1,721,076 research outputs found
Intelligenza Artificiale
No full textIntelligenza Artificiale is the official journal of the Italian Association for Artificial Intelligence (AIxIA). Intelligenza Artificiale publishes rigorously reviewed articles (in English) in all areas of Artificial Intelligence, with a special attention to original contributions. It will also publish assessments of the state of the art in various areas of AI, and innovative system descriptions with appropriate evaluation
Attractor Landscape: A Bridge between Robotics and Synthetic Biology
No full textGenetic regulatory networks (GRNs) model the dynamics and interactions among genes. From a robotics perspective, GRNs are extremely interesting because they are capable of producing complex behaviors. Notably, cell differentiation can be modeled using GRNs, and the dynamics of this process can be studied by means of dynamical systems methods. In a nutshell, the state of a cell is represented by an attractor in the state space of a dynamical system, and the transitions between cell states correspond to transitions between attractors. This view suggests a visionary approach: apply the metaphor of landscape attractor to design specific cell dynamics that can match the attractor landscape required for attaining a target behavior in a robotic system. The constraints prescribed by the robotic application are just the correspondence between behavioral attractors in the robot and cell attractors in the cell, along with specific transitions between attractors. This perspective may lead to applications in biorobotics, and it may also help synthetic biology systems design, which may benefit from methods developed for complex dynamical systems. We believe that this level of abstraction can provide a common vocabulary and a shared set of categories between researchers in robotics and synthetic biology. In this paper, we elaborate on previous results on GRNs-controlled robots and propose some guidelines for making this approach viable, illustrating these concepts with examples and case studies in biorobotics
A preliminary analysis on metaheuristics methods applied to the haplotype inference problem,
No full textRobots, Cells and Baroque Music: Creativity as an Emergent Phenomenon
No full textAt a first glance, it might seem unlikely to find any common properties in robots’ behavior, cell dynamics and baroque music performance. Yet, from a systemic perspective they all share an essential and crucial emergent phenomenon: their dynamics is the result of the interaction
between their structure and the environment. In this perspective paper, we discuss this property and build upon it to outline a guiding principle for artificial creativity
Machine Improvisation Through Generalized Transition Probability Graphs
No full textImprovisation plays a cardinal role in the arts and is acknowledged to be a typical manifestation of creativity. In performing arts, an impromptu consists in playing extemporaneous sequences of actions, i.e.notes or movements, in accordance to some rules and constraints. Typically, a good improviser masters those constraints and can produce meaningful paths in the feasible space of allowed actions and can also explore some areas in the adjacencies of this space. From a computational perspective, one of the possible ways to capture this creative production is to make use of statistical learning mechanisms, which are also believed to be involved in human musical improvisation. At the basis of statistical learning are transitional probabilities between segments of a sequence and their following segments of symbols. In this paper we present preliminary results of a statistical learning model in which a transitional probability graph is computed from a set of sample pieces of music. This graph is subsequently generalized by applying a node similarity mechanism. This generalized graph is used for generating melodies that resemble improvisations in a given musical style
A third transition in science?
Full text linkSince Newton, classical and quantum physics depend upon the "Newtonian Paradigm". The relevant variables of the system are identified. For example, we identify the position and momentum of classical particles. Laws of motion in differential form connecting the variables are formulated. An example is Newton's three Laws of Motion. The boundary conditions creating the phase space of all possible values of the variables are defined. Then, given any initial condition, the differential equations of motion are integrated to yield an entailed trajectory in the pre-stated phase space. It is fundamental to the Newtonian Paradigm that the set of possibilities that constitute the phase space is always definable and fixed ahead of time.
This fails for the diachronic evolution of ever-new adaptations in any biosphere. Living cells achieve Constraint Closure and construct themselves. Thus, living cells, evolving via heritable variation and Natural selection, adaptively construct new-in-the-universe possibilities. We can neither define nor deduce the evolving phase space: We can use no mathematics based on Set Theory to do so. We cannot write or solve differential equations for the diachronic evolution of ever-new adaptations in a biosphere.
