15,949 research outputs found
Towards an Intelligent Tutor for Mathematical Proofs
Computer-supported learning is an increasingly important form of study since
it allows for independent learning and individualized instruction. In this
paper, we discuss a novel approach to developing an intelligent tutoring system
for teaching textbook-style mathematical proofs. We characterize the
particularities of the domain and discuss common ITS design models. Our
approach is motivated by phenomena found in a corpus of tutorial dialogs that
were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor
for textbook-style mathematical proofs can be built on top of an adapted
assertion-level proof assistant by reusing representations and proof search
strategies originally developed for automated and interactive theorem proving.
The resulting prototype was successfully evaluated on a corpus of tutorial
dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453
A Classification Model for Sensing Human Trust in Machines Using EEG and GSR
Today, intelligent machines \emph{interact and collaborate} with humans in a
way that demands a greater level of trust between human and machine. A first
step towards building intelligent machines that are capable of building and
maintaining trust with humans is the design of a sensor that will enable
machines to estimate human trust level in real-time. In this paper, two
approaches for developing classifier-based empirical trust sensor models are
presented that specifically use electroencephalography (EEG) and galvanic skin
response (GSR) measurements. Human subject data collected from 45 participants
is used for feature extraction, feature selection, classifier training, and
model validation. The first approach considers a general set of
psychophysiological features across all participants as the input variables and
trains a classifier-based model for each participant, resulting in a trust
sensor model based on the general feature set (i.e., a "general trust sensor
model"). The second approach considers a customized feature set for each
individual and trains a classifier-based model using that feature set,
resulting in improved mean accuracy but at the expense of an increase in
training time. This work represents the first use of real-time
psychophysiological measurements for the development of a human trust sensor.
Implications of the work, in the context of trust management algorithm design
for intelligent machines, are also discussed.Comment: 20 page
Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices
Smart devices with built-in sensors, computational capabilities, and network
connectivity have become increasingly pervasive. The crowds of smart devices
offer opportunities to collectively sense and perform computing tasks in an
unprecedented scale. This paper presents Crowd-ML, a privacy-preserving machine
learning framework for a crowd of smart devices, which can solve a wide range
of learning problems for crowdsensing data with differential privacy
guarantees. Crowd-ML endows a crowdsensing system with an ability to learn
classifiers or predictors online from crowdsensing data privately with minimal
computational overheads on devices and servers, suitable for a practical and
large-scale employment of the framework. We analyze the performance and the
scalability of Crowd-ML, and implement the system with off-the-shelf
smartphones as a proof of concept. We demonstrate the advantages of Crowd-ML
with real and simulated experiments under various conditions
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