arXiv subjects

Janusz A. Starzyk

Publications and source records attributed to Janusz A. Starzyk.

4 recordsLinked to original sources

arXiv2026

From Biological Precursors to Artificial Cognition: Consciousness, Embodiment, and the MEM Architecture

This article asks under what conditions artificial intelligence could warrant a rational attribution of consciousness. Linguistic ability, multimodality, memory, planning, action control, and humanoid embodiment are not sufficient evidence of phenomenal experience. Biological precursors such as excitability, homeostasis, neural networks, and hierarchical representation instead identify functions whose counterparts may be engineered. The paper compares conventional LLMs, hybrid h-LLMs, vision-language-action systems, and embodied agents with the Motivated Emotional Mind (MEM) architecture. MEM adds receptor-grounded representations, regulatory self-monitoring and interoception, affect, semblion-based associative memory, and re-entry into lower sensory and interoceptive maps. A related formulation defines a phenomenal state as a dynamic, integrated state of the whole embodied system in which sensor-grounded modal content is recurrently stabilized and coupled to a suprathreshold interoceptive representation of the system's regulatory state. The mathematical model of MEM published on arXiv specifies implementable relations among these components and makes the proposal more precise and testable. Comparative and causal tests in humans, invertebrates, and artificial systems could strengthen or weaken the MEM capabilities. MEM is therefore presented as a falsifiable research program requiring empirical validation and ethical caution.

q-bio.NC↗

arXiv2026

A Mathematical Model of Motivated Emotional Mind - Cognitive Embodied System

This article presents a mathematical model of the Motivated Emotional Mind cognitive architecture developed for embodied intelligent systems. Such a system learns to maintain its homeostasis through a generalized form of reinforcement learning based on its internal motivations, termed motivated learning (ML). The principal contribution of this article is a rigorous formalization of the re-entrant loop integrating feedforward processing, lateral interactions, and feedback pathways, together with the representational selection mechanisms that govern adaptive system responses. The model specifies how ongoing exteroceptive and interoceptive signals, bodily-motivational context, and memory traces are bound into associative memory structures termed semblions, which compete for access to further processing and top-down reconstruction. The formalization encompasses secondary perception, representational competition, curiosity, procedural gaps, and action selection directed toward limiting allostatic violations. Within this framework, motivated learning is tailored to embodied systems whose dynamics are shaped by needs, affect, and the current regulatory state. Unlike standard reinforcement-learning models, the proposed approach incorporates need thresholds, goal generation and shifting goals, bodily state, resource constraints, and action uncertainty, thereby providing a more adequate account of response selection under regulatory pressure. Global affect functions as a central control signal, modulating the learning rate, representational valence, and the balance between exploration and exploitation. The model presented here is a step toward a more rigorous formalization of cognitive phenomena and may provide a basis for further theoretical analysis, computer simulation, and implementation in artificial-intelligence systems inspired by biological processes.

q-bio.NC↗

arXiv2026

SSAKG 2.0: An Open-Source Package for Structural Associative Sequence Memory and Context-Based Retrieval

This article presents SSAKG 2.0, an open-source software package for constructing and operating Structural Sequential Associative Knowledge Graphs (SSAKGs). An SSAKG represents objects as graph vertices and ordered sequences as structural patterns of graph connections. The resulting sparse graph is used as an associative memory in which complete sequences can be reconstructed from a partial, unordered context. Version 2.0 introduces new algorithms that exploit individual bits of computer memory to efficiently search graph connections. The package is implemented in Python, while performance-critical graph operations are implemented in C and exposed through a Python interface. This hybrid implementation provides a flexible high-level programming environment while reducing the memory and computational overhead associated with large sparse graphs. The algorithms were evaluated using randomly generated numerical sequences, sequences derived from sentences in the NLTK corpus, and mRNA sequences. The experiments demonstrate the ability of the package to store and reconstruct sequences from partial contexts and provide a basis for evaluating the effects of graph density, sequence length, and memory size on retrieval performance. SSAKG 2.0 is distributed under the Apache 2.0 open-source license. The package includes documentation and reproducible examples and is publicly available through GitHub and the Python Package Index (PyPI).

cs.AI↗

arXiv2025

Associative Knowledge Graphs for Efficient Sequence Storage and Retrieval

The paper addresses challenges in storing and retrieving sequences in contexts like anomaly detection, behavior prediction, and genetic information analysis. Associative Knowledge Graphs (AKGs) offer a promising approach by leveraging sparse graph structures to encode sequences. The objective was to develop a method for sequence storage and retrieval using AKGs that maintain high memory capacity and context-based retrieval accuracy while introducing algorithms for efficient element ordering. The study utilized Sequential Structural Associative Knowledge Graphs (SSAKGs). These graphs encode sequences as transitive tournaments with nodes representing objects and edges defining the order. Four ordering algorithms were developed and tested: Simple Sort, Node Ordering, Enhanced Node Ordering, and Weighted Edges Node Ordering. The evaluation was conducted on synthetic datasets consisting of random sequences of varying lengths and distributions, and real-world datasets, including sentence-based sequences from the NLTK library and miRNA sequences mapped symbolically with a window-based approach. Metrics such as precision, sensitivity, and specificity were employed to assess performance. SSAKGs exhibited quadratic growth in memory capacity relative to graph size. This study introduces a novel structural approach for sequence storage and retrieval. Key advantages include no training requirements, flexible context-based reconstruction, and high efficiency in sparse memory graphs. With broad applications in computational neuroscience and bioinformatics, the approach offers scalable solutions for sequence-based memory tasks.

cs.AI↗