This dissertation aims to provide a critical evaluation of artificial life (A-Life) models of communication in autonomous agents. In particular the focus will be on the issue of self-organisation, which is often argued to be one of the characteristic features distinguishing A-life from other approaches. To ground the arguments, a background of the study of communication within artificial intelligence is provided. This is followed by a comprehensive review of A-Life research on communication between autonomous agents, which is evaluated by breaking down self-organisation into the following sub-questions. Is communication self-organised or hard-coded? What do signals mean to the agents, and how should an external examiner interpret them? Is there any spatial or temporal displacement, or do agents only communicate about their present situation? It is shown that there is very little self-organised communication, as yet, when examined on these grounds, and that most models only look at communication as relatively independent from other behaviours. As a conclusion, it is suggested to use integrated co-evolution of behaviours, including communication, in the spirit of the enactive cognitive science paradigm, and by using incremental evolution combined with learning.

M.Sc. (Computer Science) / This research study investigates multi-agent systems (MASs), artificial life concepts and machine learning, amongst other things, in answering the key research question: “How can a generic multi-agent system integrate with machine learning through artificial life principles?” In answering this question, this dissertation illustrates the design and development of a generic multi-agent, life simulation and learning software framework. This framework simplifies and enables the realisation of MASs in solving complex problems in multiple domains. Finally, this research presents a prototype solution as a proof of concept of the framework’s strengths and weaknesses. The research study illustrates the design of MASs utilising sound design principles, patterns and methodologies. Furthermore, this research explores the requirements for creating and integrating MASs with other technologies, as well as the possible pitfalls in creating such large-scale systems. In addressing the necessity of learning, several machine learning techniques are examined and reinforcement learning is identified as an ideal candidate for the proposed framework. In addition, by understanding the overall machine learning process, the proposed framework integrates machine learning as three separate processes: data extraction, learning and inference. Lastly, the literature study focuses on artificial life, specifically its use in MASs, and defines what constitutes an intelligent system. This research depicts artificial life as a plausible natural integrator between MAS and machine learning technologies. The proposed framework presented in this dissertation consists of five core agent modules that can be extended, depending on the problem domain requirements. The framework in itself is self-containing and independent of any concrete implementation. A multi-agent antivirus system is presented as the prototype implementation of the proposed framework. A quantitative and qualitative analysis was conducted, identifying the results of the prototype and generic framework while highlighting strengths and weaknesses. The contribution of this research is found partly in the proposed generic framework as a means of augmenting mechanisms for MAS design and development by means of artificial life and machine learning integration. In a broader context, this research serves as a foundation towards creating advanced MAS frameworks, leading to numerous interesting and influential agent-oriented applications.

This thesis is about similarities and differences between natural and artificial life. It examines how a combination of insight from the disciplines of computer science and philosophy can be used to address this issue. By applying the principles of evolution to artificial life, the paper shows the perspectives of this life form and its implications for mankind. Human history contains many attempts at constructing artificial creatures; however, this dream only became reality with the advent of digital computers. Although artificial life is built on different principles than natural life, is better to view both as complementary rather than as opposites. It is possible to speculate on symbiosis between artificial and natural elements and on the formation of hybrid life forms that combine features from both worlds. Artificial life is not dependent on biological cycles and its evolution can proceed much faster. It has the potential to overcome the necessity of death, which is characteristic of all biological entities. If we compare the intelligence of machines to that of natural organisms, it is possible to identify the differences between them. Machine intelligence has the potential to create artificial collective intelligence through computer networks that exceed the level of separate entities. Simple forms of artificial life, identifiable at present, will evolve in coming decades and raise a number of unsolved questions (i.e. ethical concerns). These issues are and will remain current.

This paper deals with artificial life simulation by means of artificial BDI agents.This work aims to create a virtual world, to which agents are put. In system, there is 5 kinds of agents. Agent father, who rules and synchronizes the system. Next are agent worker, salesman, cop and thief. Model of the system is created by use of Prometheus methodology. The system is programed in the Jason language, which is implementation of AgentSpeak language.

From a visual standpoint it is often easy to point out whether a system is considered to be self-organizing or not, though a quantitative approach would be more helpful. Information theory, as introduced by Shannon, provides the right tools not only quantify self-organization, but also to investigate it in relation to the information processing performed by individual agents within a collective. This thesis sets out to introduce methods to quantify spatial self-organization in collective systems in the continuous domain as a means to investigate morphogenetic processes. In biology, morphogenesis denotes the development of shapes and form, for example embryos, organs or limbs. Here, I will introduce methods to quantitatively investigate shape formation in stochastic particle systems. In living organisms, self-organization, like the development of an embryo, is a guided process, predetermined by the genetic code, but executed in an autonomous decentralized fashion. Information is processed by the individual agents (e.g. cells) engaged in this process. Hence, information theory can be deployed to study such processes and connect self-organization and information processing. The existing concepts of observer based self-organization and relevant information will be used to devise a framework for the investigation of guided spatial self-organization. Furthermore, local information transfer plays an important role for processes of self-organization. In this context, the concept of synergy has been getting a lot attention lately. Synergy is a formalization of the idea that for some systems the whole is more than the sum of its parts and it is assumed that it plays an important role in self-organization, learning and decision making processes. In this thesis, a novel measure of synergy will be introduced, that addresses some of the theoretical problems that earlier approaches posed.

