Bibliographic record
Abstract
Research in AI field of Cognitive Robotics is concerned with integrating reasoning, perception, and action within a uniform theoretical and implementation framework. With the flourishing of many formal and powerful formalisms for reasoning about action and change, the next step has been to develop precise specifications of rational agent behaviour and agent deliberation---a formal account of agent activity will make it possible to validate the actual software implementing the behaviour; it will allow us to prove properties of such behaviour; and, ultimately, it will help us understand the whole problem of acting intelligently. This thesis gives a logical foundation for an interleaved agent architecture of perception, deliberation, and execution in the context of the situation calculus, one of the most widely used logical formalism for reasoning about dynamical systems. Special emphasis is placed on the deliberation part and its role within the general framework. We first investigate the expressive power of basic action theories, a well-known axiomatization of the dynamics of the world in the situation calculus, and show that it can accommodate incomplete causal laws and a passive account of sensing. We then pursue an investigation of plan executability/adequacy that is related to both planning and agent programming languages. After that, we develop an integrated architecture of agency where high-level deliberation is just one module. We propose two new planning modules and explain how deliberated plans can be monitored and replanned while executing within the architecture. Finally, we investigate a semantic characterization of the process of deliberation by capturing the class of simple epistemically feasible plans, defining planning modules in terms of these plans, and singling out syntactic sufficient conditions.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| 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".