Open problems in artificial life

This article lists fourteen open problems in artificial life, each of which is a grand challenge requiring a major advance on a fundamental issue for its solution. Each problem is briefly explained, and, where deemed helpful, some promising paths to its solution are indicated

Progress in the producer-scrounger game : information use and spatial models

Les animaux grégaires en quête de ressources peuvent soit consacrer leurs efforts à la recherche (stratégie producteur) ou soit attendre que les producteurs réussissent à trouver ces ressources pour les y rejoindre (stratégie chapardeur). La profitabilité de chaque option peut être analysée par le jeu producteur-chapardeur. Ce jeu a été largement exploré aux plans théorique et empirique, mais plusieurs aspects demeurent toujours inexplorés. J'ai développé cinq modèles afin d'explorer l'approvisionnement social en lien avec l'utilisation d'information et les contraintes spatiales. Le premier modèle concerne l'évolution de règles d'apprentissage, des expressions mathématiques décrivant la valeur qu'un animal accorde aux options producteur et chapardeur en fonction des gains obtenus. J'ai démontré que la règle du relative pay-off sum est évolutivement stable et donc la meilleure disponible. Les paramètres de la règle attendue demeurent intrigants et demandent maintenant à être éplorés au niveau empirique. Le second modèle explorés plutôt l'effet de l'usage d'information sociale (chapardeur) chez un prédateur en examinant son effet sur l'évolution du niveau d'agrégation de ses proies. Le modèle démontre que les proies évoluent à différents niveaux d'agrégation en réponse à l'usage d'information sociale par leurs prédateurs et que cette relation affecte à la fois l'efficacité de recherche du prédateur et la survie des proies. Le troisième modèle teste l'hypothèse, générée à partir de recherche empirique sur les oies cendrées, selon laquelle la variation du niveau de hardiesse serait associée à un dimorphisme de producteurs hardis et de chapardeurs poltrons (bold et shy, respectivement) dans le jeu producteur-chapardeur. Le modèle réfute l'existence d'un tel dimorphisme, mais démontre néanmoins un effet environnemental fort des paramètres de l'approvisionnement social sur le niveau de hardiesse d'une population. Ce résultat a d'importantes implications pour le rôle de l'utilisation d'information et les effets spatiaux dans la régulation des relations entre les producteurs et les chapardeurs. J'ai développé à partir d'une approche d'automate cellulaire un modèle producteur-chapardeur pour déterminer si une règle simple (rule of thumb) fondée sur l'apprentissage social élémentaire dans un contexte spatialement explicite pouvait prédire l'atteinte d'un équilibre producteur-chapardeur. Les résultats démontrent que l'ajout de cette règle simple génère à la fois une flexibilité comportementale significative et des dynamiques complexes qui ne sont pas habituelles à ce genre de systèmes simples. Le modèle lie l'usage d'information sociale à la structure spatiale dans un modèle déterministe. Enfin, avec le cinquième modèle j'ai exploré les effets de la géométrie du paysage (la façon dont l'espace est représenté, habituellement un quadrillage régulier) sur le jeu producteur-chapardeur. Il appert que les représentations spatiales sont un déterminant-clé dans la manière dont un jeu d'approvisionnement social d'alimentation peut réellement rendre compte de l'approvisionnement des animaux. \ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : l'approvisionnement social, effets spatiaux, l'utilisation des informations, l'apprentissage, personnalités des animau

Evolutionary games on graphs

Game theory is one of the key paradigms behind many scientific disciplines from biology to behavioral sciences to economics. In its evolutionary form and especially when the interacting agents are linked in a specific social network the underlying solution concepts and methods are very similar to those applied in non-equilibrium statistical physics. This review gives a tutorial-type overview of the field for physicists. The first three sections introduce the necessary background in classical and evolutionary game theory from the basic definitions to the most important results. The fourth section surveys the topological complications implied by non-mean-field-type social network structures in general. The last three sections discuss in detail the dynamic behavior of three prominent classes of models: the Prisoner's Dilemma, the Rock-Scissors-Paper game, and Competing Associations. The major theme of the review is in what sense and how the graph structure of interactions can modify and enrich the picture of long term behavioral patterns emerging in evolutionary games.Comment: Review, final version, 133 pages, 65 figure

