view release on metacpan or  search on metacpan

lib/AI/Pathfinding/OptimizeMultiple.pm  view on Meta::CPAN

            {
                first_search => $first_search_pdl,
                second_search => $second_search_pdl,
            },
            quotas => [400, 300, 200],
            selected_scans =>
            [
                AI::Pathfinding::OptimizeMultiple::Scan->new(
                    id => 'first_search',
                    cmd_line => "--preset first_search",
                ),
                AI::Pathfinding::OptimizeMultiple::Scan->new(
                    id => 'second_search',
                    cmd_line => "--preset second_search",
                ),
                AI::Pathfinding::OptimizeMultiple::Scan->new(
                    id => 'third_search',
                    cmd_line => "--preset third_search",
                ),
            ],
        }
    );

    $obj->calc_meta_scan();

    foreach my $scan_alloc (@{$self->chosen_scans()})
    {
        printf "Run %s for %d iterations.\n",
            $scans[$scan_alloc->scan_idx], $scan_alloc->iters;
    }

=head1 DESCRIPTION

This CPAN distribution implements the algorithm described here:

=over 4

=item * L<https://groups.google.com/group/comp.ai.games/msg/41e899e9beea5583?dmode=source&output=gplain&noredirect>

=item * L<http://www.shlomifish.org/lecture/Perl/Lightning/Opt-Multi-Task-in-PDL/>

=back

Given statistics on the performance of several game AI searches (or scans)
across a representative number of initial cases, find a scan
that solves most deals with close-to-optimal performance, by using switch
tasking.

=head1 SUBROUTINES/METHODS

=head2 my $chosen_scans_array_ref = $self->chosen_scans()

Returns the scans that have been chosen to perform the iteration. Each one is
a AI::Pathfinding::OptimizeMultiple::ScanRun object.

=head2 $calc_meta_scan->calc_meta_scan()

Calculates the meta-scan after initialisation. See here for the details
of the algorithm:

L<http://www.shlomifish.org/lecture/Freecell-Solver/The-Next-Pres/slides/multi-tasking/best-meta-scan/>

=head2 $self->calc_flares_meta_scan()

This function calculates the flares meta-scan: i.e: assuming that all atomic
scans are run one after the other and the shortest solutions of all
successful scans are being picked.

=head2 $calc_meta_scan->calc_board_iters($board_idx)

Calculates the iterations of the board $board_idx in all the scans.

Returns a hash_ref containing the key 'per_scan_iters' for the iterations
per scan, and 'board_iters' for the total board iterations when ran in the
scans.

=head2 my $status = $calc_meta_scan->get_final_status()

Returns the status as string:

=over 4

=item * "solved_all"

=item * "iterating"

=item * "out_of_quotas"

=back

=head2 my $sim_results_obj = $calc_meta_scan->simulate_board($board_idx, $args)

Simulates the board No $board_idx through the scan. Returns a
L<AI::Pathfinding::OptimizeMultiple::SimulationResults> object.

$args is an optional hash reference. It may contain a value with the key of
C<'chosen_scans'> that may specify an alternative scans to traverse.

=head2 my $n = $calc_meta_scan->get_total_iters()

Returns the total iterations count so far.

=head2 BUILD()

Moo leftover. B<INTERNAL USE>.

=head1 SEE ALSO

=over 4

=item * L<Freecell Solver|http://fc-solve.shlomifish.org/>

For which this code was first written and used.

=item * L<Alternative Implementation in C#/.NET|https://bitbucket.org/shlomif/fc-solve/src/cc5b428ed9bad0132d7a7bc1a14fc6d3650edf45/fc-solve/presets/soft-threads/meta-moves/auto-gen/optimize-seq?at=master>

An Alternative implementation in C#/.NET, which was written because the
performance of the Perl/PDL code was too slow.

=item * L<PDL> - Perl Data Language