Evolving biospheres are outside the Newtonian Paradigm. There can be no Theory of Everything that entails all that comes to exist.
We face a third major transition in science beyond the Pythagorean dream that ``All is Number'' echoed by Newtonian physics.
However, we begin to understand the emergent creativity of an evolving biosphere: Emergence is not engineering
The Impact of Self-Loops on Boolean Networks Attractor Landscape and Implications for Cell Differentiation Modelling
Full text linkBoolean networks are a notable model of gene regulatory networks and, particularly, prominent theories discuss how they can capture cellular differentiation processes. One frequent motif in gene regulatory networks, especially in those circuits involved in cell differentiation, is autoregulation. In spite of this, the impact of autoregulation on Boolean network attractor landscape has not yet been extensively discussed in literature. In this paper we propose to model autoregulation as self-loops, and analyse how the number of attractors and their robustness may change once they are introduced in a well-known and widely used Boolean networks model, namely random Boolean networks. Results show that self-loops provide an evolutionary advantage in dynamic mechanisms of cells, by increasing both number and maximal robustness of attractors. These results provide evidence to the hypothesis that autoregulation is a straightforward functional component to consolidate cell dynamics, mainly in differentiation processes
Artificial Life and Evolutionary Computation - 13th Italian Workshop, WIVACE 2018 Parma, Italy, September 10–12, 2018 Revised Selected Papers
No full textThis volume of Communication in Computer and Information Science contains the
proceedings of WIVACE 2018, the XIII Workshop on Artificial Life and Evolutionary
Computation. The event was successfully held on the Sciences Campus of the
University of Parma, Italy, during September 10–12, 2018. WIVACE aimed to bring
together researchers working in the field of artificial life and evolutionary computation
to present and share their research in a multidisciplinary context. The workshop
provided a forum for the discussion of new research directions and applications in
different fields, where different disciplines effectively meet. Some examples of these
interdisciplinary topics are: Bioinformatics and Computational Biology, Bioinspired
Algorithms and Robotics, Complex Systems, Evolutionary Computation, Genetic
Algorithms, Modeling and Simulation of Artificial, Biological, Social and Business
Intelligence Systems, Synthetic and Systems Biology and Chemistry, Theories and
Applications of Artificial Life, Quantum Computing.
Applications of Artificial Life, Quantum Computing.
WIVACE 2018 received 30 total submissions, 24 of which were selected for
presentation at the workshop as either long or short talks. We accepted 12 high-quality
papers (40% of the original submissions) for publication in an extended version in this
proceedings volume, after a single-blind review round performed by at least three
Program Committee members. Submissions and participants in WIVACE 2018 came
from 13 different countries making WIVACE an increasingly international event
despite its origins as an Italian workshop. Following this ever-increasing international
spirit, future WIVACE editions are expected to be held also outside Italy. Many people
contributed to this successful edition. We express our gratitude to the authors for
submitting their works, to the members of the Program Committee for devoting so
much effort to reviewing papers despite a tight schedule, and finally to the invited
speakers of both WIVACE and the special session on quantum computing
Stochastic local search for large-scale instances of the haplotype inference problem by parsimony
No full textHaplotype Inference is a challenging problem in bioinformatics that consists in inferring the basic genetic constitution of diploid organisms (in the basis of their genotype. This information allows researchers to perform association studies for the genetic variants involved in diseases and the individual responses to therapeutic agents. A notable approach to the problem is to encode it as a combinatorial problem (under certain hypotheses, such as the pure parsimony criterion) and to solve it using off-the-shelf combinatorial optimization techniques. The main methods applied to Haplotype Inference are either simple greedy heuristic or exact methods (integer Linear Programming, Semidefinite Programming, SAT and pseudo-boolean encoding) that, at present, are adequate only for moderate size instances. In this paper, we present and discuss an approach based on the combination of local search metaheuristics and a reduction procedure based on an analysis of the problem structure. Some relevant design issues are first described, then a family of local search metaheuristics is defined to tackle the Haplotype Inference. Results on common Haplotype Inference benchmarks show that the approach achieves a good trade-off between solution quality and execution time
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