Final answer:
Prolog is well-suited for problems requiring complex logical reasoning, such as developing expert systems for medical diagnosis. It is efficient due to features like backtracking and unification, which aid in systematic searching and pattern matching respectively.
Step-by-step explanation:
Logic programming languages like Prolog excel at solving computing problems that involve complex symbolic reasoning, knowledge representation, and problems with a well-defined set of rules or relations. A specific application where Prolog shines is in the development of expert systems, which are programs designed to mimic the decision-making capabilities of a human expert.
For instance, in medical diagnosis, an expert system can be used to infer diseases based on symptoms. Prolog's declarative nature means that we can define relationships between symptoms and diseases, and Prolog can reason through these to find possible conclusions. The language's computational model is based on formal logic, making it particularly effective for such applications where the domain knowledge can be encoded as logical rules and facts. Backtracking, a feature inherent in Prolog, allows for exploring different possibilities in a systematic way, making it ideal for tasks that require searching through large solution spaces.
Moreover, Prolog's built-in unification mechanism is excellent for pattern matching, which is requisite when assessing the fit of certain facts against established rules. These capabilities ensure Prolog's efficiency in scenarios that require a high level of logical reasoning and inference, which are common in artificial intelligence and similar fields.