Abstract: A robotic arm and hand controlled by simulated
neurons is presented. The robot makes use of a biological neuron
simulator using a point neural model. The neurons and synapses are
organised to create a finite state automaton including neural inputs
from sensors, and outputs to effectors. The robot performs a simple
pick-and-place task. This work is a proof of concept study for a
longer term approach. It is hoped that further work will lead to
more effective and flexible robots. As another benefit, it is hoped that
further work will also lead to a better understanding of human and
other animal neural processing, particularly for physical motion. This
is a multidisciplinary approach combining cognitive neuroscience,
robotics, and psychology.
Abstract: This paper presents a four-dimensional computational
model, k-neighborhood template A-type three-dimensional bounded
cellular acceptor (abbreviated as A-3BCA(k)), and discusses the
hierarchical properties. An A-3BCA(k) is a four-dimensional
automaton which consists of a pair of a converter and a
configuration-reader. The former converts the given four-dimensional
tape to the three- and two- dimensional configuration and
the latter determines the acceptance or nonacceptance of given
four-dimensional tape whether or not the derived two-dimensional
configuration is accepted. We mainly investigate the difference of the
accepting power based on the difference of the configuration-reader.
It is shown that the difference of the accepting power of the
configuration-reader tends to affect directly that of the A-3BCA(k)
for the case when the converter is deterministic. On the other hand,
results are not analogous for the nondeterministic case.
Abstract: The various types of frequent pattern discovery
problem, namely, the frequent itemset, sequence and graph mining
problems are solved in different ways which are, however, in certain
aspects similar. The main approach of discovering such patterns can
be classified into two main classes, namely, in the class of the levelwise
methods and in that of the database projection-based methods.
The level-wise algorithms use in general clever indexing structures
for discovering the patterns. In this paper a new approach is proposed
for discovering frequent sequences and tree-like patterns efficiently
that is based on the level-wise issue. Because the level-wise
algorithms spend a lot of time for the subpattern testing problem, the
new approach introduces the idea of using automaton theory to solve
this problem.
Abstract: The hybridization of artificial immune system with
cellular automata (CA-AIS) is a novel method. In this hybrid model,
the cellular automaton within each cell deploys the artificial immune
system algorithm under optimization context in order to increase its
fitness by using its neighbor-s efforts. The hybrid model CA-AIS is
introduced to fix the standard artificial immune system-s weaknesses.
The credibility of the proposed approach is evaluated by simulations
and it shows that the proposed approach achieves better results
compared to standard artificial immune system.
Abstract: In this paper the use of sequential machines for recognizing actions taken by the objects detected by a general tracking algorithm is proposed. The system may deal with the uncertainty inherent in medium-level vision data. For this purpose, fuzzification of input data is performed. Besides, this transformation allows to manage data independently of the tracking application selected and enables adding characteristics of the analyzed scenario. The representation of actions by means of an automaton and the generation of the input symbols for finite automaton depending on the object and action compared are described. The output of the comparison process between an object and an action is a numerical value that represents the membership of the object to the action. This value is computed depending on how similar the object and the action are. The work concludes with the application of the proposed technique to identify the behavior of vehicles in road traffic scenes.
Abstract: In theoretical computer science, the Turing machine has played a number of important roles in understanding and exploiting basic concepts and mechanisms in computing and information processing [20]. It is a simple mathematical model of computers [9]. After that, M.Blum and C.Hewitt first proposed two-dimensional automata as a computational model of two-dimensional pattern processing, and investigated their pattern recognition abilities in 1967 [7]. Since then, a lot of researchers in this field have been investigating many properties about automata on a two- or three-dimensional tape. On the other hand, the question of whether processing fourdimensional digital patterns is much more difficult than two- or threedimensional ones is of great interest from the theoretical and practical standpoints. Thus, the study of four-dimensional automata as a computasional model of four-dimensional pattern processing has been meaningful [8]-[19],[21]. This paper introduces a cooperating system of four-dimensional finite automata as one model of four-dimensional automata. A cooperating system of four-dimensional finite automata consists of a finite number of four-dimensional finite automata and a four-dimensional input tape where these finite automata work independently (in parallel). Those finite automata whose input heads scan the same cell of the input tape can communicate with each other, that is, every finite automaton is allowed to know the internal states of other finite automata on the same cell it is scanning at the moment. In this paper, we mainly investigate some accepting powers of a cooperating system of eight- or seven-way four-dimensional finite automata. The seven-way four-dimensional finite automaton is an eight-way four-dimensional finite automaton whose input head can move east, west, south, north, up, down, or in the fu-ture, but not in the past on a four-dimensional input tape.
Abstract: A Watson-Crick automaton is recently introduced as a
computational model of DNA computing framework. It works on
tapes consisting of double stranded sequences of symbols. Symbols
placed on the corresponding cells of the double-stranded sequences are
related by a complimentary relation. In this paper, we investigate a
variation of Watson-Crick automata in which both heads read the tape
in reverse directions. They are called reverse Watson-Crick finite
automata (RWKFA). We show that all of following four classes, i.e.,
simple, 1-limited, all-final, all-final and simple, are equal to
non-restricted version of RWKFA.
Abstract: Model-based approaches have been applied successfully
to a wide range of tasks such as specification, simulation, testing, and
diagnosis. But one bottleneck often prevents the introduction of these
ideas: Manual modeling is a non-trivial, time-consuming task.
Automatically deriving models by observing and analyzing running
systems is one possible way to amend this bottleneck. To
derive a model automatically, some a-priori knowledge about the
model structure–i.e. about the system–must exist. Such a model
formalism would be used as follows: (i) By observing the network
traffic, a model of the long-term system behavior could be generated
automatically, (ii) Test vectors can be generated from the model,
(iii) While the system is running, the model could be used to diagnose
non-normal system behavior.
The main contribution of this paper is the introduction of a model
formalism called 'probabilistic regression automaton' suitable for the
tasks mentioned above.