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The attempt to evolve complete embodied and situated artificial creatures in which both morphological and control characteristics are adapted during the evolutionary process has been and still represents a long term goal key for the artificial life and the evolutionary robotics community. Loosely inspired by ancient biological organisms which are not provided with a central nervous system and by simple organisms such as stick insects, this thesis proposes a new genotype encoding which allows development and evolution of mor- phology and neural controller in artificial agents provided with a distributed neural network. In order to understand if this kind of network is appropriate for the evolution of non trivial behaviours in artificial agents, two experiments (description and results will be shown in chapter 3) in which evolution was applied only to the controller’s parameters were performed. The results obtained in the first experiment demonstrated how distributed neural networks can achieve a good level of organization by synchronizing the output of oscillatory elements exploiting acceleration/deceleration mechanisms based on local interactions. In the second experiment few variants on the topology of neural architecture were introduced. Results showed how this new control system was able to coordinate the legs of a simulated hexapod robot on two different gaits on the basis of the external circumstances. After this preliminary and successful investigation, a new genotype encoding able to develop and evolve artificial agents with no fixed morphology and with a distributed neural controller was proposed. A second set of experiments was thus performed and the results obtained confirmed both the effectiveness of genotype encoding and the ability of distributed neural network to perform the given task. The results have also shown the strength of genotype both in generating a wide range of different morphological structures and in favouring a direct co-adaptation between neural controller and morphology during the evolutionary process. Furthermore the simplicity of the proposed model has showed the effective role of specific elements in evolutionary experiments. In particular it has demonstrated the importance of the environment and its complexity in evolving non-trivial behaviours and also how adding an independent component to the fitness function could help the evolutionary process exploring a larger space solutions avoiding a premature convergence towards suboptimal solutions.

In the last 50 years computers have made dramatic progress in their capabilities, but at the same time their failings have demonstrated that we, as designers, do not yet understand the nature of intelligence. Chess playing, for example, was long offered up as an example of the unassailability of the human mind to Artificial Intelligence, but now a chess engine on a smartphone can beat a grandmaster. Yet, at the same time, computers struggle to beat amateur players in simpler games, such as Stratego, where sheer processing power cannot substitute for a lack of deeper understanding. The task of developing that deeper understanding is overwhelming, and has previously been underestimated. There are many threads and all must be investigated. This dissertation explores one of those threads, namely asking the question "How might an artificial agent decide on a sensible course of action, without being told what to do?". To this end, this research builds upon empowerment, a universal utility which provides an entirely general method for allowing an agent to measure the preferability of one state over another. Empowerment requires no explicit goals, and instead favours states that maximise an agent's control over its environment. Several extensions to the empowerment framework are proposed, which drastically increase the array of scenarios to which it can be applied, and allow it to evaluate actions in addition to states. These extensions are motivated by concepts such as bounded rationality, sub-goals, and anticipated future utility. In addition, the novel concept of strategic affinity is proposed as a general method for measuring the strategic similarity between two (or more) potential sequences of actions. It does this in a general fashion, by examining how similar the distribution of future possible states would be in the case of enacting either sequence. This allows an agent to group action sequences, even in an unknown task space, into 'strategies'. Strategic affinity is combined with the empowerment extensions to form soft-horizon empowerment, which is capable of composing action policies in a variety of unknown scenarios. A Pac-Man-inspired prey game and the Gambler's Problem are used to demonstrate this selfmotivated action selection, and a Sokoban inspired box-pushing scenario is used to highlight the capability to pick strategically diverse actions. The culmination of this is that soft-horizon empowerment demonstrates a variety of 'intuitive' behaviours, which are not dissimilar to what we might expect a human to try. This line of thinking demonstrates compelling results, and it is suggested there are a couple of avenues for immediate further research. One of the most promising of these would be applying the self-motivated methodology and strategic affinity method to a wider range of scenarios, with a view to developing improved heuristic approximations that generate similar results. A goal of replicating similar results, whilst reducing the computational overhead, could help drive an improved understanding of how we may get closer to replicating a human-like approach.

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The multi-agent robotics paradigm has attracted much attention due to the variety of pertinent applications that are well-served by the use of a multiplicity of agents (including space robotics, search and rescue, and mobile sensor networks). The use of this paradigm for most applications, however, demands economical, lightweight agent designs for reasons of longer operational life, lower economic cost, faster and easily-verified designs, etc. An important contributing factor to an agent’s cost is its control architecture. Due to the emergence of novel implementation technologies carrying the promise of economical implementation, we consider the development of a technology-independent specification for computational machinery. To that end, the use of cybernetics toolsets (control and dynamical systems theory) is appropriate, enabling a principled specifi- cation of robotic control architectures in mathematical terms that could be mapped directly to diverse implementation substrates. This dissertation, hence, addresses the problem of developing a technologyindependent specification for lightweight control architectures to enable robotic agents to serve in a multi-agent scheme. We present the principled design of static and dynamical regulators that elicit useful behaviors, and integrate these within an overall architecture for both single and multi-agent control. Since the use of control theory can be limited in unstructured environments, a major focus of the work is on the engineering of emergent behavior. The proposed scheme is highly decentralized, requiring only local sensing and no inter-agent communication. Beyond several simulation-based studies, we provide experimental results for a two-agent system, based on a custom implementation employing field-programmable gate arrays.

I argue against the commonly held intuition that robots and virtual agents will never have emotions by contending robots can have emotions in a sense that is functionally similar to humans, even if the robots' emotions are not exactly equivalent to those of humans. To establish a foundation for assessing the robots' emotional capacities, I first define what emotions are by characterizing the components of emotion consistent across emotion theories. Second, I dissect the affective-cognitive architecture of MIT's Kismet and Leonardo, two robots explicitly designed to express emotions and to interact with humans, in order to explore whether they have emotions. I argue that, although Kismet and Leonardo lack the subjective feelings component of emotion, they are capable of having emotions.

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.