7,808 research outputs found
A Review on the Application of Natural Computing in Environmental Informatics
Natural computing offers new opportunities to understand, model and analyze
the complexity of the physical and human-created environment. This paper
examines the application of natural computing in environmental informatics, by
investigating related work in this research field. Various nature-inspired
techniques are presented, which have been employed to solve different relevant
problems. Advantages and disadvantages of these techniques are discussed,
together with analysis of how natural computing is generally used in
environmental research.Comment: Proc. of EnviroInfo 201
Enaction-Based Artificial Intelligence: Toward Coevolution with Humans in the Loop
This article deals with the links between the enaction paradigm and
artificial intelligence. Enaction is considered a metaphor for artificial
intelligence, as a number of the notions which it deals with are deemed
incompatible with the phenomenal field of the virtual. After explaining this
stance, we shall review previous works regarding this issue in terms of
artifical life and robotics. We shall focus on the lack of recognition of
co-evolution at the heart of these approaches. We propose to explicitly
integrate the evolution of the environment into our approach in order to refine
the ontogenesis of the artificial system, and to compare it with the enaction
paradigm. The growing complexity of the ontogenetic mechanisms to be activated
can therefore be compensated by an interactive guidance system emanating from
the environment. This proposition does not however resolve that of the
relevance of the meaning created by the machine (sense-making). Such
reflections lead us to integrate human interaction into this environment in
order to construct relevant meaning in terms of participative artificial
intelligence. This raises a number of questions with regards to setting up an
enactive interaction. The article concludes by exploring a number of issues,
thereby enabling us to associate current approaches with the principles of
morphogenesis, guidance, the phenomenology of interactions and the use of
minimal enactive interfaces in setting up experiments which will deal with the
problem of artificial intelligence in a variety of enaction-based ways
Lenia and Expanded Universe
We report experimental extensions of Lenia, a continuous cellular automata
family capable of producing lifelike self-organizing autonomous patterns. The
rule of Lenia was generalized into higher dimensions, multiple kernels, and
multiple channels. The final architecture approaches what can be seen as a
recurrent convolutional neural network. Using semi-automatic search e.g.
genetic algorithm, we discovered new phenomena like polyhedral symmetries,
individuality, self-replication, emission, growth by ingestion, and saw the
emergence of "virtual eukaryotes" that possess internal division of labor and
type differentiation. We discuss the results in the contexts of biology,
artificial life, and artificial intelligence.Comment: 8 pages, 5 figures, 1 table; submitted to ALIFE 2020 conferenc
A Membrane-Inspired Evolutionary Algorithm with a Population P System and its Application to Distribution System Recon guration
This paper develops a membrane-inspired evolutionary algorithm, PSMA,
which is designed by using a population P system and a quantum-inspired evolutionary
algorithm (QIEA). We use a population P system with three cells to organize three
types of QIEAs, where communications between cells are performed at the level of genes,
instead of the level of individuals reported in the existing membrane algorithms in the
literature. Knapsack problems are applied to discuss the parameter setting and to test
the effectiveness of PSMA. Experimental results show that PSMA is superior to four representative
QIEAs and our previous work with respect to the quality of solutions and the
elapsed time. We also use PSMA to solve the optimal distribution system reconfiguration
problem in power systems for minimizing the power loss.Junta de Andalucía P08-TIC-04200Ministerio de Ciencia e Innovación TIN-2009-1319
"Going back to our roots": second generation biocomputing
Researchers in the field of biocomputing have, for many years, successfully
"harvested and exploited" the natural world for inspiration in developing
systems that are robust, adaptable and capable of generating novel and even
"creative" solutions to human-defined problems. However, in this position paper
we argue that the time has now come for a reassessment of how we exploit
biology to generate new computational systems. Previous solutions (the "first
generation" of biocomputing techniques), whilst reasonably effective, are crude
analogues of actual biological systems. We believe that a new, inherently
inter-disciplinary approach is needed for the development of the emerging
"second generation" of bio-inspired methods. This new modus operandi will
require much closer interaction between the engineering and life sciences
communities, as well as a bidirectional flow of concepts, applications and
expertise. We support our argument by examining, in this new light, three
existing areas of biocomputing (genetic programming, artificial immune systems
and evolvable hardware), as well as an emerging area (natural genetic
engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin
Reactive direction control for a mobile robot: A locust-like control of escape direction emerges when a bilateral pair of model locust visual neurons are integrated
Locusts possess a bilateral pair of uniquely identifiable visual neurons that respond vigorously to
the image of an approaching object. These neurons are called the lobula giant movement
detectors (LGMDs). The locust LGMDs have been extensively studied and this has lead to the
development of an LGMD model for use as an artificial collision detector in robotic applications.
To date, robots have been equipped with only a single, central artificial LGMD sensor, and this
triggers a non-directional stop or rotation when a potentially colliding object is detected. Clearly,
for a robot to behave autonomously, it must react differently to stimuli approaching from
different directions. In this study, we implement a bilateral pair of LGMD models in Khepera
robots equipped with normal and panoramic cameras. We integrate the responses of these LGMD
models using methodologies inspired by research on escape direction control in cockroaches.
Using ‘randomised winner-take-all’ or ‘steering wheel’ algorithms for LGMD model integration,
the khepera robots could escape an approaching threat in real time and with a similar
distribution of escape directions as real locusts. We also found that by optimising these
algorithms, we could use them to integrate the left and right DCMD responses of real jumping
locusts offline and reproduce the actual escape directions that the locusts took in a particular
trial. Our results significantly advance the development of an artificial collision detection and
evasion system based on the locust LGMD by allowing it reactive control over robot behaviour.
The success of this approach may also indicate some important areas to be pursued in future
biological research
- …