Reducing Negative Complexity by a Computational Semiotic System

The chapter describes the set-up for an experiment in computational semiotics. Starting with a hypothesis about negative complexity in the environment of human persons today it describes a strategy, how to assist human persons to reduce this complexity by using a semiotic system. The basic ingredients of this strategy are a visual programming interface with an appropriate abstract state machine, which has to be realized by distributed virtual machines. The distributed virtual machines must be scalable, have to allow parallel processing, have to be fault tolerant, and should have the potential to work in real time. The objects, which have to be processed by these virtual machines, are logical models (LModels), which represent dynamic knowledge, including self learning systems. The descriptions are based on a concrete open source project called Planet Earth Simulator.

The Semiotic Structure of Practical Reasoning Habits

This chapter introduces Relational Thinking Styles (RTS), a model and method for identifying practical reasoning habits. Taken together, these unintentional reasoning habits parallel C.S. Peirce’s logic of inquiry (methodeutic). However, unlike the deliberate application of inferences prescribed by Peirce’s logic, these find expression as the unconscious applications of methods for the selection of ends and means (goals and processes). Not everyone applies the same infer-encing patterns, especially for encountering novelty. Most people persistently lay familiar tem-plates over novel issues, habitually engaging inductive-like processes to the solving of new prob-lems. However, some apply abductive-like mental processes in the face of novelty; others, de-ductive-like ones. Because RTS is capable of predicting future consequences and of empirical verification by means of a reliable assessment tool (Chiasson et al 2003) it is amenable to com-puter modeling. Computer modeling of the abductive-like process defined by this model may contribute to eventual development of an abductive inference-engine.

Morphological Semiosis

This chapter argues that reality, both material and conceptual, functions as a complex network of continuous adaptive morphological formation. The morphological form is a well-formed form (wff), a Sign. It materializes as this informational form within a function, an irreducible triad, where f(x)=x models the three procedures of input/mediation/output. The procedures are, in themselves, relations, which are encoded spatial and temporal measurements that enable both symmetrical and asymmetrical informational interactions. Using a Cartesian quadrant, the six possible relations are examined to show how reality is molded into well-formed forms, or signs, to provide capacities for both random and planned information and for both mechanical and reasoned templates of informational behavior. It is hoped that such an applied analysis of information can enable researchers to construct and manage artificial information systems.

Semiotic Brains and Artificial Minds

At the roots of thinking abilities there is a process of disembodiment of mind that presents a new cognitive perspective on the role of external models and representations. Taking advantage of Turing’s comparison between “unorganized” brains and “logical” and “practical” machines” the presentation will illustrate the centrality to cognition of the disembodiment of mind from the point of view of the interplay between internal and external representations, both mimetic and creative. The last part of the presentation will describe the concept of mimetic mind I have introduced to shed new cognitive and philosophical light on the role of computational modeling and on the decline of the so-called Cartesian computationalism.

What Makes a Thinking Machine? Computational Semiotics And Semiotic Computation

Semiotics is considered here as a relational and ontogenetic approach to describing cognition and communication in signifying systems. Implementing a semiotic approach to computing thus would require a computable and scalable signifying space where signs can be arbitrarily created, related, interpreted and deliberated. The author argues that although signs are representations, a signifying space cannot be realized under the current representational paradigm of recording and processing static data in a hierarchical data space. A ‘semiotic machine’ instead must implement a genetic epistemology of cognition based on assimilation and pure relations. The ‘Pile’ system introduced in this essay is supposed to meet these requirements and is described as a ‘semiotic computation’ system structurally enabling deductive as well as inductive and abductive processes of self-reflection, deliberation and interpretation, commonly associated with ‘thinking’.

Systemic Semiotics as a Basis for an Agent-Oriented Conceptual Modeling Methodology

The authors demonstrate how Systemic Semiotics, an approach that combines a semiotic model of language called Systemic Functional Linguistics with selected concepts from Social Semiotics, can be applied to create Agent-Oriented Information Systems in which social processes can be elicited from stakeholders, specified by designers, and embedded into actual agent-based systems. The utility of Systemic Semiotics applied to Agent-Oriented Conceptual modelling is demonstrated by developing a real world system to address the problem of registering and training volunteers in an emergency service organisation. The experience of developing this system was then used to propose an experimental Agent-Oriented Conceptual Modelling Methodology that uses the same theory and concepts for describing the artefacts and the processes of agent-oriented systems development.

