Algebraic Model for Agent Explicit Knowledge in Multi-agent Systems
Bibliographic record
Abstract
In this chapter, we present a structure to specify agent explicit knowledge based on information algebra. We define in the context of agent knowledge the combining, marginalizing, and labelling operators. Also, we define remove and frame substitution operator. These operators are all what is needed to express operations on agent explicit knowledge. We also define a set of frames to be associated with information. Then, we prove that our structure is an information algebra which links our work to a rich heritage of mathematical theories. Our mathematical structure is expressive as it allows combining information for different purposes regardless of their frames, extracting a part of information, or associating information with a frame. We give two applications of the proposed structure. First, we apply it to the specification and analysis of agent knowledge in cryptographic protocols. In the literature of cryptographic protocols, operators are usually defined on information that belongs to a specific type, while our structure enables a uniform and a general way to handle information. Also, defining a relation between frames and linking them to the operators applied on information is not addressed in the literature. Furthermore, different protocol-dependent structures should be defined to relate different kinds of information which are not needed in our representation. Second, we show its use in the analysis of information flow between agents in multi-agent systems. Our structure provides a comprehensive language to specify agents knowledge and confidentiality policies. For example, it allows specifying and reasoning on composite information flow. Also, it allows specifying policies similar those articulated within Bell-LaPadula and Chinese Wall models.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".