Grounding and the expression of belief
Bibliographic record
Abstract
In this paper we investigate the logic of speech acts and groundedness. A piece of information is grounded for a group of agents if it is publicly expressed and established by all agents of the group. Our concept of groundedness is founded on the expression of the sincerity condition of speech act theory. We formalize groundedness within an extended BDI (Belief, Desire, Intention) logic where belief is viewed as a kind of group belief. We show that our logic permits to reconcile the mentalist approaches on the one hand, and the structural and social approaches on the other, which are the two rival research programs in the formalization of agent interaction. Although groundedness is thus linked to the standard mental attitude of belief, it is immune to the critiques that have been put forward against the mentalist approaches, viz. that they require too strong hypotheses about the agents ’ mental states such as sincerity and cooperation: just as the structural approaches, groundedness only bears on the public aspect of communication. In our extended BDI logic we study communication between heterogeneous agents. We characterize inform and request speech acts in terms of preconditions and effects. We demonstrate the power of our solution by means of two examples. First, we revisit the well-known FIPA Contract Net Protocol. As a second example, we show how Walton & Krabbe’s commitments can be redefined in term of groundedness.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".