Inductive Logic Programming (ILP) sits at the intersection of machine learning and logic. Instead of learning patterns purely as numbers in vectors, ILP learns human-readable rules expressed in logic, often using first-order predicates. The goal is straightforward: given specific observations (examples) and background knowledge, derive general rules that explain those examples. One of the classic mechanisms behind ILP is inverse resolution, a method that “reverses” deductive reasoning to propose hypotheses. For learners exploring symbolic learning alongside modern AI systems, the topic is increasingly relevant in an artificial intelligence course in Delhi, especially when discussing explainability, reasoning, and knowledge-driven modelling.
What ILP Tries to Achieve
In normal deduction, you start with general rules and derive specific conclusions. ILP flips the direction. You start with examples—facts about the world—and try to infer general rules that could produce them.
A simple ILP setup includes:
- Positive examples: facts we want the learned rules to entail (e.g., parent(alex, sam)).
- Negative examples: facts the rules should not entail (to avoid overgeneralisation).
- Background knowledge: known facts and rules that provide context (e.g., “if someone is a mother, they are a parent”).
The output is a hypothesis: a set of logic clauses that explain the positives while excluding the negatives. This ability to produce interpretable rules is a key reason ILP remains useful in domains like compliance, healthcare reasoning, fraud detection with business constraints, and knowledge graph enrichment—topics often introduced in an artificial intelligence course in Delhi to balance deep learning with symbolic approaches.
ILP Primitives: The Building Blocks of Rule Learning
In ILP, “primitives” are the basic representational and operational pieces used to build candidate rules. They control what the system is allowed to express and how it searches.
Predicates and terms
- Predicates represent relations or properties, such as likes(person, item) or connected(node1, node2).
- Constants represent specific entities (e.g., delhi, alice).
- Variables allow generalisation (e.g., X, Y).
Without variables, ILP would only store facts. Variables are what enable rule learning.
Literals and clauses
A literal is a predicate statement (possibly negated). A clause (often a Horn clause) is a rule like:
target(X) :- condition1(X), condition2(X, Y).
This reads: X is a target if conditions hold. Clauses are the main form of learned knowledge.
Mode declarations and language bias
ILP systems usually enforce a language bias—constraints that limit the search space. Examples include:
- Which predicates can appear in rule heads or bodies
- Allowed argument types (person, transaction, product)
- Maximum clause length or depth
Language bias is not a limitation; it is what makes learning feasible and keeps results meaningful.
Inverse Resolution: Learning by Reversing Deduction
Resolution is a classic proof method in logic: it combines clauses to derive conclusions. Inverse resolution attempts to reverse this process to generate candidate hypotheses.
At a high level:
- Deduction: Rules + facts → derived example
- Inverse resolution: Example + background knowledge → possible rules
Imagine you observe that grandparent(alex, jordan) is true. If your background knowledge already includes facts like parent(alex, sam) and parent(sam, jordan), then a plausible general rule is:
grandparent(X, Z) :- parent(X, Y), parent(Y, Z).
Inverse resolution helps propose such rule structures systematically. It does this by identifying what intermediate statements (often called “missing links”) must exist for the example to be derivable and then turning those links into candidate rule bodies.
This is one reason ILP is valuable for explainable AI: the hypothesis is not a black box; it is a set of statements you can inspect, test, and refine. In practice, many programmes in an artificial intelligence course in Delhi will contrast this with statistical models and show how each approach fits different problem contexts.
Generalisation, Specialisation, and Search Control
Learning rules is not just about producing a rule; it is about producing a rule that is correct, not too narrow, and not too broad.
Overly specific rules
A rule that only explains one example is usually not useful. For example, hard-coding constants into the rule body makes it fit a single case but fail on new data.
Overly general rules
A rule that explains positives but also covers many negatives is dangerous. This is why ILP relies on:
- Coverage tests: how many positives a rule explains
- Consistency checks: whether it avoids negatives
- Scoring functions: balancing simplicity, coverage, and correctness
Search is usually guided by heuristics and bounded by language bias. Many systems also use refinement operators:
- Generalisation: dropping conditions or replacing constants with variables
- Specialisation: adding conditions to reduce false positives
Inverse resolution participates in this search by proposing hypotheses that are consistent with how deduction would work, which can be more principled than random rule generation.
Where ILP Still Matters Today
ILP becomes especially useful when you need:
- Interpretability: rules that domain experts can validate
- Structured relational data: graphs, hierarchies, linked entities
- Strong constraints: business logic, policies, or scientific rules
- Data efficiency: learning from fewer examples using background knowledge
Even when teams deploy neural models, ILP can support them by generating rules for validation, building symbolic constraints, or providing explanations for decisions. These hybrid perspectives are now common in advanced discussions within an artificial intelligence course in Delhi.
Conclusion
Inductive Logic Programming uses logic primitives—predicates, variables, clauses, and language bias—to learn general rules from specific observations. Inverse resolution is a key idea that reverses deductive reasoning to propose hypotheses that could explain the examples under given background knowledge. While not a replacement for modern statistical learning, ILP remains a strong approach when interpretability, relational structure, and rule-based reasoning matter. For learners developing a balanced foundation through an artificial intelligence course in Delhi, understanding ILP primitives and inverse resolution offers a clear view of how machines can learn structured knowledge—not just correlations.
