No / Decisions on settlement location in the face of climate change and coastal inundation may have resulted in success, survival or even catastrophic failure for early settlers in many parts of the world. In this study, we investigate various questions related to how individuals respond to a palaeoenvironmental simulation, on an interactive tabletop device where participants have the opportunity to build a settlement on a coastal landscape, balancing safety, and access to resources, including sea and terrestrial foodstuffs, while taking into consideration the threat of rising sea levels. The results of the study were analyzed to consider whether decisions on settlement were predicated to be near to locations where previous structures were located, stigmergically, and whether later settler choice would fare better, and score higher, as time progressed. The proximity of settlements was investigated and the reasons for clustering were considered. The interactive simulation was exhibited to thousands of visitors at the 2012 Royal Society Summer Science Exhibition at the “Europe's Lost World” exhibit. 347 participants contributed to the simulation, providing a sufficiently large sample of data for analysis.

While a symbol based system externally, there is evidence that, internally the realization of language is much different. Through revisiting the foundations of our perceptions and assumptions about language and cognition, the presented argument will coalesce into an illustration of the unsuitability of symbolic systems for recreating the functions which we call "mind". Simply stated, computational models of mind are the latest arguments of the Cartesian paradigm. The thesis concludes with an argument for the exploration of a symbol-less architecture of cognition based upon a model found repeatedly throughout nature: swarms. Discussions of some of the impacts are presented.

Stigmergy refers to indirect communication that was originally found in biological systems. It is used for self-organization by ants, bees, and flocks of birds, by allowing individuals to focus on local information. Through local communication among individuals, larger patterns are formed without centralized communication. This self-organization is just one type of system studied within complex systems. Systems of ants, bees, and flocks of birds are considered complex because they exhibit emergent behavior: the outcome is more than the sum of the individual parts. Emergent behavior can be found in many other systems as well. One example is the Internet, which is a series of computers organized in a self-organized fashion. Complexity can also be defined through properties other than emergent behavior, such as existing on multiple scales. Many biological systems are multi-scale. For instance, cancer exists on many scales, including the sub-cellular and cellular levels. Many computing systems are also multi-scale, as there may be both individual and system-wide controls interacting together to determine the output. Many multi-agent systems would fall into this category, as would many large software systems. In this dissertation I examine complex systems in artificial intelligence and biology: the growth of cancer, population dynamics, emotions, multi-agent fault tolerance, and real-time strategic AI for games. My goal is twofold: a) to develop novel computational models of complex biological systems, and b) to tackle key AI research questions by proposing new algorithms and techniques that are inspired by those complex biological systems. In all of these cases I design variations on stigmergic communication to accomplish the task at hand. My contributions are a new agent-based cancer growth model, a proposed use of location communication for removing cancer, improved multi-agent fault tolerance through localized messaging, a new approach to modeling predator-prey dynamics using computational emotions, and improved strategic game AI through computational emotions.

