Abductive reasoning is a form of logical inference primarily employed for forming hypotheses. It starts with an observation and then seeks the simplest or most likely explanation for that observation.
Imagine you wake up to find your garden wet. You didn’t see it raining but you think it must have rained while you were asleep, because that’s the simplest and most likely cause. This thought process is “Abductive Reasoning”.
Abductive reasoning, sometimes referred to as “inference to the best explanation”, represents a type of logical inference that is distinguished principally by its goal, which is to find the best or most likely explanation for a certain set of observations. It contrasts with other forms of reasoning like deductive reasoning (which proceeds from general principles to specific instances) and inductive reasoning (which moves from particular instances to general principles).
In computer science and artificial intelligence (AI), abductive reasoning is often used to help systems make probability-based decisions. Consider an AI system designed to diagnose diseases. The system might observe a myriad of symptoms (e.g., a fever, cough, body aches), and then use abductive reasoning to infer the most likely disease causing these symptoms, such as flu.
However, abductive reasoning also presents challenges in AI, largely related to its licentiousness; there are often many possible explanations for a given observation, and it can be computationally expensive and complex to sift through these to find the “best” one. A prominent approach to implement abductive reasoning in AI is through Bayesian networks, probabilistic graphical models that combine aspects of graph theory and probability theory. These networks can compute the likelihood of different scenarios, enabling the AI to infer the most probable explanation.
While AI systems utilizing abductive reasoning can perform impressively on certain tasks, they also highlight some of the shortcomings of artificial cognition vis-a-vis human cognition. Human intuition serves as a powerful tool for reducing a problem’s complexity and curating the set of plausible explanations in a way that AI systems, constrained by their prescribed algorithms, are currently unable to replicate.
In summary, abductive reasoning in AI concerns the identification of the most probable explanation for a given observation or set of observations. It is a significant area of study both for its successes in enabling effective decision-making in AI, but also for the challenges it presents that reveal the current limits of artificial cognition.