CURRENT ARCHITECTURES FOR DECISION-MAKING BY AUTONOMOUS AGENTS
DOI:
https://doi.org/10.32782/2786-9024/v4i6(38).359287Keywords:
autonomous agents, decision-making architectures, neuro-symbolic artificial intelligence, deep learning, symbolic logic, behavior trees, knowledge graphs, semantic modeling.Abstract
Autonomous agents operating in highly dynamic and stochastic environments with a high degree of uncertainty require computationally efficient and reliable decision-making architectures. Historically, the control of such systems has been based on classical paradigms, including reactive architectures, finite state machines, and behavior trees. However, these methods face the problem of an exponential combinatorial explosion of the state space in unstructured conditions and exhibit a critical degradation in performance due to their inability to adapt continuously. At the same time, the transition to modern, purely neural network-based control methods is accompanied by an inherent tendency of systems toward stochastic hallucinations, epistemic opacity of decision- making mechanisms, and a fundamental inability to provide deterministic mathematical guarantees of safe operation. This article investigates and justifies hybrid neurosymbolic architectures that synergistically combine the approximation capabilities of deep learning methods for processing multimodal sensory data with the mathematical rigor and semantic interpretability of classical symbolic logic methods. A comprehensive analysis was conducted of the structural integration of neural network modules for high-level feature extraction with graph-based world models and hierarchical symbolic planners. Particular attention is paid to solving the problem of semantic ambiguity through automated verification of the structure of knowledge graphs and the elimination of logical conflicts prior to the start of the physical execution stage. The promise of using semantic scene decomposition for optimizing computational resources has been demonstrated.
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