We introduce a novel framework called "artificial physics", which provides distributed control of large collections of agents. The agents react to artificial forces that are motivated by natural physical laws. This framework provides an effective mechanism for achieving self-assembly, fault-tolerance, and self-repair. Examples are shown for various regular geometric configurations of agents. A further example demonstrates that self-assembly via distributed control can also perform distributed computation. 1.

This paper discusses epistemological consequences of embodied AI for Artificial Life models. The importance of robotic systems for ALife lies in the fact that they are not purely formal models and thus have to address issues of semantic adaptation and epistemic autonomy, which means the system's own ability to decide upon the validity of measurements. Epistemic autonomy in artificial systems is a difficult problem that poses foundational questions. The proposal is to concentrate on biological transformations of epistemological questions that have lead to the development of modern ethology. Such an approach has proven to be useful in the design of control systems for behavior-based robots. It leads to a better understanding of modern ontological conceptions as well as a reacknowledgement of finality in the description and design of autonomous systems. Key words: epistemic autonomy, embodied AI, epistemology, robotics, theoretical biology, finality, teleology, ontology. 1 Introduction ...

This chapter addresses the nature of open-ended evolutionary processes, and the related, but more subtle, issue of how fundamental novelty (i.e. creativity) can arise in such processes. A number of existing artificial evolutionary systems, such as Tierra (Ray, 1991), are analysed in this context, but it is found that the theoretical grounding upon which they are based does not usually consider all of the relevant issues for creative evolution. The importance of considering the design of the environment, and of interactions between individuals, as well as the design of the individuals themselves, is emphasised. The properties of a hypothetical `proto-DNA' structure---a suitable seed for an open-ended, and creative, evolutionary process---are discussed. A number of open questions relating to these issues are highlighted as useful areas of future research. Finally, a paradigm for an evolutionary process described by Waddington (1969) is described. It is suggested that this might r...

Pattee's semantic closure principle is used to study the characteristics and requirements of evolving material symbols systems. By contrasting agents that reproduce via genetic variation with agents that reproduce via self-inspection, we reach the conclusion that symbols are necessary to attain open-ended evolution, but only if the phenotypes of agents are the result of a material, selforganization process. This way, a study of the inter-dependencies of symbol and matter is presented. This study is based first on a theoretical treatment of symbolic representations, and secondly on simulations of simple agents with matter-symbol inter-dependencies. The agent-based simulations use evolutionary algorithms with indirectly encoded phenotypes. The indirect encoding is based on Fuzzy Development programs, which are procedures for combining fuzzy sets in such a way as to model self-organizing development processes.

Abstract We present a new definition of the concept of representation for cognitive science that is based on a study of the origin of structures that are used to store memory in evolving systems. This study consists of novel computer experiments in the evolution of cellular automata to perform nontrivial tasks as well as evidence from biology concerning genetic memory. Our key observation is that representations require inert structures to encode information used to construct appropriate dynamic configurations for the evolving system. We propose criteria to decide if a given structure is a representation by unpacking the idea of inert structures that can be used as memory for arbitrary dynamic configurations. Using a genetic algorithm, we evolved cellular automata rules that can perform nontrivial tasks related to the density task (or majority classification problem) commonly used in the literature. We present the particle catalogs of the new rules following the computational mechanics framework. We discuss if the evolved cellular automata particles may be seen as representations according to our criteria. We show that while they capture some of the essential characteristics of representations, they lack an essential one. Our goal is to show that artificial life can be used to shed new light on the computation-versus-dynamics debate in cognitive science, and indeed function as a constructive bridge between the two camps. Our definitions of representation and cellular automata experiments are proposed as a complementary approach, with both dynamics and informational modes of explanation.

The study of adapting and evolving autonomous agents should be based on a complex systems-theoretic framework which requires both self-organizing and symbolic dimensions. An inclusive framework based on the notions of semiotics and situated action is advanced to build models capable of representing, as well as evolving in their environments. Such undertaking is pursued by discussing the ways in which symbol and self-organization are irreducibly intertwined in evolutionary systems. This way, we re-think the notion of autonomy of evolving systems, and show that evolutionary systems are characterized by a particular type of syntactic autonomy. Recent developments in emergent computation in cellular automata are discussed as examples of the emergence of syntactic autonomy in computational environments. New results emphasizing this syntactic autonomy in cellular automata are presented.

We present novel experiments in the evolution of Cellular Automata (CA) to solve nontrivial tasks. Using a genetic algorithm, we evolved CA rules that can solve non-trivial logical tasks related to the density task (or majority classification problem) commonly used in the literature. We present the particle catalogs of the new rules following the computational mechanics framework. We know from Crutchfield et al (2002) that particle computation in CA is a process of information processing and integration. Here, we discuss the type of memory that emerges from the evolving CA experiments for storing and manipulating information. In particular, we contrast this type of evolved memory with the type of memory we are familiar with in Computer Science, and also with the type of biological memory instantiated by DNA. A novel CA rule obtained from our own experiments is used to elucidate the type of memory that one-dimensional CA can attain.

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As we enter this amazing new world of artificial and virtual systems and environments in the context of human communities, we are interested in the development of systems and environments which have the capacity to grow and evolve their own meanings in the context of this community of interaction. In this paper we analyze the necessary conditions to achieve systems and environments with these properties: 1) a coupled interaction between a system and its environment; 2) an environment with sufficient initial richness and structure to allow for; 3) embodied emergent classification of that environment-system coupling; 4) which is subject to pragmatic selection.

We connect transistors and proteins in two ways. The first is by showing that they have much in common as fundamental devices of electronics and life. The second is by describing how an evolvable wiring of electronic devices can parallel the wiring of proteins into genetic regulatory networks. We then transform this connection into a methodology for the study of the evolutionary properties of circuits. The approach is based on the use of analog electronic circuit simulators. We present an example of implementation with the first results obtained.