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La modélisation et la simulation des systèmes complexes constitue une solution idéal pour comprendre ces systèmes. En effet, l'expérimentation virtuelle permet, par rapport à l'expérimentation réelle dans le champ d'étude considéré, d'apporter des réponses plus rapides aux questions posées sur ces systèmes, ce qui donne la possibilité de proposer des solutions en un temps adapté au contexte réel. Ce travail traite la question de la représentation de l'action humaine en prenant en compte sa dimension temporelle et spatiale aux échelles individuelle et collective. Cette question a déjà été traitée dans le domaine de l'intelligence artificielle, en général, et celui des systèmes agricoles, en particulier, qui constitue le domaine d'application de cette thèse. Les modèles proposés jusqu'à présent se basaient principalement sur la théorie de l'action planifiée en ne prenant en compte que la dimension temporelle de l'action. Les limites majeures de ces modèles résident dans leur complexité dans la mesure où il est difficile de pouvoir prédire l'ensemble des changements futurs dans l'environnement de l'acteur. Cela conduit à la nécessité de re-planifier fréquemment les actions afin d'obtenir des résultats cohérents. La deuxième limite réside dans l'écart qu'il peut y avoir entre les résultats des actions simulées et la réalité observée. En effet, un acteur ne réalise pas systématiquement les actions qu'il prévoit selon les situations réelles dans lesquelles il se trouve. Afin de pallier aux limites des modèles de l'action planifiée, nous avons développé un modèle de l'action humaine qui se base sur la théorie de l'action située. L'action est vue comme un processus doté d'une épaisseur temporelle émergent des situations créées par l'interaction entre l'acteur et son environnement dans le temps et dans l'espace. Notre modèle combine le concept d'affordance, le concept de stigmergie ainsi que la notion d'émergence. Nous proposons donc un système multi-agents dans lequel l'espace est explicitement représenté et partitionné en un ensemble de places. Le pilotage de chaque place est attribué à un agent abstrait. Celui-ci représente un observateur capable de détecter à tout instant les affordances émergentes sur sa place ainsi que de déclencher l'action appropriée. Les acteurs sont représentés comme des entités de l'environnement au même titre que les objets passifs. Ces entités de l'environnement portent un ensemble d'informations sur leurs capacités à exécuter ou subir des actions. Ces informations permettent aux agents, grâce aux méta-connaissances qu'ils détiennent de détecter les affordances. Celles-ci, une fois détectées, sont réifiées dans l'environnement et utilisées par les agents grâce à un mécanisme de sélection d'actions pour déterminer l'action qui sera finalement exécuter. La coordination des actions au niveau collectif se fait par stigmergie : les agents communiquent de façon implicite en utilisant un ensemble de marques qui sont une métaphore des phéromones des colonies de fourmis. Afin de montrer la pertinence du modèle proposé, un prototype appliqué au domaine des systèmes de production agricoles a été implémenté en utilisant la plateforme AnyLogic. / Simulation modelling of complex systems nowadays is an ideal solution to get a good understanding of these systems. In effect, compared with real experiments in the field of studies considered, virtual experiments allow one to quickly answer questions about these systems and provide solutions within a delay well adapted to their actual context. This thesis deals with the issue of human action representation, accounting with its temporal and spatial dimensions at individual and collective levels. This question has already been addressed in the field of Artificial intelligence in general and in the one of Agricultural systems in particular, the latter being the application domain of this thesis. The models proposed to date were mainly based upon the theory of planned action, explicitly accounting with the temporal dimension of action only. The main limits of these models lie in their complexity, because the ability to predict all future changes in actors' behaviors is far too difficult. This difficulty leads to the need of frequently re-planning the course of actions in order to get consistent results. The second drawback lies in the discrepancy that may arise between the results of simulated actions and actual observations. In effect, real actors do not realize systematically the actions they forecast according to the situations they actually encounter. In order to overcome the limits of planning models, we developed a model of human action based on the theory of situated action. Action is there viewed as a process endowed with a temporal thickness and emerging from the situations created by the interaction, through time and space, between the actor and its environment. Our model combines the concepts of affordance and stigmergy as well as the notion of emergence. Therefore we propose a multi-agents system within which space is explicitly represented and partitioned into a set of “places”. The control of each place is left to an abstract agent standing for an observer capable of detecting the affordances occurring on its place and trigger appropriate actions. Actors as well as passive objects are represented as “environmental entities”. These entities carry information about their capacity of performing or undergoing actions. This information allows the agents to detect affordances thanks to the meta-knowledge they hold. Once detected, these affordances are reified in the environment to be used to determine the action that will eventually be executed. Coordination of actions, at the collective level, is performed through stigmergy: the agents communicate implicitly between them using a set of marks as a metaphor of pheromons in ant colonies. To prove the relevance of the proposed model, a software prototype, applied to the domain of agricultural production systems, has been implemented with the simulation platform AnyLogic.

