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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.
btuktcogsys
Bereits in der Frühphase der Kybernetik stellte C. E. Shannon ein Labyrinthexperiment zur Problemlösung vor, das sich an der mythologischen Figur des Theseus orientierte. Seine Lösung kommt dabei ohne ein Bedeutungskonzept aus. Eine Problemanalyse aus psychologischer Sicht macht allerdings deutlich, dass bei diesem Experiment wesentliche kognitive Aspekte nicht berücksichtigt wurden. Um diese Aspekte herauszustellen, befassen wir uns kurz mit den mythologischen und historischen Hintergründen des Labyrinthproblems und erläutern anschließend, warum wir über Shannons Modellvorstellungen hinausgehen müssen und Verfahren benötigen, mit denen wir zu semantischen Datenträgern gelangen können. Zur Veranschaulichung unserer Methode modifizieren wir die Problemstellung, in dem wir "Theseus" die Befriedigung der Bedürfnisse Hunger und Durst ermöglichen. Die Grundlage des Verfahrens ist die Festlegung von Zielmerkmalen und daran anschließend die Identifikation von lösungsrelevanten Steuermerkmalen, welche "Theseus" zur Problemlösung nutzen kann. Wir verwenden dabei den mathematischen Formalismus der Quantenmechanik, da er uns die Ableitung von semantischen Strukturen auf eine sehr elegante Weise erlaubt. Zunächst exploriert "Theuseus" die Umgebung und erhält eine kompakte Repräsentation in Form eines Weltvektors in einem Hilbertraum, der durch Tensorverknüpfungen verschiedener Merkmalsräume entsteht. Die Kombination dieser Merkmalsräume führt auf die mathematische Struktur eines Verbandes. Ausgehend vom gefundenen Weltvektor stellen wir ein Verfahren vor, mit dem "Theseus" Unterstrukturen des Verbandes bestimmen kann, welche semantischen Strukturen entsprechen und anschließend für die Problemlösung verwendet werden können. Wir demonstrieren das Verfahren an verschiedenen Weltkonfigurationen. Den dabei abgeleiteten semantischen Strukturen kann man entnehmen, welche Merkmale berücksichtigt werden müssen, um die Zielmerkmale einer gegebenen Konfiguration manipulieren zu können.
In this paper we give instructions on how to write a minimalist grammar (MG). In order to present the instructions as an algorithm, we use a variant of context free grammars (CFG) as an input format. We can exclude overgeneration, if the CFG has no recursion, i.e. no non-terminal can (indirectly) derive to a right-hand side containing itself. The constructed MGs utilize licensors/-ees as a special way of exception handling. A CFG format for a derivation A_eats_B↦∗peter_eats_apples, where A and B generate noun phrases, normally leads to overgeneration, e.\,g., i_eats_apples. In order to avoid overgeneration, a CFG would need many non-terminal symbols and rules, that mainly produce the same word, just to handle exceptions. In our MGs however, we can summarize CFG rules that produce the same word in one item and handle exceptions by a proper distribution of licensees/-ors. The difficulty with this technique is that in most generations the majority of licensees/-ors is not needed, but still has to be triggered somehow. We solve this problem with ϵ-items called \emph{adapters}.
The concept of intelligent agents is—roughly speaking—based on an architecture and a set of behavioral programs that primarily serve to solve problems autonomously.
Increasing the degree of autonomy and improving cognitive performance, which can be assessed using cognitive and behavioral tests, are two important research trends. The degree of autonomy can be increased using higher-level psychological modules with which needs and motives are taken into account. In our approach we integrate these modules in architecture for an embodied, enactive multi-agent system, such that distributed problem solutions can be achieved. Furthermore, after uncovering some weaknesses in the cognitive performance of traditionally designed agents, we focus on two major aspects. On the one hand, the knowledge processing of cognitive agents is based on logical formalisms, which have deficiencies in the representation and processing of incomplete or uncertain knowledge. On the other hand, in order to fully understand the performance of cognitive agents, explanations at the symbolic and subsymbolic levels are required. Both aspects can be addressed by quantum-inspired cognitive agents. To investigate this approach, we consider two tasks in the sphere of Shannon's famous mouse-maze problem: namely classifying target objects and ontology inference. First, the classification of an unknown target object in the mouse-maze, such as cheese, water, and bacon, is based on sensory data that measure characteristics such as odor, color, shape, or nature. For an intelligent agent, we need a classifier with good prediction accuracy and explanatory power on a symbolic level. Boolean logic classifiers do work on a symbolic level but are not adequate for dealing with continuous data. Therefore, we demonstrate and evaluate a quantum-logic-inspired classifier in comparison to Boolean-logic-based classifiers. Second, ontology inference is iteratively achieved by a quantum-inspired agent through maze exploration. This requires the agent to be able to manipulate its own state by performing actions and by collecting sensory data during perception. We suggest an algebraic approach where both kinds of behaviors are uniquely described by quantum operators. The agent's state space is then iteratively constructed by carrying out unitary action operators, while Hermitian perception operators act as observables on quantum eigenstates. As a result, an ontology emerges as the simultaneous solution of the respective eigenvalue equations.
Tags: btuktqiai; btuktcogsys; btukttheseus; btuktqtheseus
Speech-controlled user interfaces facilitate the operation of devices and household functions to laymen. State-of-the-art language technology scans the acoustically analyzed speech signal for relevant keywords that are subsequently inserted into semantic slots to interpret the user's intent. In order to develop proper cognitive information and communication technologies, simple slot-filling should be replaced by utterance meaning transducers (UMT) that are based on semantic parsers and a mental lexicon, comprising syntactic, phonetic and semantic features of the language under consideration. This lexicon must be acquired by a cognitive agent during interaction with its users. We outline a reinforcement learning algorithm for the acquisition of the syntactic morphology and arithmetic semantics of English numerals, based on minimalist grammar (MG), a recent computational implementation of generative linguistics. Number words are presented to the agent by a teacher in form of utterance meaning pairs (UMP) where the meanings are encoded as arithmetic terms. Since MG encodes universal linguistic competence through inference rules, thereby separating innate linguistic knowledge from the contingently acquired lexicon, our approach unifies generative grammar and reinforcement learning, hence potentially resolving the still pending Chomsky-Skinner controversy.
ucuikt2015