Artificial Societies of Intelligent Agents

In this thesis we present our work, where we developed artificial societies of intelligent agents, in order to understand and simulate adaptive behaviour and social processes. We obtain this in three parallel ways: First, we present a behaviours production system capable of reproducing a high number of properties of adaptive behaviour and of exhibiting emergent lower cognition. Second, we introduce a simple model for social action, obtaining emergent complex social processes from simple interactions of imitation and induction of behaviours in agents. And third, we present our approximation to a behaviours virtual laboratory, integrating our behaviours production system and our social action model in animats. In our behaviours virtual laboratory, the user can perform a wide variety of experiments, allowing him or her to test the properties of our behaviours production system and our social action model, and also to understand adaptive and social behaviour. It can be accessed and downloaded through the Internet. Before presenting our proposals, we make an introduction to artificial intelligence and behaviour-based systems, and also we give notions of complex systems and artificial societies. In the last chapter of the thesis, we present experiments carried out in our behaviours virtual laboratory showing the main properties of our behaviours production system, of our social action model, and of our behaviours virtual laboratory itself. Finally, we discuss about the understanding of adaptive behaviour as a path for understanding cognition and its evolution

The World as Evolving Information

This paper discusses the benefits of describing the world as information, especially in the study of the evolution of life and cognition. Traditional studies encounter problems because it is difficult to describe life and cognition in terms of matter and energy, since their laws are valid only at the physical scale. However, if matter and energy, as well as life and cognition, are described in terms of information, evolution can be described consistently as information becoming more complex. The paper presents five tentative laws of information, valid at multiple scales, which are generalizations of Darwinian, cybernetic, thermodynamic, and complexity principles. These are further used to discuss the notions of life and cognition and their evolution

Evolutionary Robotics: a new scientific tool for studying cognition

We survey developments in Artificial Neural Networks, in Behaviour-based Robotics and Evolutionary Algorithms that set the stage for Evolutionary Robotics in the 1990s. We examine the motivations for using ER as a scientific tool for studying minimal models of cognition, with the advantage of being capable of generating integrated sensorimotor systems with minimal (or controllable) prejudices. These systems must act as a whole in close coupling with their environments which is an essential aspect of real cognition that is often either bypassed or modelled poorly in other disciplines. We demonstrate with three example studies: homeostasis under visual inversion; the origins of learning; and the ontogenetic acquisition of entrainment

Chemical communication between synthetic and natural cells: a possible experimental design

The bottom-up construction of synthetic cells is one of the most intriguing and interesting research arenas in synthetic biology. Synthetic cells are built by encapsulating biomolecules inside lipid vesicles (liposomes), allowing the synthesis of one or more functional proteins. Thanks to the in situ synthesized proteins, synthetic cells become able to perform several biomolecular functions, which can be exploited for a large variety of applications. This paves the way to several advanced uses of synthetic cells in basic science and biotechnology, thanks to their versatility, modularity, biocompatibility, and programmability. In the previous WIVACE (2012) we presented the state-of-the-art of semi-synthetic minimal cell (SSMC) technology and introduced, for the first time, the idea of chemical communication between synthetic cells and natural cells. The development of a proper synthetic communication protocol should be seen as a tool for the nascent field of bio/chemical-based Information and Communication Technologies (bio-chem-ICTs) and ultimately aimed at building soft-wet-micro-robots. In this contribution (WIVACE, 2013) we present a blueprint for realizing this project, and show some preliminary experimental results. We firstly discuss how our research goal (based on the natural capabilities of biological systems to manipulate chemical signals) finds a proper place in the current scientific and technological contexts. Then, we shortly comment on the experimental approaches from the viewpoints of (i) synthetic cell construction, and (ii) bioengineering of microorganisms, providing up-to-date results from our laboratory. Finally, we shortly discuss how autopoiesis can be used as a theoretical framework for defining synthetic minimal life, minimal cognition, and as bridge between synthetic biology and artificial intelligence.Comment: In Proceedings Wivace 2013, arXiv:1309.712

Autonomy: a review and a reappraisal

In the field of artificial life there is no agreement on what defines ‘autonomy’. This makes it difficult to measure progress made towards understanding as well as engineering autonomous systems. Here, we review the diversity of approaches and categorize them by introducing a conceptual distinction between behavioral and constitutive autonomy. Differences in the autonomy of artificial and biological agents tend to be marginalized for the former and treated as absolute for the latter. We argue that with this distinction the apparent opposition can be resolved

Between Sense and Sensibility: Declarative narrativisation of mental models as a basis and benchmark for visuo-spatial cognition and computation focussed collaborative cognitive systems

What lies between `\emph{sensing}' and `\emph{sensibility}'? In other words, what kind of cognitive processes mediate sensing capability, and the formation of sensible impressions ---e.g., abstractions, analogies, hypotheses and theory formation, beliefs and their revision, argument formation--- in domain-specific problem solving, or in regular activities of everyday living, working and simply going around in the environment? How can knowledge and reasoning about such capabilities, as exhibited by humans in particular problem contexts, be used as a model and benchmark for the development of collaborative cognitive (interaction) systems concerned with human assistance, assurance, and empowerment? We pose these questions in the context of a range of assistive technologies concerned with \emph{visuo-spatial perception and cognition} tasks encompassing aspects such as commonsense, creativity, and the application of specialist domain knowledge and problem-solving thought processes. Assistive technologies being considered include: (a) human activity interpretation; (b) high-level cognitive rovotics; (c) people-centred creative design in domains such as architecture & digital media creation, and (d) qualitative analyses geographic information systems. Computational narratives not only provide a rich cognitive basis, but they also serve as a benchmark of functional performance in our development of computational cognitive assistance systems. We posit that computational narrativisation pertaining to space, actions, and change provides a useful model of \emph{visual} and \emph{spatio-temporal thinking} within a wide-range of problem-solving tasks and application areas where collaborative cognitive systems could serve an assistive and empowering function.Comment: 5 pages, research statement summarising recent publication

The challenge of complexity for cognitive systems

Complex cognition addresses research on (a) high-level cognitive processes – mainly problem solving, reasoning, and decision making – and their interaction with more basic processes such as perception, learning, motivation and emotion and (b) cognitive processes which take place in a complex, typically dynamic, environment. Our focus is on AI systems and cognitive models dealing with complexity and on psychological findings which can inspire or challenge cognitive systems research. In this overview we first motivate why we have to go beyond models for rather simple cognitive processes and reductionist experiments. Afterwards, we give a characterization of complexity from our perspective. We introduce the triad of cognitive science methods – analytical, empirical, and engineering methods – which in our opinion have all to be utilized to tackle complex cognition. Afterwards we highlight three aspects of complex cognition – complex problem solving, dynamic decision making, and learning of concepts, skills and strategies. We conclude with some reflections about and challenges for future research