Mnemonic: DIAACMF - Dragons In Armor Always Catch Magical Fish.

Deductive reasoning

It follows a top-down approach where conclusions are drawn from general principles or premises that are known or assumed to be true. This form of reasoning relies on established facts to infer valid conclusions.
Example: If all humans are mortal, and Socrates is a human, then Socrates is mortal.
Application in AI: Deductive reasoning is often used in expert systems and rule-based AI systems, where knowledge is represented through rules (if-then statements). These systems apply general rules to specific problems to derive solutions or make decisions.

It is a bottom up approach that involves drawing general conclusions from specific instances or observations. Unlike deductive reasoning, inductive reasoning generates hypotheses rather than certain conclusions, making it more probabilistic.
Example: If we observe that the sun rises in the east every day, we may infer that the sun will rise in the east tomorrow.
Application in AI: Inductive reasoning is widely used in machine learning algorithms. Models trained on data patterns generalize from the data and use this information to make predictions about new, unseen data.

It starts with an incomplete set of observations and then seeks the most plausible explanation. It focuses on finding the most likely conclusion based on what is known, rather than seeking an absolute truth.
Example: If a patient has a fever and cough, a doctor might hypothesize that they have the flu, even though other illnesses could cause similar symptoms.
Application in AI: Abductive reasoning is used in diagnostic systems, such as medical diagnosis tools or fault detection systems, where the goal is to identify the most probable cause of a problem given incomplete data.

It compares two situations that share similarities, using knowledge from one domain to solve problems in another. This reasoning allows AI systems to draw parallels between similar scenarios.
Example: If flying a drone is similar to piloting a helicopter, knowledge gained from helicopter control can be applied to drones.
Application in AI: Analogical reasoning is useful in AI for problem-solving, decision-making, and knowledge transfer, especially in robotics and cognitive systems.

It relies on everyday knowledge and experiences to draw conclusions. It simulates how humans use common sense to handle day-to-day situations, which is often challenging for AI due to the implicit nature of this knowledge.
Example: If it rains, we can expect the ground to get wet, even without explicitly stating it.
Application in AI: AI systems like conversational agents (e.g., Siri, Alexa) are being developed to incorporate common sense reasoning to handle more natural and complex user interactions effectively.

It refers to a form of reasoning where conclusions, once drawn, cannot be reversed, even if new information becomes available. This ensures that conclusions remain consistent regardless of updates to the knowledge base.
Example: The statement “The Sahara is a desert” remains true even if more information about the world’s deserts is introduced.
Application in AI: Monotonic reasoning is applied in conventional reasoning systems and logic-based AI, where consistency is critical. Systems like formal verification tools rely on this type of reasoning to ensure that conclusions do not change over time.

In contrast to monotonic reasoning, non-monotonic reasoning allows AI systems to revise conclusions when new information becomes available. This is especially useful in dynamic environments where the knowledge base is continuously updated.
Example: Initially concluding that all birds can fly, but revising this conclusion upon learning about penguins, which cannot fly.
Application in AI: Nonmonotonic reasoning is used in AI for dynamic decision-making systems that adapt to changing environments or new information, such as real-time traffic management or adaptive learning systems.

It handles uncertainty and imprecision by allowing degrees of truth rather than binary true/false outcomes. This makes it well-suited for real-world scenarios where data can be ambiguous or incomplete.
Example: In human language, statements like “It is warm outside” are vague. Fuzzy reasoning might assign a degree of truth, such as 0.7 warm, rather than strictly true or false.
Application in AI: Fuzzy reasoning is widely applied in control systems, such as temperature regulation in air conditioners, washing machines, and autonomous vehicle systems, where precise measurements are not always available.