Ricardo Gudwin

In this work, we propose a computational approach to the triadic model of Peircean semiosis (meaning processes). We investigate theoretical constraints about the feasibility of simulated semiosis. These constraints, which are basic requirements for the simulation of semiosis, refer to the synthesis of irreducible triadic relations (Sign–Object–Interpretant). We examine the internal organization of the triad S–O–I, that is, the relative position of its elements and how they relate to each other. We also suggest a multi-level approach based on self-organization principles. In this context, semiosis is described as an emergent process. Nevertheless, the term ‘emergence’ is often used in a very informal way in the so called ‘emergent’ computation, without clear explanations and/or definitions. In this paper, we discuss in some detail the meaning of the theoretical terms ‘emergence’ and ‘emergent’, showing how such an analysis can lead to improvements of the algorithm proposed.
Keywords Meaning - Semiosis - Emergence - Simulation - C. S. Peirce

Modern semiotics is a branch of logics that formally defines symbol-based communication. In recent years, the semiotic classification of signs has been invoked to support the notion that symbols are uniquely human. Here we show that alarm-calls such as those used by African vervet monkeys (Cercopithecus aethiops), logically satisfy the semiotic definition of symbol. We also show that the acquisition of vocal symbols in vervet monkeys can be successfully simulated by a computer program based on minimal semiotic and neurobiological constraints. The simulations indicate that learning depends on the tutor–predator ratio, andthat apprentice-generated auditory mistakes in vocal symbol interpretation have little effect on the learning rates of apprentices (up to 80% of mistakes are tolerated). In contrast, just 10% of apprentice-generated visual mistakes in predator identification will prevent any vocal symbol to be correctly associated with a predator call in a stable manner. Tutor unreliability was also deleterious to vocal
symbol learning: a mere 5% of “lying” tutors were able to completely disrupt symbol learning, invariably leading to the acquisition of incorrect associations by apprentices. Our investigation corroborates the existence of vocal symbols in a non-human species, and indicates that symbolic competence emerges spontaneously from classical associative learning mechanisms when the conditioned stimuli are self-generated, arbitrary and socially efficacious.We propose that more exclusive properties of human language, such as syntax, may derive from the evolution of higher-order domains for neural association, more removed from both the sensory input
and the motor output, able to support the gradual complexification of grammatical categories into syntax.

In last decades, neuroscience and psychology research findings about emotion have been increasingly attracting the attention of many researchers in Computer Science and Artificial Intelligence (AI) areas. AI, interested in cognitive processes modeling and simulation, clearly see that emotion is a crucial element to model perception, learning, decision processes, memory, behavior and others functions. Currently, two Computer Science areas use emotion concepts on their research: Human-Computer Interaction and systems whose internal architecture is emotion-based.Even considering current state-of-art projects, theoretical aspects of emotion to be employed in computational systems projects are scarcely discussed. Our research intends to discuss these problems and propose tentative directions to solve them.First, (ii) the lack of a well defined scientific framework to approach 'Artificial Emotion', with few advanced attempts been published suggesting one. Besides that, a close look at some projects provides a non-exhausted list of (ii) important questions they might face to achieve trustworthy results. They can be grouped in two types, theoretical-conceptual or computational questions. Examples are: How to integrate emotion with other mechanisms, such as: sensory, learn, selection and communication? Can artificial emotion be an emergent property? What kind of data structure and computational mechanisms should be used to both capture and represent the complexity of emotion processes? What kind of experimental test allows to better explore emotion-based models? Moreover, an essential question to be answered is related to which extent supposed structural complexity involved in emotion phenomenon can be abstracted and modeled, not missing important brain structure interactions and not being too complex to impair computational representation.Last, these facts mainly contribute to a third noticeable problem: (iii) lack of comparative analysis between projects and also within same project, with beneficial comparisons of emotion and non-emotion-based experiments. Positively, overcome these challenges can be an important step to field progress goes beyond engineering applications and towards a more scientific discipline.

This is a project on Articial Life where we simulate an ecosystem that allows cooperative interaction between agents, including intra-specic predatorwarning communication in a virtual environment of predatory events. We propose, based on Peircean semiotics and informed by neuroethological constraints, an experiment to simulate the emergence of symbolic communication among articial creatures. Here we describe the simulation environment and the creatures'
control architectures, and briey present obtained results.

"In this book, our goal is to present the most representative research projects in computational semiotics at the present time. Considering the relevance of the semiotic approach for future developments in artificial intelligence, we suggest—and certainly hope—that the collection will be a major contribution to the field. Within the book, we have contributions from philosophers, cognitive scientists, computer scientists, and engineers, all focused on the singular agenda of inquiring how semiotics works with intelligent system techniques in order to create newer and more robust types of intelligent systems. One of the main criticisms of which intelligent systems developers are accused is being naïve in their approaches to the question, “What is intelligence?” Therefore, it is as important to take into
account the philosophy of the mind and to be aware of the issues of that field within current philosophic speculations as it is to develop a practical methodology of the technologies of semiotic intelligent systems.

The book is divided into four parts. Section I: Theoretical Issues includes chapters with a more philosophical tone. Section II: Discussions on Semiotic Intelligent Systems includes chapters that still have a philosophical flavor but move beyond philosophical speculations toward some kind of implementation of intelligent systems. Section III: Semiotics in the Development of Intelligent Systems includes chapters that use semiotics in some sense for the development of an intelligent system. Finally, the fourth part, Semiotic Systems Implementations, includes chapters whose authors claim to be using semiotic concepts in intelligent systems implementation."

Este livro reúne contribuições de diversas áreas e perspectivas referentes ao estudo da cognição, emoção e ação e das conexões entre elas. Os seus capítulos foram agrupados em três partes, de acordo com a predominância de seu conteúdo: história da filosofia; ciências cognitivas; ciências humanas e sociais. Há, ainda, uma entrevista com o Prof. Lauro F. B. da Silveira, a quem a obra é dedicada.