Σχεδίαση και ανάπτυξη ενός στρατηγικού εταίρου τεχνητής νοημοσύνης : μια ολοκληρωμένη αρχιτεκτονική βασισμένη σε δυναμικά ψηφιακά δίδυμα και μεγάλα γλωσσικά μοντέλα

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Keywords
Τεχνητή νοημοσύνη ; Δυναμικό ψηφιακό δίδυμο ; Μεγάλα γλωσσικά μοντέλα ; RAG ; Γράφοι γνώσης ; Προσομοίωση Monte Carlo ; PICP ; Επεξηγήσιμη τεχνητή νοημοσύνηAbstract
Despite their impressive capabilities, modern artificial intelligence systems typically function as passive assistants, where the user asks a question and the system provides a response. This thesis proposes and experimentally documents the transformation of this passive role into a true strategic partner, capable of actively analyzing data, simulating scenarios, and evaluating the reliability of its own outputs.
The proposed architecture is based on the concept of a Dynamic Enterprise Digital Twin and is organized into four core pillars. These include Deep Knowledge through hybrid search, self-correcting RAG, and knowledge graphs; Reasoning with dynamic skill injection; Simulation via stochastic Monte Carlo analysis; and finally, Trust through the use of a composite reliability score. The coordination of these pillars is achieved via the Pillar Integration Context Protocol (PICP), a protocol that applies distributed systems principles to the coordination of artificial intelligence subsystems.
For the evaluation of the system, seven experiments were conducted using a dataset comprising 544,629 real transactions. The data were extracted from the Enterprise Resource Planning (ERP) system of a Greek commercial enterprise, covering the period from 2004 to 2024. The primary findings were highly positive, with hybrid search achieving an nDCG@5 score of 0.926 (95% CI: [0.833–0.991]), while routing accuracy reached 80% with errors exclusively on ambiguous queries. The self-correcting mechanism achieved a 66.7% pass rate, with failure analysis revealing three distinct categories (cross-temporal synthesis, variable correlation, inferential analysis) that reflect the fundamental boundary between retrieval and reasoning. Concurrently, the Monte Carlo simulation demonstrated convergence at 500 samples with a retrospective coverage of 75%. Notably, the PICP protocol exhibited absolute resilience across eight different failure scenarios, with a negligible temporal overhead of less than 1 ms.
Overall, this work offers a threefold contribution. At a theoretical level, it proposes a comprehensive framework for the integration of knowledge, reasoning, simulation, and trust. At a practical level, it introduces PICP as an original application of distributed coordination, while at an empirical level, it provides reproducible results through 459 automated tests (89.3% code coverage) and 9.291 lines of code.


