Cognitive Architecture for Decision-Making Based on Brain Principles Programming (in Russian)

• URL: http://arxiv.org/abs/2302.09377v1
• Date: Sat, 18 Feb 2023 16:34:05 GMT
• Title: Cognitive Architecture for Decision-Making Based on Brain Principles Programming (in Russian)
• Authors: Anton Kolonin, Andrey Kurpatov, Artem Molchanov, Gennadiy Averyanov
• Abstract summary: We describe a cognitive architecture intended to solve a wide range of problems based on the five identified principles of brain activity. We provide a basic ontology for a number of practical applications as well as for the functional domain based upon it, describe the proposed architecture, and give possible examples of the execution of these applications in this architecture.
• Score: 0.34410212782758043
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We describe a cognitive architecture intended to solve a wide range of problems based on the five identified principles of brain activity, with their implementation in three subsystems: logical-probabilistic inference, probabilistic formal concepts, and functional systems theory. Building an architecture involves the implementation of a task-driven approach that allows defining the target functions of applied applications as tasks formulated in terms of the operating environment corresponding to the task, expressed in the applied ontology. We provide a basic ontology for a number of practical applications as well as for the subject domain ontologies based upon it, describe the proposed architecture, and give possible examples of the execution of these applications in this architecture.

Related papers

• Task Agnostic Architecture for Algorithm Induction via Implicit Composition [10.627575117586417]
  This position paper aims to explore developing such a unified architecture and proposes a theoretical framework of how it could be constructed. Recent Generative AI, especially Transformer-based models, demonstrate potential as an architecture capable of constructing algorithms for a wide range of domains. Our exploration delves into current capabilities and limitations of Transformer-based and other methods in efficient and correct algorithm composition.
  arXiv  Detail & Related papers  (2024-04-03T04:31:09Z)
• Hierarchical Invariance for Robust and Interpretable Vision Tasks at Larger Scales [54.78115855552886]
  We show how to construct over-complete invariants with a Convolutional Neural Networks (CNN)-like hierarchical architecture. With the over-completeness, discriminative features w.r.t. the task can be adaptively formed in a Neural Architecture Search (NAS)-like manner. For robust and interpretable vision tasks at larger scales, hierarchical invariant representation can be considered as an effective alternative to traditional CNN and invariants.
  arXiv  Detail & Related papers  (2024-02-23T16:50:07Z)
• An Encoding of Abstract Dialectical Frameworks into Higher-Order Logic [57.24311218570012]
  This approach allows for the computer-assisted analysis of abstract dialectical frameworks. Exemplary applications include the formal analysis and verification of meta-theoretical properties.
  arXiv  Detail & Related papers  (2023-12-08T09:32:26Z)
• Proceedings Modalities in substructural logics: Applications at the interfaces of logic, language and computation [0.0]
  The focus of the workshop is on applications in the areas of interest to the ESSLLI community, in particular logical approaches to natural language syntax and semantics and the dynamics of reasoning. The workshop is held with the support of the Horizon 2020 MSCA-Rise project MOSAIC.
  arXiv  Detail & Related papers  (2023-08-01T22:40:19Z)
• Conjunctive Query Based Constraint Solving For Feature Model Configuration [79.14348940034351]
  We show how to apply conjunctive queries to solve constraint satisfaction problems. This approach allows the application of a wide-spread database technology to solve configuration tasks.
  arXiv  Detail & Related papers  (2023-04-26T10:08:07Z)
• Cognitive Architecture for Decision-Making Based on Brain Principles Programming [0.3867363075280543]
  We describe a cognitive architecture intended to solve a wide range of problems based on the five identified principles of brain activity. We provide a basic ontology for a number of practical applications as well as for the functional domain based upon it, describe the proposed architecture, and give possible examples of the execution of these applications in this architecture.
  arXiv  Detail & Related papers  (2022-04-17T04:25:20Z)
• Proceedings of the Sixteenth Workshop on Logical Frameworks and Meta-Languages: Theory and Practice [0.0]
  Design, implementation and their use in reasoning tasks have been the focus of considerable research over the last two decades. This workshop brings together designers, implementors and practitioners to discuss various aspects impinging on the structure and utility of logical frameworks.
  arXiv  Detail & Related papers  (2021-07-14T05:19:09Z)
• Towards a Predictive Processing Implementation of the Common Model of Cognition [79.63867412771461]
  We describe an implementation of the common model of cognition grounded in neural generative coding and holographic associative memory. The proposed system creates the groundwork for developing agents that learn continually from diverse tasks as well as model human performance at larger scales.
  arXiv  Detail & Related papers  (2021-05-15T22:55:23Z)
• The General Theory of General Intelligence: A Pragmatic Patternist Perspective [0.0]
  Review covers underlying philosophies, formalizations of the concept of intelligence, and a proposed high level architecture for AGI systems. The specifics of human-like cognitive architecture are presented as manifestations of these general principles. Lessons for practical implementation of advanced AGI in frameworks such as OpenCog Hyperon are briefly considered.
  arXiv  Detail & Related papers  (2021-03-28T10:11:25Z)
• Planning with Submodular Objective Functions [118.0376288522372]
  We study planning with submodular objective functions, where instead of maximizing cumulative reward, the goal is to maximize the value induced by a submodular function. Our framework subsumes standard planning and submodular objective with cardinality constraints as special cases.
  arXiv  Detail & Related papers  (2020-10-22T16:55:12Z)
• Understanding Deep Architectures with Reasoning Layer [60.90906477693774]
  We show that properties of the algorithm layers, such as convergence, stability, and sensitivity, are intimately related to the approximation and generalization abilities of the end-to-end model. Our theory can provide useful guidelines for designing deep architectures with reasoning layers.
  arXiv  Detail & Related papers  (2020-06-24T00:26:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.