demo completed
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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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% Observable biological traits for identifying cats and dogs
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% Based on static image analysis with ranked diagnostic reliability
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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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% Muzzle Structure
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short_muzzle.
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rounded_muzzle.
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distinct_stop.
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straight_nasal_bridge.
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narrow_nose.
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smooth_rounded_tip.
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nose_pad_below_eyes.
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pointed_ears.
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upright_ears.
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narrow_set_ears.
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vertical_slit_pupils.
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eyes_set_high_on_face.
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whiskers_extend_horizontally.
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narrow_vertically_aligned_pad.
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tail_tapers_to_fine_point.
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base_narrower_than_head_width.
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head_appears_large.
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slender_neck.
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compact_body.
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is_long_elongated_skull :- false.
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is_prominent_muzzle :- false.
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% Dog traits (secondary confirmation)
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long_muzzle.
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prominent_stop.
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convex_nasal_bridge.
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larger_nose.
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blunt_or_upturned_tip.
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nose_pad_prominent.
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variable_ear_shapes.
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wider_set_ears.
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round_pupils.
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eyes_level_with_nose_tip.
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whiskers_extend_forward_downward.
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broad_horizontally_oriented_pad.
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tail_blunt_or_bushy.
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base_wider_than_head_width.
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head_proportionally_smaller.
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thicker_neck.
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distinct_shoulder_hip_separation.
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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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% Decision rules for classification
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dog :- long_muzzle; prominent_stop; convex_nasal_bridge; larger_nose; blunt_or_upturned_tip; nose_pad_prominent; variable_ear_shapes; wider_set_ears; round_pupils; eyes_level_with_nose_tip; whiskers_extend_forward_downward; broad_horizontally_oriented_pad; tail_blunt_or_bushy; base_wider_than_head_width.
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cat :- short_muzzle; rounded_muzzle; distinct_stop; straight_nasal_bridge; narrow_nose; smooth_rounded_tip; nose_pad_below_eyes; pointed_ears; upright_ears; narrow_set_ears; vertical_slit_pupils; eyes_set_high_on_face; whiskers_extend_horizontally; narrow_vertically_aligned_pad; tail_tapers_to_fine_point; base_narrower_than_head_width.
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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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% Queries for classification
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query(dog).
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query(cat).
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```
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