Towards a Quantum Mechanical Model of the Inner Stage of Cognitive Agents

Wolff, Matthias; Huber, Markus; Wirsching, Günther; Römer, Ronald; Graben, Peter beim; Schmitt, Ingo

doi:10.1109/coginfocom.2018.8639892

Cited by 10 publications

(8 citation statements)

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“…Potential of this approach is evidenced by Tomkins' vision that natural languages are centuries ahead of psychology in modeling of subtle distinctions in affect complexes Tomkins (1981). Indeed, experiment of Section 4 showed that word2vec data reflect implicit affective information allowing for extraction 10 This setting resonates with the goals pursued in robotics and AI Breazeal (2003); Cavallo et al (2018); Deng, Huang, and Perkowski (2021); Kotov et al (2021); Pessoa (2019b); Samsonovich (2020b); Vallverdú et al (2016); Wolff et al (2018); Yan, Iliyasu, and Hirota (2021b), (Samsonovich, 2020a, ch.7,10,13,34,54,56,57). As in natural cognitive systems, low-dimensional emotional states can be used to decouple stimulus from response Scherer (1982), as required for non-trivial decision making.…”

Section: Quantum Methods For Practical Emotion Sciencementioning

confidence: 83%

Quantum Core Affect. Process-Semantic Theory of Emotions

Surov¹

2021

Preprint

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The paper describes model of human affect based on quantum theory of semantics. The model considers emotion as subjective representation of behavioral context relative to a basis binary choice, organized by cyclical process structure and an orthogonal evaluation axis. The resulting spherical space, generalizing well-known circumplex models, accommodates basic emotions in specific angular domains. Predicted process-semantic structure of affect is observed in the word2vec data, as well as in the previously obtained spaces of emotion concepts. The established quantum-theoretic structure of affective space connects emotion science with quantum models of cognition and behavior, opening perspective for synergetic progress in these fields.

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Section: Quantum Methods For Practical Emotion Sciencementioning

confidence: 83%

Quantum Core Affect. Process-Semantic Theory of Emotions

Surov¹

2021

Preprint

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“…We consider the well-known mouse-maze problem (Shannon, 1953;Wolff et al, 2015Wolff et al, , 2018, where an artificial mouse lives in a simple N×M maze world that is given by a certain configuration of walls. For the mouse to survive, one ore more target objects are located at some places in the maze.…”

Section: Problem Statement and Experimental Setupmentioning

confidence: 99%

“…In contrast, the perceptive part of interaction is based on sensors and classifiers only, so that no relations between any object types can be directly encoded (Hausser, 2014). Hence, such relations have to be learned by the agent (Wolff et al, 2018). Yet, in a shared environment these different kinds of information can be used to solve the context problem.…”

Section: Knowledge Integrationmentioning

confidence: 99%

“…This work was partly based on former joint publications of the authors with Werner Meyer, Günther Wirsching, and Ingo Schmitt (Wolff et al, 2018;beim Graben et al, 2019bbeim Graben et al, , 2020Römer et al, 2019). The content of this manuscript has been presented in part at the 10th IEEE International Conference on Cognitive Infocommunications (CogInfoCom 2019, Naples, Italy) (beim Graben et al, 2019b;Römer et al, 2019).…”

Section: Author Contributionsmentioning

confidence: 99%

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Unifying Physical Interaction, Linguistic Communication, and Language Acquisition of Cognitive Agents by Minimalist Grammars

Römer

Graben

Huber-Liebl

et al. 2022

Front. Comput. Sci.

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Cognitive agents that act independently and solve problems in their environment on behalf of a user are referred to as autonomous. In order to increase the degree of autonomy, advanced cognitive architectures also contain higher-level psychological modules with which needs and motives of the agent are also taken into account and with which the behavior of the agent can be controlled. Regardless of the level of autonomy, successful behavior is based on interacting with the environment and being able to communicate with other agents or users. The agent can use these skills to learn a truthful knowledge model of the environment and thus predict the consequences of its own actions. For this purpose, the symbolic information received during the interaction and communication must be converted into representational data structures so that they can be stored in the knowledge model, processed logically and retrieved from there. Here, we firstly outline a grammar-based transformation mechanism that unifies the description of physical interaction and linguistic communication and on which the language acquisition is based. Specifically, we use minimalist grammar (MG) for this aim, which is a recent computational implementation of generative linguistics. In order to develop proper cognitive information and communication technologies, we are using utterance meaning transducers (UMT) that are based on semantic parsers and a mental lexicon, comprising syntactic and semantic features of the language under consideration. This lexicon must be acquired by a cognitive agent during interaction with its users. To this aim we outline a reinforcement learning algorithm for the acquisition of syntax and semantics of English utterances. English declarative sentences are presented to the agent by a teacher in form of utterance meaning pairs (UMP) where the meanings are encoded as formulas of predicate logic. Since MG codifies 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.

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“…VSA have recently been employed for semantic spaces [13], logical inferences [14,15], data base queries [16], and autoassociative memories [17,18]. Wolff et al [19] developed a VSA model for cognitive representations and their induction in Shannon's mouse-maze system. In this article, we focus to the dashed region in Fig.…”

Section: Introductionmentioning

confidence: 99%