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.

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This chapter provides a critical survey of emergence definitions both from a conceptual and formal standpoint. The notions of downward / backward causation and weak / strong emergence are specially discussed, for application to complex social system with cognitive agents. Particular attention is devoted to the formal definitions introduced by (Müller 2004) and (Bonabeau & Dessalles, 1997), which are operative in multi-agent frameworks and make sense from both cognitive and social point of view. A diagrammatic 4-Quadrant approach, allow us to understanding of complex phenomena along both interior/exterior and individual/collective dimension.

We discuss the concept of Heterarchy, understood in the social sciences as a model of complex, adaptive human organizations capable of evolving in rapidly changing environments. We observe that heterarchies tend to lead to control hierarchies, because as complex adaptive systems that make use of tags, emergent levels of behavior and control are expected to occur. Given that tags play a fundamental role in heterarchies, we propose that they can make good use of adaptive webs as a communication fabric to manage and co-evolve the knowledge traded with their communities of members and users. A recommendation system named TalkMine is then presented to advance adaptive web technology for heterarchies. TalkMine leads different information resources to learn new and adapt existing keywords to the categories recognized by its communities of users. It uses distributed artificial intelligence algorithms and is currently being implemented for the research library of the Los Alamos National Laboratory

The field of artificial life is questioned on the grounds of its scientific status, its object of study, and its methodological standards; the concepts "artificial life" and "science" are discussed. It is argued that, as an area of inquiry, artificial life is an extension of biology but, as a simulation tool, it is more general. A distinction between analytic and synthetic statements is introduced in order to show that artificial life is generally about clarifying the logical implications of a set of assumptions, but that it can also generate empirical hypotheses. Methodological implications are discussed. 1 Introduction Three sets of questions motivate this paper: 1. Is artificial life (AL) science? Is it going to teach us anything about the real world, or are AL experiments just complicated computer games? Should we expect universities of the future to include departments of AL, or is it just a passing fad? 2. If AL really is science, then what does it study? Is it concerned with t...

CONTENTS Introduction......................................................................................................1 AS-VAT: Agentsheets with VisualAgenTalk..................................................5 Complex Adaptive Systems, Emergence, and Genetic Algorithms.................5 Complex Adaptive Systems (cas)............................................................5 Emergence................................................................................................6 Genetic Algorithms..................................................................................8 AGES (Agentsheets Genetic Evolutionary Simulations).................................9 Genetic Actions........................................................................................9 Genetic Conditions..................................................................................14 AGES Simulations ..........................................................................................17 G

Early agreement within cognitive science on the topic of representation has now given way to a combination of positions. Some question the significance of representation in cognition. Others continue to argue in favor, but the case has not been demonstrated in any formal way. The present paper sets out a framework in which the value of representation-use can be mathematically measured, albeit in a broadly sensory context rather than a specifically cognitive one. Key to the approach is the use of Bayesian networks for modeling the distal dimension of sensory processes. More relevant to cognitive science is the theoretical result obtained, which is that a certain type of representational architecture is necessary for achievement of sensory efficiency. While exhibiting few of the characteristics of traditional, symbolic encoding, this architecture corresponds quite closely to the forms of embedded representation now being explored in some embedded/embodied approaches. It becomes meaningful to view that type of representation-use as a form of information recovery. A formal basis then exists for viewing representation not so much as the substrate of reasoning and thought, but rather as a general medium for efficient, interpretive processing.

The cognitive significance of representation continues to be the subject of some debate. Some see representation as an integral part of cognition. Others see it as unnecessary or counter-productive, although there is often agreement that representation must be required at the level of reasoning, since by definition this operates on representational tokens. But, using information theory within the framework of Bayesian networks, the paper shows that representation is required not just at the level of reasoning, but also at the level of sensing. The achievement of informational efficiency in a sensory process requires use of representational constructs. Though these are nothing like the disembodied world models of representationalist tradition, they do have all the properties needed for representation at the cognitive level. The indication is then that sensing and reasoning have similar representational requirements. The implications this has for the representation debate are considered.

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