trials. This result was not predicted by simple learning paradigms, but could be explained in terms of an interaction between learning and attention. Between-trial patterns in overall intake were complex and differed between the two prey types, but were unaffected by previous prey specialization. However, patterns of prey priority (i.e. the prey type that was preferred at the start of a trial) did vary with previous prey training. All groups of fish converged on the most profitable prey type (chironomids), but this process took 15-20 trials. In contrast, fish offered a single prey type reached asymptotic intake rates within five trials and retained high capture abilities for at least 5 weeks. Learning and memory allow fish to maximize foraging efficiency on patches of a single prey type. However, when foragers are faced with mixed prey populations, cognitive constraints associated with divided attention may impair efficiency, and this impairment can be exacerbated by experience. (c) 2005 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

Dynamics of on-line gradient descent learning for multilayer neural networks

We consider the problem of on-line gradient descent learning for general two-layer neural networks. An analytic solution is presented and used to investigate the role of the learning rate in controlling the evolution and convergence of the learning process.

Adaptive back-propagation in on-line learning of multilayer networks

An adaptive back-propagation algorithm is studied and compared with gradient descent (standard back-propagation) for on-line learning in two-layer neural networks with an arbitrary number of hidden units. Within a statistical mechanics framework, both numerical studies and a rigorous analysis show that the adaptive back-propagation method results in faster training by breaking the symmetry between hidden units more efficiently and by providing faster convergence to optimal generalization than gradient descent.

The learning dynamics of a universal approximator

The learning properties of a universal approximator, a normalized committee machine with adjustable biases, are studied for on-line back-propagation learning. Within a statistical mechanics framework, numerical studies show that this model has features which do not exist in previously studied two-layer network models without adjustable biases, e.g., attractive suboptimal symmetric phases even for realizable cases and noiseless data.

Incorporating curvature information into on-line learning

We analyse the dynamics of a number of second order on-line learning algorithms training multi-layer neural networks, using the methods of statistical mechanics. We first consider on-line Newton's method, which is known to provide optimal asymptotic performance. We determine the asymptotic generalization error decay for a soft committee machine, which is shown to compare favourably with the result for standard gradient descent. Matrix momentum provides a practical approximation to this method by allowing an efficient inversion of the Hessian. We consider an idealized matrix momentum algorithm which requires access to the Hessian and find close correspondence with the dynamics of on-line Newton's method. In practice, the Hessian will not be known on-line and we therefore consider matrix momentum using a single example approximation to the Hessian. In this case good asymptotic performance may still be achieved, but the algorithm is now sensitive to parameter choice because of noise in the Hessian estimate. On-line Newton's method is not appropriate during the transient learning phase, since a suboptimal unstable fixed point of the gradient descent dynamics becomes stable for this algorithm. A principled alternative is to use Amari's natural gradient learning algorithm and we show how this method provides a significant reduction in learning time when compared to gradient descent, while retaining the asymptotic performance of on-line Newton's method.

Prototyping Adaptive Online Learning Courses

This article describes the process of prototyping adaptive online learning using the authoring tool for developers, which is based on ontologies. The article also gives a brief overview of contemporary situation and describes modern trends of evolution e-learning courses and present standards in this area. It also describes architecture of system VITA II.

Complex Adaptive On-line Learning System “Theory of Automatic Control"

The controlled from distance teaching (DT) in the system of technical education has a row of features: complication of informative content, necessity of development of simulation models and trainers for conducting of practical and laboratory employments, conducting of knowledge diagnostics on the basis of mathematical-based algorithms, organization of execution collective projects of the applied setting. For development of the process of teaching bases of fundamental discipline control system Theory of automatic control (TAC) the combined approach of optimum combination of existent programmatic instruments of support was chosen DT and own developments. The system DT TAC included: controlled from distance course (DC) of TAC, site of virtual laboratory practical works in LAB.TAC and students knowledge remote diagnostic system d-tester.

Anisotropic opinion dynamics

We consider the process of opinion formation in a society of interacting agents, where there is a set B of socially accepted rules. In this scenario, we observed that agents, represented by simple feed-forward, adaptive neural networks, may have a conservative attitude (mostly in agreement with B) or liberal attitude (mostly in agreement with neighboring agents) depending on how much their opinions are influenced by their peers. The topology of the network representing the interaction of the society's members is determined by a graph, where the agents' properties are defined over the vertexes and the interagent interactions are defined over the bonds. The adaptability of the agents allows us to model the formation of opinions as an online learning process, where agents learn continuously as new information becomes available to the whole society (online learning). Through the application of statistical mechanics techniques we deduced a set of differential equations describing the dynamics of the system. We observed that by slowly varying the average peer influence in such a way that the agents attitude changes from conservative to liberal and back, the average social opinion develops a hysteresis cycle. Such hysteretic behavior disappears when the variance of the social influence distribution is large enough. In all the cases studied, the change from conservative to liberal behavior is characterized by the emergence of conservative clusters, i.e., a closed knitted set of society members that follow a leader who agrees with the social status quo when the rule B is challenged.

