2,399 research outputs found
Automata Tutor v3
Computer science class enrollments have rapidly risen in the past decade.
With current class sizes, standard approaches to grading and providing
personalized feedback are no longer possible and new techniques become both
feasible and necessary. In this paper, we present the third version of Automata
Tutor, a tool for helping teachers and students in large courses on automata
and formal languages. The second version of Automata Tutor supported automatic
grading and feedback for finite-automata constructions and has already been
used by thousands of users in dozens of countries. This new version of Automata
Tutor supports automated grading and feedback generation for a greatly extended
variety of new problems, including problems that ask students to create regular
expressions, context-free grammars, pushdown automata and Turing machines
corresponding to a given description, and problems about converting between
equivalent models - e.g., from regular expressions to nondeterministic finite
automata. Moreover, for several problems, this new version also enables
teachers and students to automatically generate new problem instances. We also
present the results of a survey run on a class of 950 students, which shows
very positive results about the usability and usefulness of the tool
Capturing CFLs with Tree Adjoining Grammars
We define a decidable class of TAGs that is strongly equivalent to CFGs and
is cubic-time parsable. This class serves to lexicalize CFGs in the same manner
as the LCFGs of Schabes and Waters but with considerably less restriction on
the form of the grammars. The class provides a normal form for TAGs that
generate local sets in much the same way that regular grammars provide a normal
form for CFGs that generate regular sets.Comment: 8 pages, 3 figures. To appear in proceedings of ACL'9
Studying the capacity of cellular encoding to generate feedforward neural network topologies
Proceeding of: IEEE International Joint Conference on Neural Networks, IJCNN 2004, Budapest, 25-29 July 2004Many methods to codify artificial neural networks have been developed to avoid the disadvantages of direct encoding schema, improving the search into the solution's space. A method to analyse how the search space is covered and how are the movements along search process applying genetic operators is needed in order to evaluate the different encoding strategies for multilayer perceptrons (MLP). In this paper, the generative capacity, this is how the search space is covered for a indirect scheme based on cellular systems, is studied. The capacity of the methods to cover the search space (topologies of MLP space) is compared with the direct encoding scheme.Publicad
Generative capacities of cellular automata codification for evolution of NN codification
Proceeding of: International Conference on Artificial Neural Networks. ICANN 2002, Madrid, Spain, August 28-30, 2002Automatic methods for designing artificial neural nets are desired to avoid the laborious and erratically human expert’s job. Evolutionary computation has been used as a search technique to find appropriate NN architectures. Direct and indirect encoding methods are used to codify the net architecture into the chromosome. A reformulation of an indirect encoding method, based on two bi-dimensional cellular automata, and its generative capacity are presented.Publicad
Active Self-Assembly of Algorithmic Shapes and Patterns in Polylogarithmic Time
We describe a computational model for studying the complexity of
self-assembled structures with active molecular components. Our model captures
notions of growth and movement ubiquitous in biological systems. The model is
inspired by biology's fantastic ability to assemble biomolecules that form
systems with complicated structure and dynamics, from molecular motors that
walk on rigid tracks and proteins that dynamically alter the structure of the
cell during mitosis, to embryonic development where large-scale complicated
organisms efficiently grow from a single cell. Using this active self-assembly
model, we show how to efficiently self-assemble shapes and patterns from simple
monomers. For example, we show how to grow a line of monomers in time and
number of monomer states that is merely logarithmic in the length of the line.
Our main results show how to grow arbitrary connected two-dimensional
geometric shapes and patterns in expected time that is polylogarithmic in the
size of the shape, plus roughly the time required to run a Turing machine
deciding whether or not a given pixel is in the shape. We do this while keeping
the number of monomer types logarithmic in shape size, plus those monomers
required by the Kolmogorov complexity of the shape or pattern. This work thus
highlights the efficiency advantages of active self-assembly over passive
self-assembly and motivates experimental effort to construct general-purpose
active molecular self-assembly systems
Methods for Structural Pattern Recognition: Complexity and Applications
Katedra kybernetik
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