4,700 research outputs found
Algorithmic complexity for psychology: A user-friendly implementation of the coding theorem method
Kolmogorov-Chaitin complexity has long been believed to be impossible to
approximate when it comes to short sequences (e.g. of length 5-50). However,
with the newly developed \emph{coding theorem method} the complexity of strings
of length 2-11 can now be numerically estimated. We present the theoretical
basis of algorithmic complexity for short strings (ACSS) and describe an
R-package providing functions based on ACSS that will cover psychologists'
needs and improve upon previous methods in three ways: (1) ACSS is now
available not only for binary strings, but for strings based on up to 9
different symbols, (2) ACSS no longer requires time-consuming computing, and
(3) a new approach based on ACSS gives access to an estimation of the
complexity of strings of any length. Finally, three illustrative examples show
how these tools can be applied to psychology.Comment: to appear in "Behavioral Research Methods", 14 pages in journal
format, R package at http://cran.r-project.org/web/packages/acss/index.htm
Genetic algorithms with DNN-based trainable crossover as an example of partial specialization of general search
Universal induction relies on some general search procedure that is doomed to
be inefficient. One possibility to achieve both generality and efficiency is to
specialize this procedure w.r.t. any given narrow task. However, complete
specialization that implies direct mapping from the task parameters to
solutions (discriminative models) without search is not always possible. In
this paper, partial specialization of general search is considered in the form
of genetic algorithms (GAs) with a specialized crossover operator. We perform a
feasibility study of this idea implementing such an operator in the form of a
deep feedforward neural network. GAs with trainable crossover operators are
compared with the result of complete specialization, which is also represented
as a deep neural network. Experimental results show that specialized GAs can be
more efficient than both general GAs and discriminative models.Comment: AGI 2017 procedding, The final publication is available at
link.springer.co
Empirical Encounters with Computational Irreducibility and Unpredictability
There are several forms of irreducibility in computing systems, ranging from
undecidability to intractability to nonlinearity. This paper is an exploration
of the conceptual issues that have arisen in the course of investigating
speed-up and slowdown phenomena in small Turing machines. We present the
results of a test that may spur experimental approaches to the notion of
computational irreducibility. The test involves a systematic attempt to outrun
the computation of a large number of small Turing machines (all 3 and 4 state,
2 symbol) by means of integer sequence prediction using a specialized function
finder program. This massive experiment prompts an investigation into rates of
convergence of decision procedures and the decidability of sets in addition to
a discussion of the (un)predictability of deterministic computing systems in
practice. We think this investigation constitutes a novel approach to the
discussion of an epistemological question in the context of a computer
simulation, and thus represents an interesting exploration at the boundary
between philosophical concerns and computational experiments.Comment: 18 pages, 4 figure
- …