Specifying and verifying multiagent systems using the cognitive agents specification language (casl)
Bibliographic record
Abstract
In this thesis, we introduce a specification language (CASL) and verification environment (CASLve) for multiagent systems. We use the situation calculus [52] with Reiter's solution to the frame problem [62]—enhanced with predicates to describe agents' knowledge [64], beliefs, and goals—to formally, perspicuously, and systematically describe the effects of actions on the world and the mental states of agents. We add INFORM, REQUEST, and CANCELREQUEST actions to model inter-agent communication, and investigate properties of multiagent knowledge change and goal change, as well as belief change. We use the notation of the concurrent, logic programming language ConGolog [17] to specify the behaviour of agents. ConGolog has a formal semantics defined in the situation calculus, which facilitates the process of reasoning about the behaviour of individual agents and the system as a whole. We provide an environment for verifying properties of CASL specifications, by encoding the situation calculus, its extensions to handle mental states, and ConGolog in the PVS verification system [54], and proving lemmas which are useful for verifying CASL specifications. These include proving that bounded-loop ConGolog programs terminate, and providing a framework far compositional verification of ConGolog programs. We then specify three multiagent systems using CASL and prove some properties of the specifications.
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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 | 0.009 | 0.023 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".