143 research outputs found
Real-time scheduling using minimum search
In this paper we consider a simple model of real-time scheduling. We present a real-time scheduling system called RTS which is based on Korf's Minimin algorithm. Experimental results show that the schedule quality initially improves with the amount of look-ahead search and tapers off quickly. So it sppears that reasonably good schedules can be produced with a relatively shallow search
Coactive Learning for Locally Optimal Problem Solving
Coactive learning is an online problem solving setting where the solutions
provided by a solver are interactively improved by a domain expert, which in
turn drives learning. In this paper we extend the study of coactive learning to
problems where obtaining a globally optimal or near-optimal solution may be
intractable or where an expert can only be expected to make small, local
improvements to a candidate solution. The goal of learning in this new setting
is to minimize the cost as measured by the expert effort over time. We first
establish theoretical bounds on the average cost of the existing coactive
Perceptron algorithm. In addition, we consider new online algorithms that use
cost-sensitive and Passive-Aggressive (PA) updates, showing similar or improved
theoretical bounds. We provide an empirical evaluation of the learners in
various domains, which show that the Perceptron based algorithms are quite
effective and that unlike the case for online classification, the PA algorithms
do not yield significant performance gains.Comment: AAAI 2014 paper, including appendice
Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference
Deep learning models have achieved remarkable success in natural language
inference (NLI) tasks. While these models are widely explored, they are hard to
interpret and it is often unclear how and why they actually work. In this
paper, we take a step toward explaining such deep learning based models through
a case study on a popular neural model for NLI. In particular, we propose to
interpret the intermediate layers of NLI models by visualizing the saliency of
attention and LSTM gating signals. We present several examples for which our
methods are able to reveal interesting insights and identify the critical
information contributing to the model decisions.Comment: 11 pages, 11 figures, accepted as a short paper at EMNLP 201
Exploiting prior knowledge in Intelligent Assistants - Combining relational models with hierarchies
Statitsical relational models have been successfully used to model
static probabilistic relationships between the entities of the domain.
In this talk, we illustrate their use in a dynamic decison-theoretic
setting where the task is to assist a user by inferring his intentional
structure and taking appropriate assistive actions. We show that the
statistical relational models can be used to succintly express the
system\u27s prior knowledge about the user\u27s goal-subgoal structure and
tune it with experience. As the system is better able to predict the
user\u27s goals, it improves the effectiveness of its assistance. We show
through experiments that both the hierarchical structure of the goals
and the parameter sharing facilitated by relational models significantly
improve the learning speed
The Choice Function Framework for Online Policy Improvement
There are notable examples of online search improving over hand-coded or
learned policies (e.g. AlphaZero) for sequential decision making. It is not
clear, however, whether or not policy improvement is guaranteed for many of
these approaches, even when given a perfect evaluation function and transition
model. Indeed, simple counter examples show that seemingly reasonable online
search procedures can hurt performance compared to the original policy. To
address this issue, we introduce the choice function framework for analyzing
online search procedures for policy improvement. A choice function specifies
the actions to be considered at every node of a search tree, with all other
actions being pruned. Our main contribution is to give sufficient conditions
for stationary and non-stationary choice functions to guarantee that the value
achieved by online search is no worse than the original policy. In addition, we
describe a general parametric class of choice functions that satisfy those
conditions and present an illustrative use case of the framework's empirical
utility
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