Handling Prioritized Goals and Subgoals in a Logical Account of Goal Change (Extended Abstract)
Bibliographic record
Abstract
Most existing formal models of goals [2, 3] assume that all goals are equally important and many only deal with achievement goals. Moreover, they do not guarantee that an agent’s goals will properly evolve when an action/event occurs, e.g. when the agent’s beliefs/knowledge changes or a goal is adopted or dropped. Also, most of these frameworks do not model the dependencies between goals and the subgoals and plans adopted to achieve these goals – subgoals adopted to bring about a goal should be dropped when the parent goal becomes impossible, is achieved, or is dropped. Dealing with these issues is important for developing effective models of rational agency and BDI agent programming languages. Here, we outline a formal model of prioritized goals and their dynamics that addresses these issues. In our framework, an agent can have multiple goals at different priority levels, possibly inconsistent with each other. We define intentions as the maximal set of highest priority goals that is consistent given the agent’s knowledge. Our formalization of goal dynamics ensures that the agent strives to maximize her utility. Our model of goals supports the specification of general temporally extended goals, not just achievement goals, and also handles subgoals and their dynamics. Our base framework for modeling goal change is the situation calculus as formalized in [4]. We model knowledge using a possible worlds account adapted to the situation calculus [5]. To support modeling temporally extended goals, we introduce a new sort of paths, which are essentially infinite sequences of situations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".