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Machine Semiotics
(2023)
Recognizing a basic difference between the semiotics of humans and machines presents a possibility to overcome the shortcomings of current speech assistive devices. For the machine, the meaning of a (human) utterance is defined by its own scope of actions. Machines, thus, do not need to understand the conventional meaning of an utterance. Rather, they draw conversational implicatures in the sense of (neo-)Gricean pragmatics. For speech assistive devices, the learning of machine-specific meanings of human utterances, i.e. the fossilization of conversational implicatures into conventionalized ones by trial and error through lexicalization appears to be sufficient. Using the quite trivial example of a cognitive heating | device, we show that — based on dynamic semantics — this process can be formalized as the reinforcement learning of utterance-meaning pairs (UMP).
Zunächst möchten wir in das Thema einführen, indem wir kurz auf
gegenwärtige Probleme und zukünftige Möglichkeiten von Sprachassistenten eingehen. Es folgen grundlegende Überlegungen zur Semantik und zwei fundamentale Erkenntnisse aus der Biosemiotik und dem Konstruktivismus. Danach fokussieren wir uns auf den Spracherwerbsprozess. Hierbei kommt ein Petrinetz zum Einsatz, das einem elementaren, bereits in früheren Arbeiten vorgestellten, Verstärkungsalgorithmus folgt. Dem schließt sich eine Demonstration des Spracherwerbs anhand eines Beispiels aus dem Smart Car-Bereich an.
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Metastable attractors and heteroclinic orbits are present in the dynamics of various complex systems. Although their occurrence is well-known, their identification and modeling is a challenging task. The present work reviews briefly the literature and proposes a novel combination of their identification in experimental data and their modeling by dynamical systems. This combination applies recurrence structure analysis permitting the derivation of an optimal symbolic representation of metastable states and their dynamical transitions. To derive heteroclinic sequences of metastable attractors in various experimental conditions, the work introduces a Hausdorff clustering algorithm for symbolic dynamics. The application to brain signals (event-related potentials) utilizing neural field models illustrates the methodology.
Computation is classically studied in terms of automata, formal languages and algorithms; yet, the relation between neural dynamics and symbolic representations and operations is still unclear in traditional eliminative connectionism. Therefore, we suggest a unique perspective on this central issue, to which we would like to refer as to transparent connectionism, by proposing accounts of how symbolic computation can be implemented in neural substrates. In this study we first introduce a new model of dynamics on a symbolic space, the versatile shift, showing that it supports the real-time simulation of a range of automata. We then show that the Gödelization of versatile shifts defines nonlinear dynamical automata, dynamical systems evolving on a vectorial space. Finally, we present a mapping between nonlinear dynamical automata and recurrent artificial neural networks. The mapping defines an architecture characterized by its granular modularity, where data, symbolic operations and their control are not only distinguishable in activation space, but also spatially localizable in the network itself, while maintaining a distributed encoding of symbolic representations. The resulting networks simulate automata in real-time and are programmed directly, in absence of network training. To discuss the unique characteristics of the architecture and their consequences, we present two examples: i) the design of a Central Pattern Generator from a finite-state locomotive controller, and ii) the creation of a network simulating a system of interactive automata that supports the parsing of garden-path sentences as investigated in psycholinguistics experiments.
How well does a given pitch fit into a tonal scale or key, being either a major or minor key? This question addresses the well-known phenomenon of tonal attraction in music psychology. Metaphorically, tonal attraction is often described in terms of attracting and repelling forces that are exerted upon a probe tone of a scale. In modern physics, forces are related to gauge fields expressing fundamental symmetries of a theory. In this study we address the intriguing relationship between musical symmetries and gauge forces in the framework of quantum cognition.
Recurrence structures in univariate time series are challenging to detect. We propose a combination of symbolic and recurrence analysis in order to identify recurrence domains in the signal. This method allows to obtain a symbolic representation of the data. Recurrence analysis produces valid results for multidimensional data, however, in the case of univariate time series one should perform phase space reconstruction first. In this chapter, we propose a new method of phase space reconstruction based on the signal's time-frequency representation and compare it to the delay embedding method. We argue that the proposed method outperforms the delay embedding reconstruction in the case of oscillatory signals. We also propose to use recurrence complexity as a quantitative feature of a signal. We evaluate our method on synthetic data and show its application to experimental EEG signals.
Ein wesentlicher Bestandteil kognitiver – und damit auch sprach-verarbeitender – Agentensysteme ist die Verhaltenssteuerung. Eine ihrer wichtigsten Aufgaben besteht darin, Entscheidungsprobleme, die in der Auseinandersetzung mit der physischen Umgebung eines Agenten auftreten, rational zu lösen. Zunächst diskutieren wir die klassischen, nutzenbasierten Entscheidungsmodelle für sequentielle- und episodische Umgebungen am Beispiel des Mouse-Maze-Problems. Zu letzteren gehören die sogenannten konsummatorischen Endhandlungen, sie stehen im Mittelpunkt dieser Arbeit. Weil sich Entscheidungen bei Endhandlungen final auf die Lebensdauer des Agenten auswirken können, dürfen Fehlleistungen und Täuschungen nur äußerst selten auftreten. Aus diesem Grund stellen wir der rationalen Methode die psychologische Methode entgegen, die zusätzlich auf der Einführung von semantischen Kategorien beruht. Die dafür benötigten semantischen Repräsentationen modellieren wir nach einem quantenmechanischen Formalismus. Daraus leitet sich die zentrale Fragestellung dieser Arbeitab: Bietet die quantenmechanische Perspektive einen Mehrwert bei der Modellierung der konsummatorischen Endhandlung und lässt sich dieser Formalismus inden Perzeptions-Aktionszyklus (PAC) eines kognitiven Agenten einbetten.
Vector symbolic architectures (VSA) are a viable approach for the hyperdimensional representation of symbolic data, such as documents, syntactic structures, or semantic frames. We present a rigorous mathematical framework for the representation of phrase structure trees and parse trees of context-free grammars (CFG) in Fock space, i.e. infinite-dimensional Hilbert space as being used in quantum field theory. We define a novel normal form for CFG by means of term algebras. Using a recently developed software toolbox, called FockBox, we construct Fock space representations for the trees built up by a CFG left-corner (LC) parser. We prove a universal representation theorem for CFG term algebras in Fock space and illustrate our findings through a low-dimensional principal component projection of the LC parser state. Our approach could leverage the development of VSA for explainable artificial intelligence (XAI) by means of hyperdimensional deep neural computation.