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The Quantum Mouse

Quantum-Inspired Behavior Controller for a Remake of C. E. Shannon's Electrically Controlled Mouse

Abstract

Motivation and Background

C. E. Shannon's  "Electrically Controlled Mouse" Theseus is an early and prominent example of what we call a cognitive or intelligent agent today. Shannon's construction was based on an array of telephone relays which provided the mouse with a simple memory, much like Ariadne's thread, to find its way through a configurable maze. Contemporary cognitive agents use state space models in connection with search algorithms and decision process models to solve similar problems. They have deficits in handling not-yet knowing in terms of formal logic (e.g., stochastic decision processes) and with explainability (e.g., deep neural networks). Both capabilities are, however, essential for cognitive agents. In this project we investigate hyper-dimensional state space models [Miz89, Smo90]—aka. vector-symbolic architectures [Gay06, Kle18]—to devise a so-called inner stage for final cognitive agents [Bis09], on which the agent can safely perform trial actions and plan its strategy. To that end, we develop a representation theory of semantic states, expressed by relational databases or feature-value relations, in Hilbert or Fock space [Foc32]. In this hyper-dimensional representation we can employ quantum logic [BN36] or other quantum-inspired methods to design the agent’s behavior controller and to model higher cognitive functions like, e.g., coping. Fig. 1 outlines the general architecture of a quantum-inspired cognitive agent (see [WHW+18] for details).

Goals
  • Refine and enhance Ingo Schmitt's Hilbert space representation theory of relational databases, e.g., handling of uncertain knowledge or NIL values,
  • Devise a representation theory of semantic states, expressed by feature-value relations, in Fock space,
  • Devise quantum-inspired algorithms, i.e., Hilbert and/or Fock space operator compositions, for agent actions, attention, natural language communication, ontology inference, and coping,
  • Construct a test bed and demonstrator for the application example of Shannon's mouse-maze system, and
  • Ultimately: devise quantum computer algorithms for the behavior control of cognitive agents.
Results
  • First steps taken towards representation theories and modeling of the behavior controller (see bibliography below),
  • First version of Matlab toolbox called FockBox published.

(Click or tap on images to enlarge)

References
[Bis09]Bischof, N. (2009). Psychologie, ein Grundkurs für Anspruchsvolle. Verlag Kohlhammer, 2009, 2. Auflage.
[BN36]Birkhoff, G. and von Neumann, J. (1936). The logic of quantum mechanics. Annals of Mathematics 37(4), 823{843 (1936). URL http://www.jstor.org/stable/1968621
[Foc32]Fock, V. A. (1932). Konfigurationsraum und zweite Quantelung. Z. Phys. 75, 622 – 647
[Gay06]Gayler, R. W. (2006). Vector symbolic architectures are a viable alternative for Jackendoff’s challenges.Behavioral and Brain Sciences, 29:78 – 79.
[Kle18]Kleyko, D. (2018). Vector Symbolic Architectures and their Applications: Computing with Random Vectors in a Hyperdimensional Space. PhD-Thesis, Lulea Univ. of Technology. ISBN: 978-91-7790-110-5.
[Miz89]Mizraji, E. (1989). Context-dependent associations in linear distributed memories. Bulletin of Mathematical Biology, 51(2):195 – 205.
[Smo90]Smolensky, P. (1990). Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artificial Intelligence, 46(1-2):159 – 216.
[WHW+18]Wolff, M., Huber, M., Wirsching, G., Römer, R., beim Graben, P., and Schmitt, I. (2018). Towards a Quantum Mechanical Model of the Inner Stage of Cognitive Agents. 2018 9th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), 2018, pp. 000147 – 000152, doi: 10.1109/CogInfoCom.2018.8639892
Project Data
Period2015–today
FundingBTU self-funding

Bibliography and Links

Related Projects

Mission:

Devise quantum-inspired methods for behavior control, including higher cognitive functions, of final technical cognitive systems

Behavior Control of Intelligent Machines (VIM)

Verhaltenssteuerung für intelligente Maschinen (VIM)

This project is co-funded by the European Regional Development Fund (ERDF).

