171 research outputs found
A Tale of Two Animats: What does it take to have goals?
What does it take for a system, biological or not, to have goals? Here, this
question is approached in the context of in silico artificial evolution. By
examining the informational and causal properties of artificial organisms
('animats') controlled by small, adaptive neural networks (Markov Brains), this
essay discusses necessary requirements for intrinsic information, autonomy, and
meaning. The focus lies on comparing two types of Markov Brains that evolved in
the same simple environment: one with purely feedforward connections between
its elements, the other with an integrated set of elements that causally
constrain each other. While both types of brains 'process' information about
their environment and are equally fit, only the integrated one forms a causally
autonomous entity above a background of external influences. This suggests that
to assess whether goals are meaningful for a system itself, it is important to
understand what the system is, rather than what it does.Comment: This article is a contribution to the FQXi 2016-2017 essay contest
"Wandering Towards a Goal
When is an action caused from within? Quantifying the causal chain leading to actions in simulated agents
An agent's actions can be influenced by external factors through the inputs
it receives from the environment, as well as internal factors, such as memories
or intrinsic preferences. The extent to which an agent's actions are "caused
from within", as opposed to being externally driven, should depend on its
sensor capacity as well as environmental demands for memory and
context-dependent behavior. Here, we test this hypothesis using simulated
agents ("animats"), equipped with small adaptive Markov Brains (MB) that evolve
to solve a perceptual-categorization task under conditions varied with regards
to the agents' sensor capacity and task difficulty. Using a novel formalism
developed to identify and quantify the actual causes of occurrences ("what
caused what?") in complex networks, we evaluate the direct causes of the
animats' actions. In addition, we extend this framework to trace the causal
chain ("causes of causes") leading to an animat's actions back in time, and
compare the obtained spatio-temporal causal history across task conditions. We
found that measures quantifying the extent to which an animat's actions are
caused by internal factors (as opposed to being driven by the environment
through its sensors) varied consistently with defining aspects of the task
conditions they evolved to thrive in.Comment: Submitted and accepted to Alife 2019 conference. Revised version:
edits include adding more references to relevant work and clarifying minor
points in response to reviewer
The Role of Conditional Independence in the Evolution of Intelligent Systems
Systems are typically made from simple components regardless of their
complexity. While the function of each part is easily understood, higher order
functions are emergent properties and are notoriously difficult to explain. In
networked systems, both digital and biological, each component receives inputs,
performs a simple computation, and creates an output. When these components
have multiple outputs, we intuitively assume that the outputs are causally
dependent on the inputs but are themselves independent of each other given the
state of their shared input. However, this intuition can be violated for
components with probabilistic logic, as these typically cannot be decomposed
into separate logic gates with one output each. This violation of conditional
independence on the past system state is equivalent to instantaneous
interaction --- the idea is that some information between the outputs is not
coming from the inputs and thus must have been created instantaneously. Here we
compare evolved artificial neural systems with and without instantaneous
interaction across several task environments. We show that systems without
instantaneous interactions evolve faster, to higher final levels of
performance, and require fewer logic components to create a densely connected
cognitive machinery.Comment: Original Abstract submitted to the GECCO conference 2017 Berli
PyPhi: A toolbox for integrated information theory
Integrated information theory provides a mathematical framework to fully
characterize the cause-effect structure of a physical system. Here, we
introduce PyPhi, a Python software package that implements this framework for
causal analysis and unfolds the full cause-effect structure of discrete
dynamical systems of binary elements. The software allows users to easily study
these structures, serves as an up-to-date reference implementation of the
formalisms of integrated information theory, and has been applied in research
on complexity, emergence, and certain biological questions. We first provide an
overview of the main algorithm and demonstrate PyPhi's functionality in the
course of analyzing an example system, and then describe details of the
algorithm's design and implementation.
PyPhi can be installed with Python's package manager via the command 'pip
install pyphi' on Linux and macOS systems equipped with Python 3.4 or higher.
PyPhi is open-source and licensed under the GPLv3; the source code is hosted on
GitHub at https://github.com/wmayner/pyphi . Comprehensive and
continually-updated documentation is available at https://pyphi.readthedocs.io/
. The pyphi-users mailing list can be joined at
https://groups.google.com/forum/#!forum/pyphi-users . A web-based graphical
interface to the software is available at
http://integratedinformationtheory.org/calculate.html .Comment: 22 pages, 4 figures, 6 pages of appendices. Supporting information
"S1 Calculating Phi" can be found in the ancillary file
Consciousness and Complexity: Neurobiological Naturalism and Integrated Information Theory
In this paper, we take a meta-theoretical stance and aim to compare and assess two conceptual frameworks that endeavor to explain phenomenal experience. In particular, we compare Feinberg & Mallatt’s Neurobiological Naturalism (NN) and Tononi’s and colleagues' Integrated Information Theory (IIT), given that the former pointed out some similarities between the two theories (Feinberg & Mallatt 2016c-d). To probe their similarity, we first give a general introduction to both frameworks. Next, we expound a ground plan for carrying out our analysis. We move on to articulate a philosophical profile of NN and IIT, addressing their ontological commitments and epistemological foundations. Finally, we compare the two point-by-point, also discussing how they stand on the issue of artificial consciousness
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