6. AI Reasoning Applications
The process of applying probabilistic or logical methods to infer conclusions from known facts or evidence is known as reasoning in artificial intelligence. Depending on their objectives and characteristics, various applications employ various forms of reasoning. Let’s analyze it:
| Application Area | Type of Reasoning Used |
|---|---|
| Expert Systems | Deductive, Rule-Based |
| Diagnosis Systems | Abductive, Probabilistic |
| Robotics | Deductive, Planning, Causal |
| Natural Language Processing | Logical & Statistical |
| Machine Learning | Inductive |
| Game AI | Deductive, Probabilistic, Planning |
1. Expert Systems
Used Reasoning: Deductive, Rule-Based
- What are Expert Systems?
These are computer programs that mimic a human expert’s (such as an engineer or doctor) decision-making process. - Deductive Reasoning:
Starts with general rules and applies them to specific cases.
E.g.,- Rule: If temperature > 100°C → Overheat
- Fact: Temperature = 110°C
- Deduction: Overheat = TRUE
- Rule-Based Reasoning:
Uses “IF-THEN” rules to make decisions. - Example:
In a medical expert system:- IF symptoms include fever and sore throat
- THEN diagnosis might be strep throat
2. Diagnosis Systems
Used Reasoning: Abductive, Probabilistic
- What are Diagnosis Systems?
These systems identify possible causes for observed effects (used in medical, mechanical, or network diagnosis). - Abductive Reasoning:
Seeks to determine the most likely explanation for the available data.
E.g.,- Observation: Patient has fever and rash
- Abductive inference: Most likely cause is measles
- Probabilistic Reasoning:
Uses historical data to assign probabilities to potential explanations.
Makes use of Probabilistic Graphic Models or Bayesian networks. - Example:
Given symptoms and test results, calculate:
P(Disease | Symptoms, Test Positive)
3. Robotics
Deductive, planning, and causal reasoning are among the methods of reasoning that are employed.
- What is Robotics in AI?
AI in robots enables decision-making, path planning, and environmental interaction. - Deductive Reasoning:
Used to arrive at logical conclusions using pre-established rules and current input. - Planning:
Determines a sequence of actions to reach a goal (used in motion planning, navigation). - Causal Reasoning:
Understands the cause-effect relationship.
E.g.,- Action: Push box
- Expected Result: Box moves
- Example:
A robot in a warehouse plans a path to pick up an item using known rules and cause-effect models.
4. Natural Language Processing
Used Reasoning: Logical & Statistical Reasoning
- What is NLP?
It enables machines to produce, comprehend, and interpret human language. - Logical Reasoning:
Used to comprehend meaning, grammar, and sentence structure. - Statistical Reasoning:
Uses probabilities and machine learning to interpret ambiguous language.
Example: Using language models to predict a sentence’s next word. - Example:
- Logical: “All dogs are animals. Max is a dog. ⇒ Max is an animal.”
- Statistical: Predicting that “run” is likely to follow “I want to go for a…”
5. Machine Learning
Used Reasoning: Inductive
- What is Machine Learning?
Without explicit programming, algorithms use data to identify patterns and generate predictions or decisions. - Inductive Reasoning:
Generalizes from specific examples to form general rules.
E.g.,- Example 1: This email with “win money” is spam
- Example 2: Another email with “free prize” is spam
- Induction: Emails with such phrases are probably spam
- Example:
Training a model on thousands of labeled emails to classify new emails.
6. Game AI
Used Reasoning: Deductive, Probabilistic, Planning
- What is Game AI?
AI used in games to control non-player characters (NPCs) or simulate strategic thinking. - Deductive Reasoning:
Applies rules, such as attack logic or movement. - Probabilistic Reasoning:
Deals with ambiguity (e.g., opponent strategies, hidden cards). - Planning:
Selects a series of actions to win the match. - Example:
AI plans moves in chess by predicting future board positions and determining the optimal strategy for winning.
