Bibliographic record
Abstract
This dissertation proposes two novel approaches for extending AgentSpeak with qualitative uncertainty reasoning by integrating two dynamic variants of epistemic logic. These extensions address a crucial gap in the literature where qualitative approaches to uncertainty are seldom integrated into agent-oriented programming languages due to various challenges related to methodology, implementation, and computational complexity. The extensions provide various symbolic constructs that enable the modelling and reasoning of belief uncertainty. The significance of qualitative uncertainty reasoning is illustrated through a simple Minesweeper scenario and two complex uncertainty challenges from the 2019 Multi-Agent Programming Contest: uncertain navigation and agent identification. Given the ability to express qualitative uncertainty, we equip the agent with a more robust and effective way to plan and act under uncertainty. An in-depth evaluation of the performance and scalability of the proposed AgentSpeak extensions is provided, with a heavy focus on examining their impact on the agent’s time-sensitive reasoning cycle. The results show that these extensions provide a tractable and computationally feasible approach to extending AgentSpeak with the ability to manage the statics and dynamics of uncertainty.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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 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".