A model of contingent planning for agent programming languages
Bibliographic record
Abstract
In this paper, we develop a formal model of planning for an agent that is operating in a dynamic and incompletely known environment. We assume that both the agent’s task and the behavior of the agents in the environment are expressed as high-level nondeterministic concurrent programs in some agent programming language (APL). In this context, planning must produce a deterministic conditional plan for the agent that can be successfully executed against all possible executions of the environment program. We handle actions with nondeterministic effects, as well as sensing actions, by treating them as actions that trigger an environmental reaction that is not under the planning agent’s control. Our model of contingent planning is specified for a generic APL with a transition semantics. Within this model, we devise a general procedure for computing the contingent plans. We also show how the model can be instantiated in the situation calculus with programs for the agent and the environment expressed in ConGolog, and we describe an implementation of the planning mechanism in this case.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".