A Multi-Agent Temporal Constraint Satisfaction System Based on Allen's Interval Algebra and Probabilities
Bibliographic record
Abstract
Many real-world problems can be viewed and represented as a constraint satisfaction problem (CSP). In addition, many of these problems are distributed in nature. To this end, we combine agents with a special type of CSP called an Interval Algebra network (IA network). An IA network is a graph where each node represents an interval. Directed edges in the network are labelled with temporal interval relations. A probabilistic IA network has probabilities associated with the relations on the edges that can be used to capture preferences. A probabilistic IA agent (PIA-Agent) is assigned a probabilistic IA network. PIA-Agent’s networks are connected via edges. The overall goal is to make each PIA-Agent’s network consistent and optimal. Each PIA-Agent is independent and has sole control over its network. But, it must communicate and coordinate with other PIA-Agents when modifying or updating edges that are shared between two PIA-Agents. We present an algorithm which allows the PIA-Agents to collaboratively solve and recommend a temporal schedule. At the agent level, this schedule is optimal under the given local constraints. Although the global solution may not be optimal, we try to generate near optimal ones. Note that our distributed system is not centrally controlled. Our algorithm decides which PIA-Agent should be given an opportunity to update the solution next. Also, when a conflict is detected, the algorithm modifies the PIA-Agent execution order in order to deal with the inconsistency. Request access from your librarian to read this chapter's full text.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".