What is abductive logic programming?

Abductive logic programming (ALP) is a computational framework that combines traditional logic programming with abductive reasoning—a form of logical inference focused on generating plausible hypotheses to explain observations. In ALP, a logic program (often written in a language like Prolog) is augmented with a set of “abducible” predicates, which are assumptions the system can make to fill gaps in knowledge. When faced with an observation that cannot be directly derived from existing rules and facts, ALP searches for minimal sets of abducible assumptions that, when added to the program, logically entail the observation. This approach allows reasoning under uncertainty or incomplete information.

Technically, ALP works by extending a logic program with a predefined set of abducibles—predicates that lack explicit definitions but can be hypothesized. For example, consider a diagnostic system where the observation is “engine won’t start.” The logic program might include rules like “engine_starts if fuel_pump_works and spark_plug_functions.” If neither “fuel_pump_works” nor “spark_plug_functions” can be proven, ALP can abduce either (or both) as potential hypotheses. Integrity constraints, such as “if fuel_pump_works, then fuel_gauge_not_empty,” help prune invalid assumptions. The solver then checks which combinations of abducibles satisfy both the observation and constraints, yielding possible explanations like “fuel_pump_fails” or “spark_plug_fails.”

ALP is particularly useful in domains requiring hypothetical reasoning. For instance, in medical diagnosis, it can propose diseases that explain symptoms when test results are missing. In robotics, it might infer unseen obstacles to explain sensor discrepancies. A key advantage is its ability to integrate structured logic with dynamic hypothesis generation, enabling systems to adapt to incomplete data. Tools like Prolog-based frameworks (e.g., XSB-Prolog) and dedicated ALP systems provide practical implementations, allowing developers to model problems declaratively while leveraging abduction for flexible problem-solving.