Making Meaning in Computers

This chapter describes synthetic ethology, a scientific methodology in which we construct synthetic worlds in which synthetic agents evolve and become coupled to their environment. First we review the motivations for synthetic ethology as an experimental methodology and explain how it can be used to investigate intentionality and meaning, and the mechanisms from which they emerge, with a special emphasis on communication and language. Second, we present several examples of such experiments, in which genuine (i.e., not simulated) meaningful communication evolved in a population of simple agents. Finally, we discuss the extension of the synthetic ethology paradigm to the problems of structured communications and mental states, complex environments and embodied intelligence, and suggest one way in which this extension could be accomplished. Indeed, synthetic ethology offers a new tool in a comprehensive research program investigating the neuro-evolutionary basis of cognitive processes.

Stratified Constraint Satissfaction Networks in Synergetic Multi-Agent Simulations of Language Evolution

We describe a simulation model of language evolution which integrates synergetic linguistics with multi-agent modelling. On the one hand, this enables the utilization of knowledge about the distribution of the parameter values of system variables as a touch stone of simulation validity. On the other hand, it accounts for synergetic interdependencies of microscopic system variables and macroscopic order parameters. This approach goes beyond the classical setting of synergetic linguistics by grounding processes of self-regulation and self-organization in mechanisms of (dialogically aligned) language learning. Consequently, the simulation model includes four level, (i) the level of single information processing agents which are (ii) dialogically aligned in communication processes enslaved (iii) by the social system in which the agents participate and whose countless communication events shape (iv) the corresponding language system. In summary, the present paper is basically conceptual. It outlines a simulation model which bridges between different levels of language modelling kept apart in contemporary simulation models. This model relates to artificial cognition systems in the sense that it may be implemented to endow an artificial agent community in order to perform distributed processes of meaning constitution.

Modeling Field Theory of Higher Cognitive Functions

The chapter discusses a mathematical theory of higher cognitive functions, including concepts, emotions, instincts, understanding, imagination and intuition. Mechanisms of the knowledge instinct are proposed, driving our understanding of the world. Aesthetic emotions and perception of beauty are related to “everyday” functioning of the mind. We briefly discuss neurobiological grounds as well as difficulties encountered by previous attempts at mathematical modeling of the mind encountered since the 1950s. The mathematical descriptions below are complemented with detailed conceptual discussions so the content of the chapter can be understood without necessarily following mathematical details. We relate mathematical results and computational examples to cognitive and philosophical discussions of the mind. Relating a mathematical theory to psychology, neurobiology and philosophy will improve our understanding of how the mind works.

An Embodied Logical Model for Cognition in Artificial Cognition Systems

In this paper we describe a cognitive model based on the Systemic approach and on the Autopoiesis theory. The syntactical definition of the model consists of logical propositions but the semantic definition includes, besides the usual truth value assignments, what we call emotional flavors, which correspond to the state of the agent’s body translated into cognitive terms. The combination between logical propositions and emotional flavors allows the agent to learn and memorize relevant propositions that can be used for reasoning. These propositions are represented in a specific format – prime implicants/implicates – which is enriched with annotations that explicitly store the internal relations among their literals. Based on this representation, a memory mechanism is described and algorithms are presented that learn a proposition from the agent’s experiences in the environment and that are able to determine the degree of robustness of the propositions, given a partial assignment representing the environment state.

First Steps in Experimental Phenomenology

The main thesis defended by this paper is the thesis of the autonomy – i.e., non-reducibility -- of the phenomenic level of analysis of the psyche. The thesis will be defended by exploiting four main ideas: (1) the theory of levels of reality, (2) the distinction between act and object of presentation, (3) the structure of internal time, and (4) the distinction between egological and non egological acts. I shall present these theses from the point of view of the experiments conducted by Meinong and his pupils, notably Benussi, first at Graz and then at Padua. I may therefore claim that I am here adopting the point of view of what has been called experimental phenomenology, meaning the experimental study of phenomenic or first-person experiences.