Mindmapping as an adult learning notetaking strategy to increase the encoding of information in a corporate training course

A study was conducted to investigate the effectiveness, as measured by performance on course posttests, of mindmapping versus traditional notetaking in a corporate training class. The purpose of this study was to increase knowledge concerning the effectiveness of mindmapping as an information encoding tool to enhance the effectiveness of learning. Corporations invest billions of dollars, annually, in training programs. Given this increased demand for effective and efficient workplace learning, continual reliance on traditional notetaking is questionable for the high-speed and continual learning required on workers.^ An experimental, posttest-only control group design was used to test the following hypotheses: (1) there is no significant difference in posttest scores on an achievement test, administered immediately after the course, between adult learners using mindmapping versus traditional notetaking methods in a training lecture, and (2) there is no significant difference in posttest scores on an achievement test, administered 30 days after the course, between adult learners using mindmapping versus traditional notetaking methods in a training lecture. After a 1.5 hour instruction on mindmapping, the treatment group used mindmapping throughout the course. The control group used traditional notetaking. T-tests were used to determine if there were significant differences between mean posttest scores between the two groups. In addition, an attitudinal survey, brain hemisphere dominance survey, course dynamics observations, and course evaluations were used to investigate preference for mindmapping, its perceived effect on test performance, and the effectiveness of mindmapping instruction.^ This study's principal finding was that although the mindmapping group did not perform significantly higher on posttests administered immediately and 30 days after the course, than the traditional notetaking group, the mindmapping group did score higher on both posttests and reported higher ratings of the course on every evaluation criteria. Lower educated, right brain dominant learners reported a significantly positive learning experience. These results suggest that mindmapping enhances and reinforces the preconditions of learning. Recommendations for future study are provided. ^

Transformative Learning Theory and Its Relevance to Managing Group Dynamics in a Competitive Work Environment

In any environment, group dynamics would exist. How we deal with it in a competitive work environment defines who we are using transformative learning. This paper provides useful information from a number of theorists who share perspectives on the complex nature of groups.

A Biocultural Examination of Student Learning Behaviors in Large Undergraduate Lectures

Thesis (Ph.D.)--University of Washington, 2016-08

Evolutionary dynamics on rugged fitness landscapes: Exact dynamics and information theoretical aspects

The parallel mutation-selection evolutionary dynamics, in which mutation and replication are independent events, is solved exactly in the case that the Malthusian fitnesses associated to the genomes are described by the random energy model (REM) and by a ferromagnetic version of the REM. The solution method uses the mapping of the evolutionary dynamics into a quantum Ising chain in a transverse field and the Suzuki-Trotter formalism to calculate the transition probabilities between configurations at different times. We find that in the case of the REM landscape the dynamics can exhibit three distinct regimes: pure diffusion or stasis for short times, depending on the fitness of the initial configuration, and a spin-glass regime for large times. The dynamic transition between these dynamical regimes is marked by discontinuities in the mean-fitness as well as in the overlap with the initial reference sequence. The relaxation to equilibrium is described by an inverse time decay. In the ferromagnetic REM, we find in addition to these three regimes, a ferromagnetic regime where the overlap and the mean-fitness are frozen. In this case, the system relaxes to equilibrium in a finite time. The relevance of our results to information processing aspects of evolution is discussed.

Constant-random practice and the adaptive process in motor learning with varying amounts of constant practice

The adaptive process in motor learning was examined in terms of effects of varying amounts of constant practice performed before random practice. Participants pressed five response keys sequentially, the last one coincident with the lighting of a final visual stimulus provided by a complex coincident timing apparatus. Different visual stimulus speeds were used during the random practice. 33 children (M age=11.6 yr.) were randomly assigned to one of three experimental groups: constant-random, constant-random 33%, and constant-random 66%. The constant-random group practiced constantly until they reached a criterion of performance stabilization three consecutive trials within 50 msec. of error. The other two groups had additional constant practice of 33 and 66%, respectively, of the number of trials needed to achieve the stabilization criterion. All three groups performed 36 trials under random practice; in the adaptation phase, they practiced at a different visual stimulus speed adopted in the stabilization phase. Global performance measures were absolute, constant, and variable errors, and movement pattern was analyzed by relative timing and overall movement time. There was no group difference in relation to global performance measures and overall movement time. However, differences between the groups were observed on movement pattern, since constant-random 66% group changed its relative timing performance in the adaptation phase.