kemeny_young_ranker — Logtalk 3.100.1

# `kemeny_young_ranker`

Kemeny-Young pairwise preference ranker. It aggregates head-to-head preference weights, then performs an exact branch-and-bound search over linear orders to maximize the total pairwise agreement score under the Kemeny-Young criterion.

The library implements the `ranker_protocol` defined in the `ranking_protocols` library. It provides predicates for learning an exact consensus ranker from pairwise preferences, using it to order candidate items, inspecting the learned consensus ranking and consensus score, and exporting it as a list of predicate clauses or to a file.

Datasets are represented as objects implementing the `pairwise_ranking_dataset_protocol` protocol from the `ranking_protocols` library. See the `test_datasets` directory for examples. The current implementation requires a well-formed connected pairwise dataset so that all ranked items remain part of a single comparison graph.

## API documentation

Open the ../../apis/library_index.html#kemeny_young_ranker link in a web browser.

## Loading

To load this library, load the `loader.lgt` file:

    | ?- logtalk_load(kemeny_young_ranker(loader)).

## Testing

To test this library predicates, load the `tester.lgt` file:

    | ?- logtalk_load(kemeny_young_ranker(tester)).

## Features

- Exact Consensus Ranking: Learns one deterministic Kemeny-Young consensus order from aggregated pairwise outcomes.

- Branch-and-Bound Search: Uses exact search with pruning over linear orders to maximize total pairwise agreement.

- Deterministic Optimal Tie Breaking: Supports both standard term-order and dataset declaration-order selection when multiple optimal consensus rankings exist.

- Consensus Access: Exposes the learned full-item consensus ranking and its maximum agreement score using the `consensus_ranking/2` and `consensus_score/2` predicates.

- Deterministic Ranking: Orders candidate items by the selected consensus order with deterministic tie-breaking. When multiple optimal Kemeny orders exist, the `tie_breaking/1` option selects the deterministic search order used to choose one representative consensus ranking.

- Strict Dataset Validation: Rejects duplicate items, undeclared items, self-preferences, non-positive weights, and disconnected comparison graphs.

- Training Diagnostics: Learned rankers include dataset summary metadata, the effective tie-breaking mode, the selected consensus ranking, and the maximum agreement score.

- Ranker Export: Learned rankers can be exported as self-contained terms.

## Usage

### Learning a ranker

    % Learn from a pairwise ranking dataset object
    | ?- kemeny_young_ranker::learn(my_dataset, Ranker).
    ...

    % Learn with custom optimal-order tie breaking
    | ?- kemeny_young_ranker::learn(my_dataset, Ranker, [tie_breaking(declaration_order)]).
    ...

### Inspecting the learned consensus

    % Inspect the learned full-item consensus ranking
    | ?- kemeny_young_ranker::learn(my_dataset, Ranker),
         kemeny_young_ranker::consensus_ranking(Ranker, ConsensusRanking).
    ConsensusRanking = [...]
    ...

    % Inspect the maximum pairwise agreement score
    | ?- kemeny_young_ranker::learn(my_dataset, Ranker),
         kemeny_young_ranker::consensus_score(Ranker, ConsensusScore).
    ConsensusScore = ...
    ...

### Ranking candidate items

    % Rank a candidate set from most preferred to least preferred
    | ?- kemeny_young_ranker::learn(my_dataset, Ranker),
         kemeny_young_ranker::rank(Ranker, [item_a, item_b, item_c], Ranking).
    Ranking = [...]
    ...

Candidate lists must be proper lists of unique, ground items declared by the training dataset. Invalid ranker terms, duplicate candidates, and candidates containing variables are rejected with errors instead of being silently accepted.

## Options

The following options can be passed to the `learn/3` predicate:

- `tie_breaking(term_order)`: Select the lexicographically earliest optimal consensus ranking using the standard term order of the item identifiers.

- `tie_breaking(declaration_order)`: Select the earliest optimal consensus ranking using the dataset declaration order preserved by the pairwise dataset helpers.

The default is `tie_breaking(term_order)`.

## Scoring semantics

The learned `scores/2` values are reverse consensus positions. For a learned consensus order with `N` items, the first item receives score `N-1` and the last item receives score `0`.

This positional encoding is a deterministic surrogate used to satisfy the shared `ranker_protocol` scoring interface. The authoritative Kemeny-Young output is the full `consensus_ranking/2` order together with its `consensus_score/2` agreement value.

## Diagnostics syntax

The `diagnostics/2` predicate returns a list of metadata terms with the form:

    [
        model(kemeny_young_ranker),
        options(Options),
        consensus_ranking(ConsensusRanking),
        consensus_score(ConsensusScore),
        dataset_summary(DatasetSummary)
    ]

## Ranker representation

The learned ranker is represented by a compound term of the form:

    kemeny_young_ranker(Items, Scores, Diagnostics)

Where:

- `Items`: List of ranked items.

- `Scores`: List of `Item-Score` pairs.

- `Diagnostics`: List of metadata terms, including the effective options, selected consensus ranking, maximum agreement score, and dataset summary.

## Notes

Kemeny-Young optimization is NP-hard in general. This implementation uses an exact branch-and-bound search and is intended for small to moderate item sets.

## References

1. Kemeny, J. G. (1959). Mathematics without numbers.

2. Young, H. P. (1988). Condorcet’s theory of voting.