Environmental Variability and the Emergence of Meaning

A crucial question for artificial cognition systems is what meaning is and how it arises. In pursuit of that question, this paper extends earlier work in which we show the emergence of simple signaling in biologically inspired models using arrays of locally interactive agents. Communities of “communicators” develop in an environment of wandering food sources and predators using any of a variety of mechanisms: imitation of successful neighbors, localized genetic algorithms and partial neural net training on successful neighbors. Here we focus on environmental variability, comparing results for environments with (a) constant resources, (b) random resources, and (c) cycles of “boom and bust.” In both simple and complex models across all three mechanisms of strategy change, the emergence of communication is strongly favored by cycles of “boom and bust.” These results are particularly intriguing given the importance of environmental variability in fields as diverse as psychology, ecology and cultural anthropology.

Language Evolution and Robotics

This chapter focuses on recent studies on the origins and evolution of language which have used multiple robot systems as their primary platform. After presenting some theoretical background regarding language evolution and the symbol grounding problem, the chapter discusses a number of themes within the evolutionary linguistics that have been subject of robotic studies this far. These themes include categorisation, the formation of vocabularies, the evolution of grammar and the emergence of meaningful communication. Following this review, future avenues for research are discussed. The objective of the chapter is to present why robotics is a fruitful approach to study language origins and evolution, identify the main topics, report the major achievements and problems and provide a roadmap to future studies. The chapter concludes that robotics is, indeed, a very promising methodology to study language evolution and that, although many insights have been gained, we are still closer to the starting point than to the endpoint.

The Meaningful Body

The question as to how sign processes can be meaningful to artificial agents has been a fundamental one for cognitive science throughout its history, from Turing’s (1950) argument from consciousness, to Searle’s (1980) Chinese room and Harnad’s (1990) symbol grounding problem. Currently, the question is even more pressing in the light of recent developments in AI robotics, specifically in the area of reactive and evolutionary approaches. One would perhaps expect that given the embodied and embedded nature of these systems, meaningful sign processes would emerge from the interactions between these robots and their environment. So far, however, robots seem to lack any sensitivity to the significance of signs. In this chapter we will suggest that the artificiality of the body of current robots precludes the emergence of meaning. In fact, one may question whether the label “embodied” genuinely applies to current robots. It may be more truthful to speak of robots being “physicalized,” given that the types of matter used in creating robots bears more similarity to machines like cars or airplanes than to organisms. Thus, we are driven to an investigation of how body and meaning relate. We suggest that meaning is closely related to the strengths and weaknesses of organic bodies of cognitive systems in relation to their struggle for survival. Specifically, as long as four essential characteristics of organic bodies (autopoiesis, metabolism, centrifugal development and self-organization) are lacking in artificial systems, there will be little possibility of the emergence of meaningful sign processes.

Reconstructing Human Intelligence within Computational Sciences

This chapter outlines a possible research program for computational systems representing humanlike intelligence. After a short historical introduction, a possible theoretical framework is described showing how it is possible to integrate heterogeneous disciplines like neurobiology, psychology and phenomenology within one and the same computational framework. Concrete examples are given by reconstructing behavioural (Morris) and phenomenal semiotics (Peirce) with the aid of formal theories. The author hopes to improve the interdisciplinary discussion about adaptive computational models of humanlike intelligence through a unified theoretical framework.

The Goose, The Fly, and the Submarine Navigator

Interdisciplinary research provides in¬spirations and insights into how a variety of disciplines can contribute to the formulation of an alternative path to artificial cognition systems. It has been suggested that results from ethology, evolutionary theory and epistemology can be condensed into four boundary conditions. They lead to the outline of an architecture for genuine cognitive systems, which seeks to overcome traditional problems known from artificial intelligence research. Two major points are stressed: (a) The maintenance of explanatory power by favoring an advanced rule-based system rather than neuronal systems, and (b) the organizational closure of the cognitive apparatus, which has far-reaching implications for the creation of meaningful agents.