The cognitive coherence approach for agent communication pragmatics
Bibliographic record
Abstract
Different approaches have investigated the syntax and semantic of agent communication. However, all these approaches (including : agent communication languages, conversation policies and dialogue games) have not indicated how agents should dynamically use communications. In fact, most of these approaches have mainly focused on of dialogues even though developers are more interested in agents' capabilities of having useful conversations in respect to their goals rather than in their abilities to structure dialogues. This leads us to propose a theory of use of conversations between agents. This pragmatic theory extends and adapts the cognitive dissonance theory (a major theory of social psychology) to multi-agent systems. In this paper, we show how this theory allows us to provide generic conceptual tools for the automation of both agent communicational behavior and attitude change processes. The cognitive coherence that we propose is formulated in terms of constraints and elements of cognition and allows us to define cognitive incoherences and dialogue utility measures. We show how these measures could be used to solve common problems and answer some critical questions concerning agent communication frameworks use. Finally, the theory is illustrated with an example of dialogue games automatic use.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".