Bibliographic record
Abstract
Reasoning about knowledge has been a central issue in epistemology since Plato defined knowledge as justified true belief.In the twentieth century, the discussion was renewed by the use of formal logic and modal operators in Hintikka's epistemic logic.This logic has found applications in computer science and economics, but has defects: it is mono-modal, static and has no sense of resources.In this thesis we present a logic to reason about knowledge and the change induced to it as a result of communication actions between agents in a multi-agent systems.The semantics of this logic is an algebra of propositions paired with an algebra of actions.Both have structure preserving appearance maps whose adjoints stand for knowledge of agents.The algebra of actions is a quantale, thus actions are treated as the qualitative resources of Linear Logic: they are not accessible to all agents to acquire new information.Agents themselves act as qualitative resources to other agents: their nested appearances of a context has an effect in the reasoning of other agents.We also present a sequent calculus for our semantics, in the style of Lambek Calculus and Non-commutative Intuitionistic Linear Logic.We prove the soundness and completeness of this sequent calculus with regard to the algebra and apply the setting to reason about safety of security protocols.We connect our approach to the existing literature by showing that models of dynamic epistemic logic of Baltag-Moss-Solecki are instances of our logic.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".