The central goal of this thesis is to provide additional criteria towards implementing open-ended evolution in an artificial system. Methods inspired by biological evolution are frequently applied to generate autonomous agents too complex to design by hand. Despite substantial progress in the area of evolutionary computation, additional efforts are needed to identify a coherent set of requirements for a system capable of exhibiting open-ended evolutionary dynamics. The thesis provides an extensive discussion of existing models and of the major considerations for designing a computational model of evolution by natural selection. Thus, the work in this thesis constitutes a further step towards determining the requirements for such a system and introduces a concrete implementation of an artificial evolution system to evaluate the developed suggestions. The proposed system improves upon existing models with respect to easy interpretability of agent behaviour, high structural freedom, and a low-level sensor and effector model to allow numerous long-term evolutionary gradients. In a series of experiments, the evolutionary dynamics of the system are examined against the set objectives and, where appropriate, compared with existing systems. Typical agent behaviours are introduced to convey a general overview of the system dynamics. These behaviours are related to properties of the respective agent populations and their evolved morphologies. It is shown that an intuitive classification of observed behaviours coincides with a more formal classification based on morphology. The evolutionary dynamics of the system are evaluated and shown to be unbounded according to the classification provided by Bedau and Packard’s measures of evolutionary activity. Further, it is analysed how observed behavioural complexity relates to the complexity of the agent-side mechanisms subserving these behaviours. It is shown that for the concrete definition of complexity applied, the average complexity continually increases for extended periods of evolutionary time. In combination, these two findings show how the observed behaviours are the result of an ongoing and lasting adaptive evolutionary process as opposed to being artifacts of the seeding process. Finally, the effect of variation in the system on the diversity of evolved behaviour is investigated. It is shown that coupling individual survival and reproductive success can restrict the available evolutionary trajectories in more than the trivial sense of removing another dimension, and conversely, decoupling individual survival from reproductive success can increase the number of evolutionary trajectories. The effect of different reproductive mechanisms is contrasted with that of variation in environmental conditions. The diversity of evolved strategies turns out to be sensitive to the reproductive mechanism while being remarkably robust to the variation of environmental conditions. These findings emphasize the importance of being explicit about the abstractions and assumptions underlying an artificial evolution system, particularly if the system is intended to model aspects of biological evolution.

Technology, nowadays, has given us huge computational potential, but computer sciences have major problems tapping into this pool of resources. One of the main issues is how to program and design distributed systems. Biology has solved this issue about half a billion years ago, during the Cambrian explosion: the evolution of multicellularity. The evolution of multicellularity allowed cells to differentiate and so divide different tasks to different groups of cells; this combined with evolution gives us a very good example of how massively parallel distributed computational system can function and be “programmed”. However, the evolution of multicellularity is not very well understood, and most traditional methodologies used in evolutionary theory are not apt to address and model the whole transition to multicellularity. In this thesis I develop and argue for new computational artificial life methodologies for the study of the evolution of multicellularity that are able to address the whole transition, give new insights, and complement existing methods. I argue that these methodologies should have three main characteristics: accessible across scientific disciplines, have potentiality for complex behaviour, and be easy to analyse. To design models, which possess those characteristics, I developed a model of genetic regulatory networks (GRNs) that control artificial cells, which I have used in multiple evolutionary experiments. The first experiment was designed to present some of the engineering problems of evolving multicelled systems (applied to graph-colouring), and to perfect my artificial cell model. The two subsequent experiments demonstrate the characteristics listed above: one model based on a genetic algorithm with an explicit two-level fitness function to evolve multicelled cooperative patterning, and one with freely evolving artificial cells that have evolved some multicelled cooperation as evidenced by novel measures, and has the potential to evolve multicellularity. These experiments show how artificial life models of evolution can discover and investigate new hypotheses and behaviours that traditional methods cannot.

Rapid prototyping tools exist in many fields of science and engineering, but are rare in biology especially not general tools that can handle the diversity and complexity of the many spatial and temporal scales in nature. In this thesis a general use, cell-based, middle-out biology emulation programming framework (outlining a programming paradigm) is presented, that enables biologists to emulate and use virtual biological systems of previously unimaginable complexity and potentially get results accurate enough to be used in research and ultimately, in clinical practice, such as diagnosis or operations. With this technology, virtual organisms can be created that are viable, fit and can be optimised for any task that arises. The tool, realised with a programming framework created for the C++ language is detailed and demonstrated through several examples of increasing complexity, namely several example organisms and a cancer emulation, showing both viable virtual organisms and usable experimental results.

Genetic algorithm and artificial life techniques are applied to the development of challenging and interesting opponents in a combat-based computer game. Computer simulations are carried out against an idealized human player to gather data on the effectiveness of the computer generated opponents.

L’utilisation de l’intelligence artificielle et des biotechnologies dans l’art conduit à une reformulation des enjeux du théâtre comme « spectacle vivant ». Ces pratiques technoscientifiques déplacent l’objet de la performance et produisent du discours. Nous proposons dans cette étude de nous servir de leurs énoncés pour élargir le cadre de la théâtralité aux arts de la vie artificielle. En effet, les déplacements opérés au moyen de la théâtralité dans le champ artistique s’effectuent à la fois sur les oeuvres et dans le langage. À la lumière de ces opérations, nous faisons apparaître le potentiel transformateur de l’intégration de nouveaux matériaux dans l’esthétique théâtrale mais aussi les obstacles qui s’y trouvent. Prise entre les arts et les technosciences, l’analyse démontre que la théâtralité des oeuvres technologiques repose notamment sur l’artifice du langage. / The use of artificial intelligence and biotechnology in art has led to a radical reformulation of theater as living performance. These technoscientific practices have displaced the subject of performance and produced various new discourses: In this study, I propose to make use of these discourses to expand the frame of theatricality to the realm of artificial life art. The displacements operated by means of theatricality in the artistic field are taking place both on the level of the artworks and the level of discourse. In light of such operations, we see the potential of transformation relying on the use of these materials in theatrical aesthetics, as well as the obstacles found in them. Taking place between the arts and the technosciences, the study proves that the theatricality of technological works relies on the artifice of language.