Abstract

The essential added value of intelligent machines lies in their ability to learn independently from experience, to plan, to act sensibly (via actuators) and—in a simple sense—to "think". These aspects are summarized under the term "behavior control" of the machine. Since today's technical solutions still show considerable deficits, we show in our EFRE-project "Behavioral control for intelligent machines" how a cognitive system transforms sensory information within a cognitive circle (interaction circle) into semiotic information so that it can be processed logically. The cognitive circle that is expanded with psychological and biological functionalities is implemented as a mouse-maze problem in an entity-relationship model. The mouse is represented by an agent that is divided into sub-agents, whose tasks are inspired by mythological characters. Golem executes instructions (motor function) and perceives structures and elements of the world through the sensor system, Homunculus is responsible for thinking and imagination about the world (inner stage), Theseus - for strategy finding, Demiurge - for the needs and Argus (as a control authority) - for damage avoidance.

Period:04/2020–06/2022
Funding:EU, EFRE-StaF
- Grant#85036696
- Total288 TEUR

Bibliography and Links

Mission:

Devise and construct a cognitive agent—subdivided into Golem, Homunculus, and Demiurge—based on a semantic algebra.

Researchers:

Students:

B.Sc. Friedrich Eckert

Innovation Hub 13 - TP5: Recommendation Engine

Abstract

Cognitive Systems are adaptive, adaptable and can act target‐oriented even with uncertain and incomplete information. So far Artificial Intelligence (AI) is used as an umbrella term for a diversity of methods for making data available and usable. This project (part of Innovation Hub 13) focuses on the integration of AI‐methods into a cognitive system. Such a system gathers information, reaches decisions and recommends actions. The desired cognitive system requires integration of three key technologies of AI: databases, text processing and behavior control. Initially, we create a methodical classified collection of concepts (thesaurus). Because in this project the raw data is text, the relevant terms and relations get extracted through syntactic and semantic analysis. The resulting structures are then stored in databases for universities, public authorities and industry. On this basis behavior control creates a scored matching and recommends partners and funding.

Period01/2018–12/2022
FundingBMBF, Innovative Hochschule
- Total603 TEUR
Websiteinnohub13.de

Bibliography and Links

[TODO@Christinan: UBICO keyword="btuktihre"]
Innovation Hub13 @Innovative Hochschule

Screenshots

Mission:

Devise and construct a semantic search matching research proposals, funding programs, and company profiles.

Abgeschlossen

Universal Cognitive User Interface (UCUI)

Abstract

Recent speech dialog systems and cognitive user interfaces allow natural verbal human-machine-interaction and achieve an excellent performance. However, leading commercial solutions heavily rely on transmitting sensitive user information (personal data, voice recordings, etc.) through public networks and on processing, storing and analyzing these data on servers of service providers.

The goal of the UCUI project is to develop a cognitive user interface for intuitive interaction with arbitrary electronic devices which ensures privacy by design. That means that all information processing is done on the device and that no user data ever leave the interface. To this end we develop a stand-alone hardware module doing all signal, speech and cognitive information processing. The main technical challenge lies in building a small, energy-efficient system which achieves an acceptable performance without relying on external computational power and memory.

Interaction with the UCUI device takes place through speech, acoustic and visual symbols, and gestures. The system shall be capable of learning from the behavior of users in order to improve its function. Multiple modules will be able to cooperate (distributed microphone array, task assignment, etc.) over a strongly encrypted wireless connection. The system design is based on a study of user-machine interactions in a real home-automation scenario and will take into account relevant legal and ethical aspects.

Period06/2015–05/2018
FundingBMBF, IKT 2020
- Grant#16SV7304
- Total1.6 Mio. EUR
- BTU454 TEUR

Mission:

Devise and construct a hardware cognitive user interface providing user data security by design

Researchers:

Partners:

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