MétaCan
Menu
Back to cohort
Record W3022125085 · doi:10.5555/2615731.2615757

Progression and verification of situation calculus agents with bounded beliefs

2014· article· en· W3022125085 on OpenAlexaff
Giuseppe De Giacomo, Yves Lespérance, Fabio Patrizi, Stavros Vassos

Bibliographic record

VenueAdaptive Agents and Multi-Agents Systems · 2014
Typearticle
Languageen
FieldComputer Science
TopicLogic, Reasoning, and Knowledge
Canadian institutionsYork University
Fundersnot available
KeywordsSituation calculusDecidabilityBounded functionAction (physics)Computer scienceDomain (mathematical analysis)Object (grammar)Temporal logicBelief revisionCalculus (dental)Order (exchange)MathematicsTheoretical computer scienceDiscrete mathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper we investigate agents that have incomplete information and make decisions based on their beliefs, expressed as situation calculus bounded action theories. Such theories have an infinite object domain, but the number of objects that belong to fluents at each time point is bounded by a given constant. Recently it has been shown that verifying temporal properties over such theories is decidable. Here, we first show that we can actually check whether an arbitrary action theory maintains boundedness. Secondly, we examine progression. Progression can be thought of as capturing the notion of belief states resulting from actions in the situation calculus. In the general case, such belief states can be expressed only in second-order logic. Here, we show that for bounded action theories, progression, and hence belief states, can always be represented in first-order logic. Based on this result, we further prove decidability of temporal verification over online executions, i.e., those executions resulting from agents performing only actions that are feasible according to their beliefs.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.705
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.280
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueAdaptive Agents and Multi-Agents SystemsSame topicLogic, Reasoning, and KnowledgeFrench-language works237,207