Πολλά συστήματα πολλαπλών διαμεσολαβητών είναι εμπνευσμένα από διάφορες κοινωνίες εντόμων, τα οποία συλλογικά πετυχαίνουν διάφορους στόχους οι οποίοι είναι αδύνατο να επιτευχθούν σε ατομικό επίπεδο. Στις περισσότερες περιπτώσεις, κινητήρια δύναμη στην «συνεργασία» και τη συλλογικότητα των εντόμων είναι η στιγματική συμπεριφορά (stigmergy). Ο όρος αυτός χρησιμοποιείται στη βιολογία για να περιγράψει τον τρόπο επίδρασης αλλαγών στο περιβάλλον που προέκυψαν από μια παρελθοντική συμπεριφορά κάποιων κοινωνικών εντόμων στην συμπεριφορά ατόμων της ίδιας ομάδας. Χαρακτηριστικά παραδείγματα από τη φύση βρίσκουμε σε κάποια είδη μυρμηγκιών με τον τρόπο αναζήτησης τροφής και τη δημιουργία διαδρομών μεταξύ φωλιάς και πηγών τροφής ή την κατασκευαστική διαδικασία των τερμιτών. Αν και η έννοια αυτή είναι άρρητα συνδεδεμένη με την έκκριση μιας η περισσοτέρων χημικών ουσιών (στίγματα) για την έμμεση επικοινωνία μεταξύ ατόμων μιας κοινωνίας εντόμων, έχει εφαρμοστεί σε διαφορετικούς τομείς της επιστήμης των υπολογιστών. Αν και ένα μέρος του κλάδου της τεχνητής ζωής ασχολείται με μια ένα- προς -ένα αντιστοίχηση του έμμεσου αυτού τρόπου επικοινωνίας σε ομάδες ρομπότ που λειτουργούν στον πραγματικό κόσμο με την έκκριση κάποιας αντίστοιχης ουσίας, μεγάλο επιστημονικό ενδιαφέρον υπάρχει για την αναγωγή της έννοιας και της λειτουργίας της στιγματικής συμπεριφοράς στον ψηφιακό κόσμο: Η έννοια των ψηφιακών φερορμονών ή των στιγματικών πακέτων συναντάται, κυρίως σε αντίστοιχους αλγορίθμους δρομολόγησης πακέτων σε ένα δίκτυο κόμβων. Όσο αφορά στην ένα -προς- ένα αντιστοίχηση μηχανισμών σε συστήματα διαμεσολαβητών που λειτουργούν αναλογικά (αισθητήρες) στον πραγματικό κόσμο υπάρχουν περιπτώσεις όπου ενώ ένας στιγματικός μηχανισμός που εμφανίζεται στη φύση φαινομενικά βοηθά στην αντιμετώπιση του προβλήματος που το σύστημα καλείται να αντιμετωπίσει. Η υλοποίηση όμως ενός τέτοιου μηχανισμού μπορεί να είναι ανέφικτη λόγω τεχνικών ή / και οικονομικών λόγων. Ένας προτεινόμενος τρόπος αντιμετώπισης του προβλήματος αυτού είναι η αντιστοίχηση (προσέγγιση) ενός τέτοιου μηχανισμού με ένα μηχανισμό επικοινωνίας ο οποίος θα προσφέρει αντίστοιχα αποτελέσματα χωρίς επιπλέον ή με το μικρότερο κόστος. Για το σκοπό αυτό μπορεί να γίνει χρήση της τεχνολογίας μεταφοράς αναλογικών (π.χ ήχος, φως) ή ψηφιακών σημάτων (π.χ πακέτα δεδομένων). Πόσο στιγματικός όμως είναι μια ένας τρόπος επικοινωνίας μέσω μηνυμάτων, και ποιες είναι οι παραδοχές για κάτι τέτοιο; Μπορεί να υπάρξει αντιστοίχηση της επικοινωνίας (μέσω φερορμονών) που εμφανίζεται στη φύση με κάποιο τρόπο (ασύρματης) επικοινωνίας ο οποίος να μπορεί να εφαρμοστεί σε διαμεσολαβητές περιορισμένων δυνατοτήτων? Η αντιστοίχηση μπορεί να αξιολογηθεί με ποιοτικά κριτήρια όπως η εμφάνιση ή όχι μιας επιθυμητής συμπεριφοράς του συστήματος. Στη διπλωματική αυτή διατριβή παρουσιάζεται ένα μοντέλο ψηφιακής επικοινωνίας το οποίο θεωρητικά αντιστοιχεί στην ποιοτική στιγματική συμπεριφορά ενός συστήματος διαμεσολαβητών. Επιπλέον, εισάγονται δυο μετρήσιμα κριτήρια αξιολόγησης βάση των οποίων μπορεί να γίνει σύγκριση διαφορετικών τρόπων επικοινωνίας. Τα κριτήρια αυτά προκύπτουν από τη μοντελοποίηση μιας κοινής πλατφόρμας – συστήματος διαμεσολαβητών για ένα συγκεκριμένο σενάριο λειτουργίας. Αφού πραγματοποιήθηκαν τρεις σειρές εξομοιώσεων, προέκυψε ότι το σύστημα με χρήση ψηφιακής επικοινωνίας μπορεί να επιτύχει αυτοοργανωτική συμπεριφορά αντίστοιχη με τη συμπεριφορά όταν χρησιμοποιείται στιγματική επικοινωνία ή καμία επικοινωνία. Παρόλα αυτά, η απόδοση του συστήματος βάση των κριτηρίων αξιολόγησης δεν ήταν καλύτερη για τις περισσότερες εξομοιώσεις, κάτι που δεν ήταν