1,381,284 research outputs found
Self-directedness, integration and higher cognition
In this paper I discuss connections between self-directedness, integration and higher cognition. I present a model of self-directedness as a basis for approaching higher cognition from a situated cognition perspective. According to this model increases in sensorimotor complexity create pressure for integrative higher order control and learning processes for acquiring information about the context in which action occurs. This generates complex articulated abstractive information processing, which forms the major basis for higher cognition. I present evidence that indicates that the same integrative characteristics found in lower cognitive process such as motor adaptation are present in a range of higher cognitive process, including conceptual learning. This account helps explain situated cognition phenomena in humans because the integrative processes by which the brain adapts to control interaction are relatively agnostic concerning the source of the structure participating in the process. Thus, from the perspective of the motor control system using a tool is not fundamentally different to simply controlling an arm
Using formal game design methods to embed learning outcomes into game mechanics and avoid emergent behaviour
This paper offers an approach to designing game based learning experiences inspired by the Mechanics-Dynamics-Aesthetics (MDA) model (Hunicke et al, 2004) and the elemental tetrad (Schell, 2008) model for game design. A case for game based learning as an active and social learning experience is presented including arguments from both teachers and game designers concerning the value of games as learning tools. The MDA model is introduced with a classic game- based example and a non-game based observation of human behaviour demonstrating a negative effect of extrinsic motivators (Pink, 2011) and the need to closely align or embed learning outcomes into game mechanics in order to deliver an effective learning experience. The MDA model will then be applied to create a game based learning experience with the goal of teaching some of the aspects of using source code control to groups of Computer Science students. First, clear aims in terms of learning outcomes for the game are set out. Following the learning outcomes the iterative design process is explained with careful consideration and reflection on the impact of specific design decisions on the potential learning experience, and the reasons those decisions have been made and where there may be conflict between mechanics contributing to learning and mechanics for reasons of gameplay. The paper will conclude with an evaluation of results from a trial of computer science students and staff, and the perceived effectiveness of the game at delivering specific learning outcomes, and the approach for game design will be assessed
Knowledge Transfer Between Robots with Similar Dynamics for High-Accuracy Impromptu Trajectory Tracking
In this paper, we propose an online learning approach that enables the
inverse dynamics model learned for a source robot to be transferred to a target
robot (e.g., from one quadrotor to another quadrotor with different mass or
aerodynamic properties). The goal is to leverage knowledge from the source
robot such that the target robot achieves high-accuracy trajectory tracking on
arbitrary trajectories from the first attempt with minimal data recollection
and training. Most existing approaches for multi-robot knowledge transfer are
based on post-analysis of datasets collected from both robots. In this work, we
study the feasibility of impromptu transfer of models across robots by learning
an error prediction module online. In particular, we analytically derive the
form of the mapping to be learned by the online module for exact tracking,
propose an approach for characterizing similarity between robots, and use these
results to analyze the stability of the overall system. The proposed approach
is illustrated in simulation and verified experimentally on two different
quadrotors performing impromptu trajectory tracking tasks, where the quadrotors
are required to accurately track arbitrary hand-drawn trajectories from the
first attempt.Comment: European Control Conference (ECC) 201
CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification Tasks
Data quality affects machine learning (ML) model performances, and data
scientists spend considerable amount of time on data cleaning before model
training. However, to date, there does not exist a rigorous study on how
exactly cleaning affects ML -- ML community usually focuses on developing ML
algorithms that are robust to some particular noise types of certain
distributions, while database (DB) community has been mostly studying the
problem of data cleaning alone without considering how data is consumed by
downstream ML analytics. We propose a CleanML study that systematically
investigates the impact of data cleaning on ML classification tasks. The
open-source and extensible CleanML study currently includes 14 real-world
datasets with real errors, five common error types, seven different ML models,
and multiple cleaning algorithms for each error type (including both commonly
used algorithms in practice as well as state-of-the-art solutions in academic
literature). We control the randomness in ML experiments using statistical
hypothesis testing, and we also control false discovery rate in our experiments
using the Benjamini-Yekutieli (BY) procedure. We analyze the results in a
systematic way to derive many interesting and nontrivial observations. We also
put forward multiple research directions for researchers.Comment: published in ICDE 202
NL4Py: Agent-Based Modeling in Python with Parallelizable NetLogo Workspaces
NL4Py is a NetLogo controller software for Python, for the rapid, parallel
execution of NetLogo models. NL4Py provides both headless (no graphical user
interface) and GUI NetLogo workspace control through Python. Spurred on by the
increasing availability of open-source computation and machine learning
libraries on the Python package index, there is an increasing demand for such
rapid, parallel execution of agent-based models through Python. NetLogo, being
the language of choice for a majority of agent-based modeling driven research
projects, requires an integration to Python for researchers looking to perform
statistical analyses of agent-based model output using these libraries.
Unfortunately, until the recent introduction of PyNetLogo, and now NL4Py, such
a controller was unavailable.
This article provides a detailed introduction into the usage of NL4Py and
explains its client-server software architecture, highlighting architectural
differences to PyNetLogo. A step-by-step demonstration of global sensitivity
analysis and parameter calibration of the Wolf Sheep Predation model is then
performed through NL4Py. Finally, NL4Py's performance is benchmarked against
PyNetLogo and its combination with IPyParallel, and shown to provide
significant savings in execution time over both configurations
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