Decision making in large-scale domains: a case study
Bibliographic record
Abstract
Planning under uncertainty attracted significant attention in AI and in other fields. To overcome computational problems associated with Markov Decision Processes (MDPs) in large scale domains researchers often take advantage of structural properties and look for approximately optimal solutions. DTGolog, a decision-theoretic agent programming language based on the situation calculus, was proposed to ease some of the computational difficulties by using natural ordering constraints on execution of actions. Using DTGolog, domain specific constraints on the set of available policies can be expressed in a high-level program and this program helps to reduce significantly computation required to find a policy optimal in this set. Our paper explores whether the DTGolog framework can be used to evaluate different designs of a decision making agent in a large real-world domain. Each design is understood as combination of a template (expressed as a Golog program) for available policies and a reward function. To evaluate and compare alternative designs we estimate the probability of goal satisfaction for each design. As a domain, we choose the London Ambulance Service (LAS) case study that is well known in software engineering, but remains unknown in AI. In our paper we demonstrate that DTGolog can be applied successfully to quantitative evaluation of alternative designs in terms of their ability to satisfy a system goal with a high probability. We provide a detailed axiomatization of the domain in the temporal situation calculus with stochastic actions. The main advantage of this representation is that neither actions, not states require explicit enumeration. We do an experimental analysis using an on-line implementation of DTGolog coupled with a simulator that models real time actions of many external agents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".