add basic example
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Nicolas Sebastian Schuler
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Here's a **Prolog program** that represents the logical rules for classifying an animal as either a **cat** or a **dog**, based on the morphological features described in your detailed comparison. The program uses a set of predicates representing visual traits, and applies a decision framework to infer classification.
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This version is designed to work with facts (trait observations) and logical inference using rules.
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---
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### ✅ Prolog Program: Animal Classifier
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```prolog
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% -------------------------------
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% TRAIT PREDICATES
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% These represent observable features from an image.
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% Each predicate returns true if the trait is present in the animal.
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% -------------------------------
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% Skull traits
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is_short_rounded_skull :- true. % Assume this is observed (can be set based on image input)
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is_flat_face :- true.
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is_long_elongated_skull :- false.
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is_prominent_muzzle :- false.
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% Ear traits
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is_tall_pointed_ear :- true.
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is_high_set_ear :- true.
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is_floppy_or_rounded_ear :- false.
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is_wide_spaced_ear :- false.
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% Eye traits
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is_large_round_eye :- true.
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is_lateral_eye_position :- true.
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is_small_eye :- false.
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is_forward_facing_eye :- false.
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% Paw traits
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is_small_paw_with_visible_toes :- true.
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has_retractable_claws :- true.
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is_large_paw_with_non_retractable_claws :- false.
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% Tail traits
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is_long_thin_tail :- true.
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is_tail_held_high_or_curled :- true.
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is_thick_tail :- false.
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% Body posture traits
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is_low_to_ground_body :- true.
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is_compact_body :- true.
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is_robust_upright_body :- false.
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is_long_legs_relative_to_body :- false.
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% Muzzle traits (profile)
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has_visible_stop_in_profile :- false.
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has_overbite_or_underbite :- false.
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% -------------------------------
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% CLASSIFICATION RULES
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% These are based on the decision framework provided.
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% -------------------------------
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% Cat classification rules
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is_cat :-
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cat_traits_count(CatCount),
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dog_traits_count(DogCount),
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CatCount >= 3,
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DogCount =:= 0.
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% Dog classification rules
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is_dog :-
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cat_traits_count(CatCount),
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dog_traits_count(DogCount),
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DogCount >= 3,
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CatCount =:= 0.
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% Counting cat-specific traits
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cat_traits_count(Count) :-
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findall(Trait, cat_trait(Trait), Traits),
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length(Traits, Count).
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% Define which predicates are cat-specific traits
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cat_trait(is_short_rounded_skull).
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cat_trait(is_flat_face).
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cat_trait(is_tall_pointed_ear).
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cat_trait(is_high_set_ear).
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cat_trait(is_large_round_eye).
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cat_trait(is_lateral_eye_position).
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cat_trait(is_small_paw_with_visible_toes).
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cat_trait(has_retractable_claws).
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cat_trait(is_long_thin_tail).
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cat_trait(is_tail_held_high_or_curled).
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cat_trait(is_low_to_ground_body).
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cat_trait(is_compact_body).
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% Counting dog-specific traits
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dog_traits_count(Count) :-
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findall(Trait, dog_trait(Trait), Traits),
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length(Traits, Count).
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% Define which predicates are dog-specific traits
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dog_trait(is_long_elongated_skull).
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dog_trait(is_prominent_muzzle).
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dog_trait(is_floppy_or_rounded_ear).
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dog_trait(is_wide_spaced_ear).
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dog_trait(is_small_eye).
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dog_trait(is_forward_facing_eye).
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dog_trait(is_large_paw_with_non_retractable_claws).
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dog_trait(is_thick_tail).
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dog_trait(is_robust_upright_body).
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dog_trait(is_long_legs_relative_to_body).
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dog_trait(has_visible_stop_in_profile).
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dog_trait(has_overbite_or_underbite).
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% -------------------------------
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% SAMPLE QUERY
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% To test, you can run:
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% ?- is_cat.
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% ?- is_dog.
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% You may want to redefine facts like is_short_rounded_skull/0, etc., depending on image input.
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% -------------------------------
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```
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---
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### 🧠 How It Works
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- **Traits** are defined as simple predicates (e.g., `is_short_rounded_skull`, `is_large_round_eye`) that can be set to `true` or `false`.
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- The program counts how many traits match for cats and dogs.
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- If **three or more cat-specific traits** are present, and **no dog-specific traits**, it classifies as a **cat**.
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- Similarly, if three or more **dog-specific traits** are present, it classifies as a **dog**.
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---
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### 🧪 Example Queries
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```prolog
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?- is_cat. % Returns true if enough cat traits are set to true
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?- is_dog. % Returns true if enough dog traits are set to true
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```
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---
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### 🛠️ Extending for Image Input
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To use this with real image data (e.g., a computer vision pipeline), you would:
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- Set the facts dynamically from image analysis.
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- Use `assert/1` or `retract/1` to change trait values.
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- For example:
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```prolog
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?- assert(is_short_rounded_skull).
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?- is_cat.
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```
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Let me know if you want a version that integrates with actual image recognition libraries or uses probability-based reasoning!
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