On Ability to Autonomously Execute Agent Programs with Sensing — Extended Abstract
Bibliographic record
Abstract
There has been much work already on formal models of deliberation/planning under incomplete information, where an agent can perform sensing actions to acquire additional information. But most of it has been set in epistemic logicbased frameworks and is hard to relate to work on agent programming languages (e.g. 3APL, AgentSpeak(L)). Here, we develop new non-epistemic formalizations of deliberation that are much easier to relate to standard agent programming language semantics based on transition systems. When doing deliberation/planning under incomplete information, one typically searches over a set of states, each of which is associated with a knowledge base (KB) or theory that represents what is known in the state. To evaluate tests in the program and to determine what transitions/actions are possible, one looks at what is entailed by the current KB. To allow for future sensing results, one looks at which of these are consistent with the current KB. We call this type of approach to deliberation “entailment and consistencybased” (EC-based). In this paper, we argue that EC-based approaches do not always work, and propose an alternative. Our accounts are formalized within the situation calculus and use a simple programming language based on ConGolog to specify agent programs, but we claim that the results generalize to most proposed agent programming languages/frameworks. Our accounts rely on a semantics for online executions of programs with sensing. A configuration is a pair (δ, σ) involving a program δ and a history σ specifying the actions performed so far and the sensing results obtained. In the full paper, we define a transition relation for this, i.e. when a configuration (δ, σ) may evolve to configuration (δ ,σ ) w.r.t. a model M (relative to an underlying theory of action D). The definition requires that the theory D, augmented with the sensing results in σ, entail that the transition is possible. The model M is used to represent a possible environment and generate sensing results. We also define when a configuration is final, i.e. may legally terminate.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".