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Record W2237745239

Modeling and Verifying Probabilistic Social Commitments in Multi-Agent Systems

2015· dissertation· en· W2237745239 on OpenAlexfundno aff
Khalid Sultan

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2015
Typedissertation
Languageen
FieldComputer Science
TopicMulti-Agent Systems and Negotiation
Canadian institutionsnot available
FundersConcordia University
KeywordsProbabilistic logicCorrectnessComputer scienceModel checkingProbabilistic CTLMulti-agent systemVerifiable secret sharingContext (archaeology)Theoretical computer scienceArtificial intelligenceProbabilistic analysis of algorithmsAlgorithmProgramming language
DOInot available

Abstract

fetched live from OpenAlex

Interaction among autonomous agents in Multi-Agent Systems (MASs) is the key aspect for solving complex problems that an individual agent cannot handle alone. In this context, social approaches, as opposed to the mental approaches, have recently received a considerable attention in the area of agent communication. They exploit observable social \ncommitments to develop a verifiable formal semantics by which communication protocols can be specified. However, existing approaches for defining social commitments tend to \nassume an absolute guarantee of correctness so that systems run in a certain manner. That is, social commitments have always been modeled with the assumption of certainty. Moreover, the widespread use of MASs increases the interest to explore the interactions between different aspects of the participating agents such as the interaction between agents’ knowledge and social commitments in the presence of uncertainty. This results in having a gap, in the literature of agent communication, on modeling and verifying social commitments in probabilistic settings. \n \nIn this thesis, we aim to address the above-mentioned problems by presenting a practical formal framework that is capable of handling the problem of uncertainty in social \ncommitments. First, we develop an approach for representing, reasoning about, and verifying \nprobabilistic social commitments in MASs. This includes defining a new logic called the probabilistic logic of commitments (PCTLC), and a reduction-based model checking \nprocedure for verifying the proposed logic. In the reduction technique, the problem of model checking PCTLC is transformed into the problem of model checking PCTL so that \nthe use of the PRISM (Probabilistic Symbolic Model Checker) is made possible. Formulae of PCTLC are interpreted over an extended version of the probabilistic interpreted systems \nformalism. Second, we extend the work we proposed for probabilistic social commitments to be able to capture and verify the interactions between knowledge and commitments. \nProperties representing the interactions between the two aspects are expressed in a new developed logic called the probabilistic logic of knowledge and commitment (PCTLkc). \nThird, we develop an adequate semantics for the group social commitments, for the first time in the literature, and integrate it into the framework. We then introduce an improved version of PCTLkc and extend it with operators for the group knowledge and group social commitments. The new refined logic is called PCTLkc+. In each of the latter stages, we respectively develop a new version of the probabilistic interpreted systems over which the \npresented logic is interpreted, and introduce a new reduction-based verification technique to verify the proposed logic. To evaluate our proposed work, we implement the proposed verification techniques on top of the PRISM model checker and apply them on several case studies. The results demonstrate the usefulness and effectiveness of our proposed work.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0040.005
Open science0.0030.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.101
GPT teacher head0.329
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2015
Admission routes1
Has abstractyes

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