359 research outputs found
Learning Behavior Trees with Genetic Programming in Unpredictable Environments
Modern industrial applications require robots to be able to operate in
unpredictable environments, and programs to be created with a minimal effort,
as there may be frequent changes to the task. In this paper, we show that
genetic programming can be effectively used to learn the structure of a
behavior tree (BT) to solve a robotic task in an unpredictable environment.
Moreover, we propose to use a simple simulator for the learning and demonstrate
that the learned BTs can solve the same task in a realistic simulator, reaching
convergence without the need for task specific heuristics. The learned solution
is tolerant to faults, making our method appealing for real robotic
applications
Biomimetic Engineering
Humankind is a privileged animal species for many reasons. A remarkable one is its
ability to conceive and manufacture objects. Human industry is indeed leading the
various winning strategies (along with language and culture) that has permitted this
primate to extraordinarily increase its life expectancy and proliferation rate. (It is indeed
so successful, that it now threatens the whole planet.) The design of this industry kicks
off in the brain, a computing machine particularly good at storing, recognizing and
associating patterns. Even in a time when human beings tend to populate non-natural,
man-made environments, the many forms, colorings, textures and behaviors of nature
continuously excite our senses and blend in our thoughts, even more deeply during
childhood. Then, it would be exaggerated to say that Biomimetics is a brand new
strategy. As long as human creation is based on previously acquired knowledge and
experiences, it is not surprising that engineering, the arts, and any form of expression, is
influenced by nature’s way to some extent.
The design of human industry has evolved from very simple tools, to complex
engineering devices. Nature has always provided us with a rich catalog of excellent
materials and inspiring designs. Now, equipped with new machinery and techniques, we
look again at Nature. We aim at mimicking not only its best products, but also its design
principles.
Organic life, as we know it, is indeed a vast pool of diversity. Living matter inhabits
almost every corner of the terrestrial ecosphere. From warm open-air ecosystems to the
extreme conditions of hot salt ponds, living cells have found ways to metabolize the
sources of energy, and get organized in complex organisms of specialized tissues and organs that adapt themselves to the environment, and can modify the environment to
their own needs as well. Life on Earth has evolved such a diverse portfolio of species
that the number of designs, mechanisms and strategies that can actually be abstracted is
astonishing. As August Krogh put it: "For a large number of problems there will be
some animal of choice, on which it can be most conveniently studied".
The scientific method starts with a meticulous observation of natural phenomena, and
humans are particularly good at that game. In principle, the aim of science is to
understand the physical world, but an observer’s mind can behave either as an engineer
or as a scientist. The minute examination of the many living forms that surround us has
led to the understanding of new organizational principles, some of which can be
imported in our production processes. In practice, bio-inspiration can arise at very
different levels of observation: be it social organization, the shape of an organism, the
structure and functioning of organs, tissular composition, cellular form and behavior, or
the detailed structure of molecules. Our direct experience of the wide portfolio of
species found in nature, and their particular organs, have clearly favored that the initial
models would come from the organism and organ levels. But the development of new
techniques (on one hand to observe the micro- and nanostructure of living beings, and
on the other to simulate the complex behavior of social communities) have significantly
extended the domain of interest
The Past, Present, and Future of Artificial Life
For millennia people have wondered what makes the living different from the non-living. Beginning in the mid-1980s, artificial life has studied living systems using a synthetic approach: build life in order to understand it better, be it by means of software, hardware, or wetware. This review provides a summary of the advances that led to the development of artificial life, its current research topics, and open problems and opportunities. We classify artificial life research into fourteen themes: origins of life, autonomy, self-organization, adaptation (including evolution, development, and learning), ecology, artificial societies, behavior, computational biology, artificial chemistries, information, living technology, art, and philosophy. Being interdisciplinary, artificial life seems to be losing its boundaries and merging with other fields
Detecting tax evasion: a co-evolutionary approach
We present an algorithm that can anticipate tax evasion by modeling the co-evolution of tax schemes with auditing policies. Malicious tax non-compliance, or evasion, accounts for billions of lost revenue each year. Unfortunately when tax administrators change the tax laws or auditing procedures to eliminate known fraudulent schemes another potentially more profitable scheme takes it place. Modeling both the tax schemes and auditing policies within a single framework can therefore provide major advantages. In particular we can explore the likely forms of tax schemes in response to changes in audit policies. This can serve as an early warning system to help focus enforcement efforts. In addition, the audit policies can be fine tuned to help improve tax scheme detection. We demonstrate our approach using the iBOB tax scheme and show it can capture the co-evolution between tax evasion and audit policy. Our experiments shows the expected oscillatory behavior of a biological co-evolving system
Review, challenges, design, and development
Peres, F., & Castelli, M. (2021). Combinatorial optimization problems and metaheuristics: Review, challenges, design, and development. Applied Sciences (Switzerland), 11(14), 1-39. [6449]. https://doi.org/10.3390/app11146449In the past few decades, metaheuristics have demonstrated their suitability in addressing complex problems over different domains. This success drives the scientific community towards the definition of new and better-performing heuristics and results in an increased interest in this research field. Nevertheless, new studies have been focused on developing new algorithms without providing consolidation of the existing knowledge. Furthermore, the absence of rigor and formalism to classify, design, and develop combinatorial optimization problems and metaheuristics represents a challenge to the field’s progress. This study discusses the main concepts and challenges in this area and proposes a formalism to classify, design, and code combinatorial optimization problems and metaheuristics. We believe these contributions may support the progress of the field and increase the maturity of metaheuristics as problem solvers analogous to other machine learning algorithms.publishersversionpublishe
- …