Bibliographic record
Abstract
The ability to generalize from past experiences in order to address new situations is one of the cornerstones of human intelligence. As such, the design of artificial intelligence systems must---at some point---consider the issues involved in generalization. In automated planning, where the objectives are to design systems that are capable of finding courses of action to achieve specific goals given a formal description of their environment, we can say that a system exhibits generalization capabilities if it can leverage its previous solution-finding efforts when addressing new problems. The pervasive use of automation in modern industries signifies that this type of generalization---generalization in planning---is a fundamental requirement for the integration of artificial intelligence techniques into real-world applications. This dissertation aims to provide a generic high-level approach that can be used when confronted with sequential decision-making problems where generalization is important. Overall, the approach works by reformulating the problems into abstract representations, finding solutions for these abstract problems, and attempting to directly use the resulting abstract solutions in the concrete problems. This satisfies the generalization requirements when multiple concrete problems can be represented in one single abstract problem, or when the insights gathered when solving one abstract problem can be transferred towards solving related problems. We instantiate this approach in three different classes of planning problems. First, we address planning problems that have propositional and numeric state variables, including problems with nonlinear numeric constraints. We then address a type of generalized planning where a family of multiple planning problems is described through the use of first-order logic quantification, obtaining a single policy that can be applied on any of the problems. Finally, we show how we can solve reinforcement learning problems where multiple different tasks must be solved in a single environment. As with all reinforcement learning problems, the exact domain dynamics are initially unknown, and solutions must be obtained by repeatedly interacting with the environment. We prove the soundness of all our approaches and present empirical results that demonstrate their efficacy across various different domains. In many cases, we see orders of magnitude improvements in overall time efficiency.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".