Heuristic search methods and cellular automata modelling for layout design

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Spatial layout design must consider not only ease of movement for pedestrians under normal conditions, but also their safety in panic situations, such as an emergency evacuation in a theatre, stadium or hospital. Using pedestrian simulation statistics, the movement of crowds can be used to study the consequences of different spatial layouts. Previous works either create an optimal spatial arrangement or an optimal pedestrian circulation. They do not automatically optimise both problems simultaneously. Thus, the idea behind the research in this thesis is to achieve a vital architectural design goal by automatically producing an optimal spatial layout that will enable smooth pedestrian flow. The automated process developed here allows the rapid identification of layouts for large, complex, spatial layout problems. This is achieved by using Cellular Automata (CA) to model pedestrian simulation so that pedestrian flow can be explored at a microscopic level and designing a fitness function for heuristic search that maximises these pedestrian flow statistics in the CA simulation. An analysis of pedestrian flow statistics generated from feasible novel design solutions generated using the heuristic search techniques (hill climbing, simulated annealing and genetic algorithm style operators) is conducted. The statistics that are obtained from the pedestrian simulation is used to measure and analyse pedestrian flow behaviour. The analysis from the statistical results also provides the indication of the quality of the spatial layout design generated. The technique has shown promising results in finding acceptable solutions to this problem when incorporated with the pedestrian simulator when demonstrated on simulated and real-world layouts with real pedestrian data.This study was funded by the University Science of Malaysia and Kementerian Pengajian Tinggi Malaysia

Evolution from the ground up with Amee – From basic concepts to explorative modeling

Evolutionary theory has been the foundation of biological research for about a century now, yet over the past few decades, new discoveries and theoretical advances have rapidly transformed our understanding of the evolutionary process. Foremost among them are evolutionary developmental biology, epigenetic inheritance, and various forms of evolu- tionarily relevant phenotypic plasticity, as well as cultural evolution, which ultimately led to the conceptualization of an extended evolutionary synthesis. Starting from abstract principles rooted in complexity theory, this thesis aims to provide a unified conceptual understanding of any kind of evolution, biological or otherwise. This is used in the second part to develop Amee, an agent-based model that unifies development, niche construction, and phenotypic plasticity with natural selection based on a simulated ecology. Amee is implemented in Utopia, which allows performant, integrated implementation and simulation of arbitrary agent-based models. A phenomenological overview over Amee’s capabilities is provided, ranging from the evolution of ecospecies down to the evolution of metabolic networks and up to beyond-species-level biological organization, all of which emerges autonomously from the basic dynamics. The interaction of development, plasticity, and niche construction has been investigated, and it has been shown that while expected natural phenomena can, in principle, arise, the accessible simulation time and system size are too small to produce natural evo-devo phenomena and –structures. Amee thus can be used to simulate the evolution of a wide variety of processes

Towards a Boolean network-based Computational Model for Cell Differentiation and its applications to Robotics

Living organisms are the ultimate product of a series of complex processes that take place within—and among—biological cells. Most of these processes, such as cell differentiation, are currently poorly understood. Cell differentiation is the process by which cells progressively specialise. Being a fundamental process within cells, its dysregulations have dramatic implications in biological organisms ranging from developmental issues to cancer formation. The thesis objective is to contribute to the progress in the understanding of cell differentiation and explore the applications of its properties for designing artificial systems. The proposed approach, which relies on Boolean networks based modelling and on the theory of dynamical systems, aims at investigating the general mechanisms underlying cell differentiation. The results obtained contribute to taking a further step towards the formulation of a general theoretical framework—so far missing—for cellular differentiation. We conducted an in-depth analysis of the impact of self-loops in random Boolean networks ensembles. We proposed a new model of differentiation driven by a simplified bio-inspired methylation mechanism in Boolean models of genetic regulatory networks. On the artificial side, by introducing the conceptual metaphor of the “attractor landscape” and related proofs of concept that support its potential, we paved the way for a new research direction in robotics called behavioural differentiation robotics: a branch of robotics dealing with the designing of robots capable of expressing different behaviours in a way similar to that of biological cells that undergo differentiation. The implications of the results achieved may have beneficial effects on medical research. Indeed, the proposed approach can foster new questions, experiments and in turn, models that hopefully in the next future will take us to cure differentiation-related diseases such as cancer. Our work may also contribute to address questions concerning the evolution of complex behaviours and to help design robust and adaptive robots

Increasing chances of survival for malware using theory of natural selection and the selfish gene

Malware, short for malicious software, is used as a general term for computer viruses, Trojan horses, worms, and other harmful software or code. Malware authors try to obfuscate their code in order to evade antiviral programs. Different analysis techniques are used by antiviral programs in order to detect different encryption and obfuscation methods. Survivability of malware becomes the main concern for an attacker since the malware should usually be able to spread to other computers; use resources of victim's computer; and create new copies of itself. In this thesis, inspired by Darwin's theory of natural selection and the selfish gene concept explained by Richard Dawkins, we propose novel methods which increase the chance of survivability for malware. We implement selfishness, altruistic behavior, mimicry, group selection, and similar behavior models into our experimental malware and we also test our techniques against existing solutions. We develop tools in order to enhance existing malware with features presented in this thesis. Effectiveness of proposed techniques are presented and an experimental test is carried out with a dataset containing more than 300.000 malware samples. Group behavior models are also introduced and methods proposed for enhancing botnets to have better stability (Evolutionarily stable botnet)

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp