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Record W2265087217

A knowledge-level approach for effective acting, sensing, and planning

2006· article· en· W2265087217 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTSpace · 2006
Typearticle
Languageen
FieldComputer Science
TopicLogic, Reasoning, and Knowledge
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceCorrectnessKnowledge representation and reasoningConstruct (python library)Action (physics)PlannerSituation calculusSet (abstract data type)Theoretical computer scienceArtificial intelligenceProgramming language
DOInot available

Abstract

fetched live from OpenAlex

In this thesis we investigate a "knowledge-level" approach to the problem of modelling an agent's incomplete knowledge, for the purpose of planning or high-level agent control. We investigate two formal accounts of knowledge, action, and sensing in the situation calculus: the Scherl and Levesque ( SL) approach that is based on "possible worlds," and the Demolombe and Pozos Parra (DP) approach that utilizes a set of "knowledge fluents." While the SL approach is expressive, reasoning is computationally more expensive; the DP account treats knowledge change as ordinary fluent change, but restricts its representation to primitive knowledge-level assertions. To relate these two accounts we construct "combined action theories," and prove that a set of primitive knowledge assertions remains identical in both accounts after any sequence of actions. We also extend this equivalence to more complex formulae. These results allow us to compile an expressive class of SL theories into equivalent DP theories that avoid the computational drawbacks of possible world reasoning. Moreover, this correspondence gives us a correctness result for the DP treatment of knowledge and action, in terms of possible worlds. We also describe a new conditional planner called PKS (Planning with Knowledge and Sensing), that works directly at the knowledge level to construct plans with incomplete information and sensing actions. PKS represents it knowledge by using a collection of databases, each of which models a particular type of knowledge. The contents of each database have a fixed, formal translation to a modal logic of knowledge that defines the planner's knowledge state. Actions are modelled as updates to the databases (i.e., the knowledge state), rather than the world state, which differs from other planners. This representation supports features, like functions, that world-level planners often have difficulty working with. We also describe a preliminary procedure for automatically converting DP actions into PKS actions. Together with our SL equivalence results, this transformation provides an important first step towards the goal of compiling world-level actions into equivalent knowledge-level actions usable by PKS. Finally, we demonstrate PKS's expressiveness and efficiency with a series of planning problems that also illustrate the potential of the knowledge-based approach.

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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: Methods · Consensus signal: none
Teacher disagreement score0.872
Threshold uncertainty score0.634

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 head0.315
Teacher spread0.276 · 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