12,210 research outputs found
Discovering Restricted Regular Expressions with Interleaving
Discovering a concise schema from given XML documents is an important problem
in XML applications. In this paper, we focus on the problem of learning an
unordered schema from a given set of XML examples, which is actually a problem
of learning a restricted regular expression with interleaving using positive
example strings. Schemas with interleaving could present meaningful knowledge
that cannot be disclosed by previous inference techniques. Moreover, inference
of the minimal schema with interleaving is challenging. The problem of finding
a minimal schema with interleaving is shown to be NP-hard. Therefore, we
develop an approximation algorithm and a heuristic solution to tackle the
problem using techniques different from known inference algorithms. We do
experiments on real-world data sets to demonstrate the effectiveness of our
approaches. Our heuristic algorithm is shown to produce results that are very
close to optimal.Comment: 12 page
Feedback Generation for Performance Problems in Introductory Programming Assignments
Providing feedback on programming assignments manually is a tedious, error
prone, and time-consuming task. In this paper, we motivate and address the
problem of generating feedback on performance aspects in introductory
programming assignments. We studied a large number of functionally correct
student solutions to introductory programming assignments and observed: (1)
There are different algorithmic strategies, with varying levels of efficiency,
for solving a given problem. These different strategies merit different
feedback. (2) The same algorithmic strategy can be implemented in countless
different ways, which are not relevant for reporting feedback on the student
program.
We propose a light-weight programming language extension that allows a
teacher to define an algorithmic strategy by specifying certain key values that
should occur during the execution of an implementation. We describe a dynamic
analysis based approach to test whether a student's program matches a teacher's
specification. Our experimental results illustrate the effectiveness of both
our specification language and our dynamic analysis. On one of our benchmarks
consisting of 2316 functionally correct implementations to 3 programming
problems, we identified 16 strategies that we were able to describe using our
specification language (in 95 minutes after inspecting 66, i.e., around 3%,
implementations). Our dynamic analysis correctly matched each implementation
with its corresponding specification, thereby automatically producing the
intended feedback.Comment: Tech report/extended version of FSE 2014 pape
The Complexity of SORE-definability Problems
Single occurrence regular expressions (SORE) are a special kind of deterministic regular expressions, which are extensively used in the schema languages DTD and XSD for XML documents. In this paper, with motivations from the simplification of XML schemas, we consider the SORE-definability problem: Given a regular expression, decide whether it has an equivalent SORE. We investigate extensively the complexity of the SORE-definability problem: We consider both (standard) regular expressions and regular expressions with counting, and distinguish between the alphabets of size at least two and unary alphabets. In all cases, we obtain tight complexity bounds. In addition, we consider another variant of this problem, the bounded SORE-definability problem, which is to decide, given a regular expression E and a number M (encoded in unary or binary), whether there is an SORE, which is equivalent to E on the set of words of length at most M. We show that in several cases, there is an exponential decrease in the complexity when switching from the SORE-definability problem to its bounded variant
Regular Boardgames
We propose a new General Game Playing (GGP) language called Regular
Boardgames (RBG), which is based on the theory of regular languages. The
objective of RBG is to join key properties as expressiveness, efficiency, and
naturalness of the description in one GGP formalism, compensating certain
drawbacks of the existing languages. This often makes RBG more suitable for
various research and practical developments in GGP. While dedicated mostly for
describing board games, RBG is universal for the class of all finite
deterministic turn-based games with perfect information. We establish
foundations of RBG, and analyze it theoretically and experimentally, focusing
on the efficiency of reasoning. Regular Boardgames is the first GGP language
that allows efficient encoding and playing games with complex rules and with
large branching factor (e.g.\ amazons, arimaa, large chess variants, go,
international checkers, paper soccer).Comment: AAAI 201
Minimal model of associative learning for cross-situational lexicon acquisition
An explanation for the acquisition of word-object mappings is the associative
learning in a cross-situational scenario. Here we present analytical results of
the performance of a simple associative learning algorithm for acquiring a
one-to-one mapping between objects and words based solely on the
co-occurrence between objects and words. In particular, a learning trial in our
learning scenario consists of the presentation of objects together
with a target word, which refers to one of the objects in the context. We find
that the learning times are distributed exponentially and the learning rates
are given by in the case the target
words are sampled randomly and by in the
case they follow a deterministic presentation sequence. This learning
performance is much superior to those exhibited by humans and more realistic
learning algorithms in cross-situational experiments. We show that introduction
of discrimination limitations using Weber's law and forgetting reduce the
performance of the associative algorithm to the human level
Self-Optimizing and Pareto-Optimal Policies in General Environments based on Bayes-Mixtures
The problem of making sequential decisions in unknown probabilistic
environments is studied. In cycle action results in perception
and reward , where all quantities in general may depend on the complete
history. The perception and reward are sampled from the (reactive)
environmental probability distribution . This very general setting
includes, but is not limited to, (partial observable, k-th order) Markov
decision processes. Sequential decision theory tells us how to act in order to
maximize the total expected reward, called value, if is known.
Reinforcement learning is usually used if is unknown. In the Bayesian
approach one defines a mixture distribution as a weighted sum of
distributions \nu\in\M, where \M is any class of distributions including
the true environment . We show that the Bayes-optimal policy based
on the mixture is self-optimizing in the sense that the average value
converges asymptotically for all \mu\in\M to the optimal value achieved by
the (infeasible) Bayes-optimal policy which knows in advance. We
show that the necessary condition that \M admits self-optimizing policies at
all, is also sufficient. No other structural assumptions are made on \M. As
an example application, we discuss ergodic Markov decision processes, which
allow for self-optimizing policies. Furthermore, we show that is
Pareto-optimal in the sense that there is no other policy yielding higher or
equal value in {\em all} environments \nu\in\M and a strictly higher value in
at least one.Comment: 15 page
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