Summary Table (Simplified):
| Reasoning Type | Used In |
|---|---|
| Deductive | Expert Systems, Robotics, Game AI |
| Abductive | Diagnosis Systems |
| Probabilistic | Diagnosis Systems, Game AI, NLP |
| Rule-Based | Expert Systems |
| Planning | Robotics, Game AI |
| Causal Reasoning | Robotics |
| Inductive | Machine Learning |
| Logical Reasoning | NLP, Expert Systems |
| Statistical Reasoning | NLP, Machine Learning |
7. Challenges in AI Reasoning
Artificial Intelligence reasoning isn’t as simple as applying formulas or rules. The real world is uncertain, ambiguous, and complex. Let’s explore the key challenges:
- Incomplete or uncertain knowledge
- Natural language ambiguity
- Computational complexity
- Balancing logic with probabilistic methods
1. Incomplete or Uncertain Knowledge
- Meaning of it:
AI often does not have access to all the facts or the full context. It has to work with partial, noisy, or uncertain information. - Why it’s a problem:
Conventional reasoning presumes total knowledge, meaning that if A is true, then B is true. However, complete data is rarely provided by real-world scenarios. - Example:
A medical diagnosis system might not have all test results or symptom reports. It still needs to guess the most probable disease. - Solution techniques:
- Use probabilistic reasoning (like Bayesian Networks)
- Use fuzzy logic to handle imprecise data
- Use default or non-monotonic reasoning to make assumptions
2. Natural Language Ambiguity
- The meaning of it:
Natural language (like English or Urdu) is full of ambiguity — one sentence can have multiple meanings depending on context. - Why it’s a problem:
AI needs to understand and reason based on user input, but language ambiguity can cause wrong interpretation. - Example:
- “I saw the man with the telescope,” is the sentence.
- Did I use the telescope?
- Or did the man have it?
- “I saw the man with the telescope,” is the sentence.
- In AI reasoning:
If the system misunderstands a command or question, it may draw wrong conclusions or make bad decisions. - Solution techniques:
- For disambiguation, combine machine learning and logical reasoning.
- Use contextual embeddings like BERT or GPT to understand meaning.
3. Computational Complexity
- What it means:
Reasoning over large sets of facts or rules can become computationally expensive — even exponential in time. - Why it’s a problem:
AI systems must make decisions fast, but they become slower the more rules and options they take into account. - Example:
- In chess, there are millions of possible board states after just a few moves.
- Searching all possible outcomes to find the best move is very complex.
- In logic-based reasoning:
Inference engines using propositional or first-order logic can become undecidable or non-scalable for large problems. - Solution techniques:
- Use heuristics to reduce search space
- Use approximate inference methods
- Use knowledge pruning or rule prioritization
4. Balancing Logic with Probabilistic Methods
- What it means:
Combining strict logical rules with uncertain probabilistic data is difficult — they work on different principles. - Why it’s a problem:
Probability is gray (likelihood), whereas logic is black or white (true/false). AI in the real world frequently requires both. - Example:
- Logical rule: “If a patient has symptom X and test Y is positive, then they have Disease Z.”
- But what if the test is only 70% reliable?
- Challenge:
How to reason logically when inputs are uncertain or partially true? - Solution techniques:
- Probabilistic logic (e.g., Markov Logic Networks)
- Bayesian Logic Programs
- Combine symbolic AI (logic) with statistical AI (ML)
Summary Table
| Challenge | Why It Matters in AI Reasoning |
|---|---|
| Incomplete or Uncertain Knowledge | AI can’t always know everything. Needs to make guesses based on probability or default assumptions. |
| Ambiguity in Natural Language | Multiple meanings confuse AI — makes reasoning based on user input error-prone. |
| Computational Complexity | Large knowledge bases or decision trees are slow and may not finish in real time. |
| Balancing Logic and Probability | Real-world problems mix certainty and uncertainty — combining logic and statistics is hard. |