High-level robot programming in dynamic and incompletely known environments
Bibliographic record
Abstract
This thesis advocates the usefulness and practicality of a logic-based approach to AI and in particular to high-level control of mobile robots. The contribution of the research work reported here is twofold: 1) the development of theoretical frameworks that account for uncertainty and unmodeled dynamics in an environment where an acting agent has to achieve certain goals and 2) the implementation of the developed ideas on a mobile robot. We have elaborated the approach to designing efficient and reliable controllers in Golog following two different perspectives on the environment where the control program is supposed to operate. According to one perspective, investigated in Chapter 4, the agent has a logical model of the world, but there is no probabilistic information about the environment where the agent is planning to act, and the agent is not capable or has no time for acquiring probabilities of different effects of its actions. In this case, the uncertainty and dynamics of the environment can be accounted only by observing the real outcomes of actions executed by the agent, by determining possible discrepancies between the observed outcomes and the effects expected according to the logical model of the world and then by recovering, if necessary, from the
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".