Computational AutoGnomics

This chapter introduces an approach to understanding the “origin” and nature of Mind via a new method of integrated inquiry/inference/intuition. The method is concomitantly applicable to the foundations notions (philosophically, formally, and theoretically) which themselves subtend a theory of theory formation. An approach is posited regarding the formalization of an order and its derivative calculus, the latter taken as a formulation of the disorder experientially related to the given order, which also implies a reorder(ing) format that, within a Nonseparable System of order/disorder/reorder Relations, suggests The Form of a meta theory of theory formation. The Form will, in turn, be invoked in formulating a Theory of Intelligence/Mind and its technological implementations as Synthetic Intelligence/Mind, i.e. the AutoGnome, a semiotic machine interpreting C.S. Peirce. The key commercial application of the AutoGnome reported here is that of the IntelliSite (an Intelligent Website generically branded as TrueThinker.com) and its derivative implementations.

The Semiotics of Smart Appliances and Pervasive Computing

This paper presents Digital Habitats, a conceptual and methodological framework for analyzing and designing smart appliances in the context of pervasive computing. The concrete topic is a project in pervasive gaming for children. The framework consists of a set of theoretical concepts supplemented by diagrams for representing semi-formal models. We give a short overview of selected theories of play and gaming and apply the framework to an implemented simple pervasive game. Finally, we use the framework in a constructive man-ner to produce a concrete design of a new game. The result is dis-cussed and compared to other approaches. The main points are: (a) it can describe communicative as well as material acts plus the way they hang together, (b) it provides an explicit link between human activities and their spatial context, (c) it has an explicit dynamic model that pre-cisely describes the conditions for executing actions, and (d) it offers a typology of participant roles, based on linguistic theory, that supports design processes.

Symbols

What is the nature of symbols? This word is used for traffic signs, for mathematical notations, and motivationally loaded cultural objects, which may inspire war and piece. This chapter explains relationships among symbols, cognition, and language. Symbols are explained as processes in the mind involving cognition and language. Relationships between cognition and language were a mystery until recently. Linguists often considered language as relationships among words and other linguistic entities, separately from its relationships to the world. Mechanisms of language in the mind and brain were considered separate and different from thinking and cognition. Neural mechanisms integrating language and cognition are unknown. Yet, language and cognition are intertwined in evolution, ontogenesis, learning, and in everyday usage, therefore a unified understanding of working of the mind is essential. A mathematical description of such unifying mechanisms is the subject of this paper. We discuss relationships among computational intelligence, known mechanisms of the mind, semiotics, computational linguistics, and describe a process integrating language and cognition. Mathematical mechanisms of concepts, emotions, and instincts are described as a part of information processing in the mind and related to perception and cognition processes in which an event is understood as a concept. Development of such mathematical theories in the past often encountered difficulties of fundamental nature manifested as combinatorial complexity. Here, combinatorial complexity is related to logic underlying algorithms and a new type of logic is introduced, dynamic fuzzy logic, which overcomes past limitations. This new type of logic is related to emotional signals in the brain and combines mechanisms of emotions and concepts. The mathematical mechanism of dynamic logic is applicable to both language and cognition, unifying these two abilities and playing an important role in language acquisition as well as cognitive ontogenesis. The mathematical description of thought processes is related to semiotic notions of signs and symbols.

A theory of Semantics Based on Old Arabic

In this chapter, we show how we derived a universal theory of semantics. Then we discuss the discovery’s impact on automated learning and text analysis. Using induction, we derive general principles from some observations on word meanings in Old Arabic passages called muhkam. Muhkam means that the meaning is made perfectly clear. We find that the 32 sounds of Arabic are signs that refer to abstract objects generated from two symmetry pairs and a three-element set. We show that word roots are structured signs referring to structured abstract objects. Arabic roots and their interpretations and reinterpretations form an abstract set of concepts that can be used as cognitive tools over which any language can render reality. We realized this in a software system we called Readware which performs automated text exploration and analysis in English, German and French, on and off the internet.