Artificial intelligence driven decision-making under uncertainty
Bibliographic record
Abstract
Decision-making is a fundamental problem in the modern world.Technology has developed to a level where automated decision-making is used even in safety-critical systems such as self-driving cars and industrial gas turbines.The design and operation of such systems often requires decision-making without full-knowledge or information, i.e., decision-making under uncertainty.Uncertainty can manifest itself in many ways.Notable examples include non-reliable and/or delayed information.This thesis investigates methods to advance and improve the reliability of artificial intelligence led decision-making systems under these forms of uncertainty.This thesis covers both sequential decision-making and predictive systems.These are investigated on the backdrop of two industrial application spaces: food retail and cyber-physical systems.First, this thesis develops novel algorithms for multi-armed bandits and sequential decision-making.We present the first practical computation and indexing method for the optimal policy for Bernoulli multi-armed bandits, which was previously considered intractable for several decades.Furthermore, we provide the optimal policy for Bernoulli bandits with delayed decision outcomes.We benchmark and gauge existing popular algorithms and showcase how performance deteriorates significantly in the presence of delay.We then generalize the concepts to non-Bernoulli distributions with delay.To exploit sequential decision-making in a practical application, we build a simulator to serve as a sandbox for experimentation to reduce food waste in food retail.We present a flexible framework capable of simulating a variety of food retail entities and their interactions.Each entity is controllable by reinforcement learning agents.We demonstrate how combining simulation with reinforcement learning can effectively reduce food waste and increase profits relative to a baseline.Finally, the thesis investigates and provides methodologies for building more robust predictive systems in the presence of information uncertainty.Many industrial problems require decision-making with limited information or potentially unreliable information.In collaboration with Siemens Energy as industrial partner, we develop machine learning predictors used for designing aeroderivative gas turbines as complex cyber-physical systems.We also provide a methodology for deploying such machine learning predictors to existing resource-constrained control hardware.In conclusion, this thesis provides novel decision-making techniques for various forms of uncertainty by exploiting both theoretical and practical results across different application domains.prise de décision avec des informations limitées ou potentiellement peu fiables.En collaboration avec Siemens Energy comme partenaire industriel, nous développons des prédicteurs d'apprentissage automatique utilisés pour concevoir des turbines à gaz aérodérivées en tant que systèmes cyber-physiques complexes.Nous fournissons également une méthodologie pour le déploiement de ces prédicteurs d'apprentissage automatique dans le matériel de contrôle à ressources limitées existant.En conclusion, cette thèse fournit de nouvelles techniques de prise de décision pour diverses formes d'incertitude en exploitant les résultats théoriques et pratiques dans différents domaines d'application.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".