168,728 research outputs found
Problem-Solving Knowledge Mining from Users’\ud Actions in an Intelligent Tutoring System
In an intelligent tutoring system (ITS), the domain expert should provide\ud
relevant domain knowledge to the tutor so that it will be able to guide the\ud
learner during problem solving. However, in several domains, this knowledge is\ud
not predetermined and should be captured or learned from expert users as well as\ud
intermediate and novice users. Our hypothesis is that, knowledge discovery (KD)\ud
techniques can help to build this domain intelligence in ITS. This paper proposes\ud
a framework to capture problem-solving knowledge using a promising approach\ud
of data and knowledge discovery based on a combination of sequential pattern\ud
mining and association rules discovery techniques. The framework has been implemented\ud
and is used to discover new meta knowledge and rules in a given domain\ud
which then extend domain knowledge and serve as problem space allowing\ud
the intelligent tutoring system to guide learners in problem-solving situations.\ud
Preliminary experiments have been conducted using the framework as an alternative\ud
to a path-planning problem solver in CanadarmTutor
The Effect of Explicit Structure Encoding of Deep Neural Networks for Symbolic Music Generation
With recent breakthroughs in artificial neural networks, deep generative
models have become one of the leading techniques for computational creativity.
Despite very promising progress on image and short sequence generation,
symbolic music generation remains a challenging problem since the structure of
compositions are usually complicated. In this study, we attempt to solve the
melody generation problem constrained by the given chord progression. This
music meta-creation problem can also be incorporated into a plan recognition
system with user inputs and predictive structural outputs. In particular, we
explore the effect of explicit architectural encoding of musical structure via
comparing two sequential generative models: LSTM (a type of RNN) and WaveNet
(dilated temporal-CNN). As far as we know, this is the first study of applying
WaveNet to symbolic music generation, as well as the first systematic
comparison between temporal-CNN and RNN for music generation. We conduct a
survey for evaluation in our generations and implemented Variable Markov Oracle
in music pattern discovery. Experimental results show that to encode structure
more explicitly using a stack of dilated convolution layers improved the
performance significantly, and a global encoding of underlying chord
progression into the generation procedure gains even more.Comment: 8 pages, 13 figure
Twin Networks: Matching the Future for Sequence Generation
We propose a simple technique for encouraging generative RNNs to plan ahead.
We train a "backward" recurrent network to generate a given sequence in reverse
order, and we encourage states of the forward model to predict cotemporal
states of the backward model. The backward network is used only during
training, and plays no role during sampling or inference. We hypothesize that
our approach eases modeling of long-term dependencies by implicitly forcing the
forward states to hold information about the longer-term future (as contained
in the backward states). We show empirically that our approach achieves 9%
relative improvement for a speech recognition task, and achieves significant
improvement on a COCO caption generation task.Comment: 12 pages, 3 figures, published at ICLR 201
Abductive Reasoning in Multiple Fault Diagnosis
Abductive reasoning involves generating an explanation for a given set of observations about the world. Abduction provides a good reasoning framework for many AI problems, including diagnosis, plan recognition and learning. This paper focuses on the use of abductive reasoning in diagnostic systems in which there may be more than one underlying cause for the observed symptoms. In exploring this topic, we will review and compare several different approaches, including Binary Choice Bayesian, Sequential Bayesian, Causal Model Based Abduction, Parsimonious Set Covering, and the use of First Order Logic. Throughout the paper we will use as an example a simple diagnostic problem involving automotive troubleshooting
Massively Parallel Video Networks
We introduce a class of causal video understanding models that aims to
improve efficiency of video processing by maximising throughput, minimising
latency, and reducing the number of clock cycles. Leveraging operation
pipelining and multi-rate clocks, these models perform a minimal amount of
computation (e.g. as few as four convolutional layers) for each frame per
timestep to produce an output. The models are still very deep, with dozens of
such operations being performed but in a pipelined fashion that enables
depth-parallel computation. We illustrate the proposed principles by applying
them to existing image architectures and analyse their behaviour on two video
tasks: action recognition and human keypoint localisation. The results show
that a significant degree of parallelism, and implicitly speedup, can be
achieved with little loss in performance.Comment: Fixed typos in densenet model definition in appendi
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