On the Development of Tools for Modelling Dynamic Beliefs Based on Past Data
Bibliographic record
Abstract
In order to develop effective ubiquitous computing systems, we often need to predict an agent's behaviour based on past data. One way to do this is to maintain a model of what the agent believes at any point in time, as well as a mechanism for changing the beliefs to incorporate new information. In the knowledge representation community, this process is captured through formal belief revision operators. In this paper, we assume that we are monitoring the behaviour of an agent that uses a belief revision operator to incorporate new information; but we do not know exactly which operator is being used. Given past data about the beliefs of the agent, we propose two approaches for predicting future changes in belief. In the first approach, we simply search for all revision operators consistent with the data. In the second approach, we use machine learning to predict if a certain formula will be believed based on past data. We describe work in progress on prototype software to experiment with both approaches, and discuss when each is appropriate. We argue that modelling the dynamic beliefs of an agent in this way can be a useful component of a software system tasked with predicting behaviour when new information is received.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".