Intelligent Systems - Reasoning Types

Intelligent Systems - Reasoning Representation

• Reasoning is the ability to assess things rationally using logic based on existing or new information when making decisions or solving problems.
• Reasoning helps weigh advantages/disadvantages of different courses of action, solve problems, handle uncertainty, verify claims, and assess situations carefully for best outcomes.
• Reasoning comes in various forms, from everyday decisions to complex AI algorithms.
• Reasoning can be categorized into seven basic types.
• Understanding reasoning types helps evaluate and improve decision-making.

Types of Reasoning

• Deductive reasoning: Uses formal logic and observations to prove theories/hypotheses. Starts with an assumption and uses observations to validate/refute it. Deductive reasoning often leads to logically certain results when applied correctly.

  • Example: A marketing team observes young parents are their largest customer demographic and allocates more budget to social media to target that group.

• Inductive reasoning: Uses theories and assumptions to validate observations. Works from specific cases to a general rule. This type of reasoning isn't always certain as it makes generalizations.

  • Example: A kindergarten teacher notices better attention spans after adding a short activity break and decides to continue the break due to the improved classroom behavior.

• Analogical reasoning: Finds similarities between things to identify other similar qualities. Leveraging patterns and associations to aid understanding and expand knowledge.

  • Example: Businesses use a supermarket analogy to plan for a new product line, evaluating how to provide customers everything they need.

• Abductive reasoning: Uses observations to reach a logical conclusion, often making educated guesses in the face of uncertainty.

  • Example: Salesperson receives a short message from a client about an issue. The salesperson, through limited information, must narrow down possible concerns related to the message.

• Cause-and-effect reasoning: Shows the link between two events. Used to explain why things happen when certain conditions are present. Often used in everyday decisions and by businesses and professionals in prediction analysis.

  • Example: A marketing agency shows how increased advertising budget in year one led to a 10% increase in sales. This points to effectiveness, and they can use similar reasoning to request future budget increases.

• Critical thinking: Involves extensive, rational thought on a specific subject to reach a definitive conclusion

  • Useful in many fields including computing, engineering, social sciences, and logic
  • Used for evaluating authenticity of different things like art, film, etc.
  • A crucial part of problem-solving, including troubleshooting
  • Important for understanding complexities or "gray areas" where definitive answers are hard to find
  • Example: A restaurant manager learns a key bakery supplier is going on strike. This leads to purchasing additional prepared goods and finding an alternate supplier.

• Decompositional reasoning: Breaks down complex things into smaller parts to understand each part's function and interaction contributing to the whole.

  • Used in many areas from managing projects to product development, engineering, science and others