70,837 research outputs found
Genetic Evolution of Hierarchical Behavior Structures
The development of coherent and dynamic behaviors for mobile robots is an exceedingly complex endeavor ruled by task objectives, environmental dynamics and the interactions within the behavior structure. This paper discusses the use of genetic programming techniques and the unified behavior framework to develop effective control hierarchies using interchangeable behaviors and arbitration components. Given the number of possible variations provided by the framework, evolutionary programming is used to evolve the overall behavior design. Competitive evolution of the behavior population incrementally develops feasible solutions for the domain through competitive ranking. By developing and implementing many simple behaviors independently and then evolving a complex behavior structure suited to the domain, this approach allows for the reuse of elemental behaviors and eases the complexity of development for a given domain. Additionally, this approach has the ability to locate a behavior structure which a developer may not have previously considered, and whose ability exceeds expectations. The evolution of the behavior structure is demonstrated using agents in the Robocode environment, with the evolved structures performing up to 122 percent better than one crafted by an expert
A Hierarchical Approach to Protein Molecular Evolution
Biological diversity has evolved despite the essentially infinite complexity
of protein sequence space. We present a hierarchical approach to the efficient
searching of this space and quantify the evolutionary potential of our approach
with Monte Carlo simulations. These simulations demonstrate that non-homologous
juxtaposition of encoded structure is the rate-limiting step in the production
of new tertiary protein folds. Non-homologous ``swapping'' of low energy
secondary structures increased the binding constant of a simulated protein by
relative to base substitution alone. Applications of our approach
include the generation of new protein folds and modeling the molecular
evolution of disease.Comment: 15 pages. 2 figures. LaTeX styl
From Physical Aggression to Verbal Behavior: Language Evolution and Self-Domestication Feedback Loop
The dimensions of personality in humans and other animals: A comparative and evolutionary perspective
This paper considers the structure and proximate mechanisms of personality in humans and other animals. Significant similarities were found between personality structures and mechanisms across species in at least two broad traits: Extraversion and Neuroticism. The factor space tapped by these personality dimensions is viewed as a general integrative framework for comparative and evolutionary studies of personality in humans and other animals. Most probably, the cross-species similarities between the most broad personality dimensions like Extraversion and Neuroticism as well as other Big Five factors reflect conservative evolution: constrains on evolution imposed by physiological, genetic and cognitive mechanisms. Lower-order factors, which are more species- and situation-specific, would be adaptive, reflecting correlated selection on and trade-offs between many traits
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Automatic Generation of Cognitive Theories using Genetic Programming
Cognitive neuroscience is the branch of neuroscience that studies the neural mechanisms underpinning cognition and develops theories explaining them. Within cognitive neuroscience, computational neuroscience focuses on modeling behavior, using theories expressed as computer programs. Up to now, computational theories have been formulated by neuroscientists. In this paper, we present a new approach to theory development in neuroscience: the automatic generation and testing of cognitive theories using genetic programming. Our approach evolves from experimental data cognitive theories that explain âthe mental programâ that subjects use to solve a specific task. As an example, we have focused on a typical neuroscience experiment, the delayed-match-to-sample (DMTS) task. The main goal of our approach is to develop a tool that neuroscientists can use to develop better cognitive theories
An evolutionary behavioral model for decision making
For autonomous agents the problem of deciding what to do next becomes increasingly complex when acting in unpredictable and dynamic environments pursuing multiple and possibly conflicting goals. One of the most relevant behavior-based model that tries to deal with this problem is the one proposed by Maes, the Bbehavior Network model. This model proposes a set of behaviors as purposive perception-action units which are linked in a nonhierarchical network, and whose behavior selection process is orchestrated by spreading activation dynamics. In spite of being an adaptive model (in the sense of self-regulating its own behavior selection process), and despite the fact that several extensions have been proposed in order to improve the original model adaptability, there is not a robust model yet that can self-modify adaptively both the topological structure and the functional purpose\ud
of the network as a result of the interaction between the agent and its environment. Thus, this work proffers an innovative hybrid model driven by gene expression programming, which makes two main contributions: (1) given an initial set of meaningless and unconnected units, the evolutionary mechanism is able to build well-defined and robust behavior networks which are adapted and specialized to concrete internal agent's needs and goals; and (2)\ud
the same evolutionary mechanism is able to assemble quite\ud
complex structures such as deliberative plans (which operate in the long-term) and problem-solving strategies
Information Based Hierarchical Brain Organization/Evolution from the Perspective of the Informational Model of Consciousness
Introduction: This article discusses the brain hierarchical organization/evolution as a consequence of the information-induced brain
development, from the perspective of the Informational Model of Consciousness.
Analysis: In the frame of the Informational Model of Consciousness, a detailed info-neural analysis ispresented, concerning the specific
properties/functions of the informational system of the human body composed by the Center of Acquisition and Storing of Information, Center of
Decision and Command, Info-Emotional Center, Maintenance Informational System, Genetic Transmission System, Info Genetic Generator and Info-
Connection center, in relation with the neuro-connected brain areas, with a special attention to the Info-Connection and its specific properties.
Besides a meticulous analysis of the info-connections/neuro-functions of these centers, a special attention was paid to limbic/cingulate cortex
activities. Defined as a trust/confidence center, additional features are highlighted in correlation with the activity of the anterior cingulate cortex,
consisting in the intervention/moderation of amygdala emotional signals, conflicting opposite YES/NO data and error elimination in the favor of the
organism adaptation/survival, the intervention in the certainty/uncertainty balance to select a suitable pro-life information (antientropic effect), in
moderation of pain and in the stimulation of the empathic inter-human relations/communication. Representing the correspondence between the
informational subsystems and the brain area map, itis shown that the up/down integration of information by epigenetic mechanisms and the down/
up evolution are correlated.
Results: The analysis of the functions of the anterior cingulate opens new gates of investigations concerning the involved intimate mechanisms
at the level of cell microstructure, specifically on the compatibility with quantum assisted processes admitted by the Informational Model of
Consciousness and the quantum-based models The discussion on the information integration/codification by epigenetic mechanisms shows that
this process starts from the superior levels of brain conscious info-processing areas and progressively advances to the automatic/autonomic inferior
levels ofthe informational system, under insistent/repetitive cues/stress conditions, pointing out an hierarchical functional/anatomical structure of
the brain organization. Additional arguments are discussed, indicating thatthe down/up progressive scale representation is a suggestive illustration
of the brain evolution, induced/assisted/determined by information, accelerated at humans by the antientropic functions of the Info-Connection
center.
Conclusions: The hierarchical organization of the brain is a consequence of the integration process of information, defining its development
accordingly to the adaptation requirements for survival during successive evolution stages of the organism, information playing a determinant/key
role
Sub-structural Niching in Estimation of Distribution Algorithms
We propose a sub-structural niching method that fully exploits the problem
decomposition capability of linkage-learning methods such as the estimation of
distribution algorithms and concentrate on maintaining diversity at the
sub-structural level. The proposed method consists of three key components: (1)
Problem decomposition and sub-structure identification, (2) sub-structure
fitness estimation, and (3) sub-structural niche preservation. The
sub-structural niching method is compared to restricted tournament selection
(RTS)--a niching method used in hierarchical Bayesian optimization
algorithm--with special emphasis on sustained preservation of multiple global
solutions of a class of boundedly-difficult, additively-separable multimodal
problems. The results show that sub-structural niching successfully maintains
multiple global optima over large number of generations and does so with
significantly less population than RTS. Additionally, the market share of each
of the niche is much closer to the expected level in sub-structural niching
when compared to RTS
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