θεωρητικά αναμενόμενο. Τέλος, τα αποτελέσματα των εξομοιώσεων οδηγούν σε διάφορα συμπεράσματα τα οποία πρέπει να λαμβάνονται υπόψη στη μελέτη τέτοιων συστημάτων. / Multiagent systems are inspired from insect societies, which collectively achieve different targets, each of is impossible to complete by an individual. In most cases, the driving force to insect “cooperation” and collectiveness is stigmergic behavior. This concept is used in biology to describe the way that environmental changes that occur as a result of a behavior of some social insects, affects the behavior of individuals of the same species. A prominent nature –based example is found in some ant species, where individuals use pheromones during food acquisition, creating different routes between the nest and several food sources. Another example is the termite nest building process. Stigmergy is associated to the emission of one or several chemical substances (stigmata), so as to achieve forms of indirect communication between individuals of a society, although, this concept has been used in different sections of the Computer Science. While much effort is done from the Artificial Life discipline to achieve a one-to-one equivalence of this indirect form of communication into groups of robots that operate in the real world by emitting a pheromone-alike substance, there is much interest in converting this stigmergic behavior to the digital world. The concept of digital pheromones (or stigmergic packets) is met in relative network routing algorithms. Staying on the one-to-one equivalence, there are cases and situations where a proposed stigmergic mechanism can be applied in a multiagent system, where each individual is operating using sensors and actuators inside the real world. However, the implementation of such a mechanism may be unfeasible because of technical, technological or economic reasons. A suggestion to counterattack this limitation is to approximate the stigmergic solution using a different communication mechanism, which may combine feasible, low cost implementation with corresponding to the stigmergic mechanism results. To achieve that, analog (eg light, sound) or digital (data packets) signals may be used. The question is, by using these new concepts, the mechanism of communication is classified as stigmergic and, if yes, are there any admissions? Is there any digital communication analog to the stigmergic communication which may be used to robotic and limited agents? This hypothetical equivalent can be evaluated using qualitative measures like the relevance (or absence) of a desired system behavior In this master thesis, I use a digital communication model which is theoretically assumed to be relative to the quantitative stigmergic behavior of a multiagent system. Additionally, two measurable evaluation criteria are introduced. By using these criteria, different mechanisms of communication may be compared to each other. These criteria result from the modeling of the common platform-multiagent system for a particular scenario of operation. By performing three different series of simulation, there was demonstrated that by using digital communication, the system achieves self organizing behavior relative to the behavior of the same system, using stigmergic or no form of communication. Still, the system performance (according to the introduced evaluation criteria) was not better for most cases of simulations, as it may be expected. Finally, the results lead to several conclusions that must be taken under consideration and further attention for the study of swarm intelligence based systems.

This thesis presents an application-oriented theoretical framework for generalised and specific collaborative contexts with a special focus on Internet-based mass collaboration. The proposed framework is informed by the author’s many years of collaborative arts practice and the design, building and moderation of a number of online collaborative environments across a wide range of contexts and applications. The thesis provides transdisciplinary architecture for describing the underlying mechanisms that have enabled the emergence of mass collaboration and other activities associated with ‘Web 2.0’ by incorporating a collaboratively developed definition and general framework for collaboration and collective activity, as well as theories of swarm intelligence, stigmergy, and distributed cognition. (For complete abstract open document)

Construction is a complex activity that requires the cooperation of multiple workers. Every year, construction activities cause injuries and casualties. To make construction safer, new solutions could be provided by robotics. Robots could be employed not only to replace human workers, but also to make construction in harsh environments safe and cost-effective, paving the way for enhanced underwater infrastructure, deeper underground mining, and planetary colonization. In this thesis, we focus on the topic of collective construction, which involves the cooperation of multiple robots, by presenting a collective robot construction method of our own. Collective construction can be a more viable option than employing individual, complex robots, by potentially allowing the effective realization of large structures, while offering resilience through redundancy, analogous to insect colonies. Our approach offers a novel solution in the design trade-off between choosing the number of robots involved vs. the complexity of the robots involved. On the one hand, capable and complex robots are expensive, limiting the cost effectiveness of realizing large swarms which provide redundancy and increase the system’s resilience to faults. On the other hand, simple and inexpensive robots can be manufactured in large numbers and offer high redundancy, at the cost of limited individual capa bilities and lower performance. We use two types of robots: intelligent scaffolding and worker robots. The intelligent scaffolding acts as regular scaffolding, allowing the worker robots to navigate the structure they assemble, while also guiding and monitoring the construction of the structure. The worker robots move and connect scaffolding and building material while only knowing the local commands necessary to complete their task. This approach is loosely inspired by termite mounds, in which termites use the process of stigmergy in which they mark construction pellets with pheromones to affect the progress of construction, while navigating the struc ture that they build. Thanks to intelligent scaffolding, construction robots have a simple design that allows minimalist onboard computation and communication equipment. In this thesis, we produced a minimum viable prototype demonstrating this concept. Intelligent scaffolding is realized through smart blocks that can be laid and connected to each other. The smart blocks are capable of simple computation and communication once laid. The construction robot uses local navigation methods by line-following across the scaffolding and building blocks of the system. The blocks and construction robot both have a modular design, simplifying the process of manufacturing and repairs while maintaining a low cost. The robot and blocks use magnets to increase the margin of error during block manipulation and allow for the assembly and removal of scaffolding as well as its reuse between build sites. To communicate with the robot, the intelligent scaffolding blocks send local IR signals, similar to TV remote signals, when the robot is on top of them, minimizing the risk of global interference and keeping the system portable. To monitor the connectivity of the system throughout the life cycle of the structure, electrical connections run through each of the blocks, which indicate the status of the structure and can be used to diagnose the location of breaks in the structure for maintenance.

Many models have been developed to explain the process of self organization-the emergence of seemingly purposeful behaviors from groups of entities with limited individual intelligence. However, the underlying behavior that facilitates the emergence of this global pattern is not generally well understood. Our study focuses on different low complexity building algorithms and characterizes how nests are built using these algorithms. Three rules postulated to be functions of wasps' building behavior were developed. First is the random rule, in which there is no constraint per the choice of site to be initiated. The second is the 2-cell rule where only sites with at least two ready walls are initiated. Third, the maxWall rule ensures only sites with the maximum number of ready walls are initiated. This work provides better insight and visualization through simulation into wasps building behavior. This acquired knowledge can be applied to robotics and distributed optimization processes.

The self-organized nest construction behaviors of European paper wasps (Polistes dominula) show potential for adoption in artificial intelligence and robotic systems where centralized control proves challenging. However, P. dominula nest construction mechanisms are not fully understood. This research investigated how nest structures stimulate P. dominula worker action at different stages of nest construction. A novel stochastic site selection model, weighted by simple rules for cell age, height, and wall count, was implemented in a three-dimensional, step-by-step nest construction simulation. The simulation was built on top of a hexagonal coordinate system to improve precision and performance. Real and idealized nest data were used to evaluate simulated nests via two parameters: outer wall counts and compactness numbers. Structures generated with age-based rules were not significantly different from real nest structures along both parameters.