Artificial Intelligence - Components of Intelligent Systems

Intelligent Systems are automated systems that interpret their surroundings, analyze data, learn from experiences, and make decisions to achieve specific goals. These systems often incorporate AI algorithms that can perform tasks that require human intelligence, such as problem-solving, reasoning, learning, interpreting natural language, recognizing patterns, and adjusting to changing circumstances.

In the context of artificial intelligence, it is important to know how the components of intelligent systems interact and integrate to form a agent capable of solving complex problems. Following is the list of the primary components −

Perception

Perception is the cognitive process of interpreting and organizing sensory information gathered from environment which includes cameras, microphones, and radar. Additionally, it also includes data acquisition methods and protocols used to collect data efficiently and accurately.

Reasoning

Reasoning is achieved through inference engines that use logical rules on the knowledge base, enabling the system to gain new information and make decisions. Logic frames, including propositional and first order logic are frequently used for formal reasoning processes. There are broadly two types −

• Inductive Reasoning: It conducts specific observations to makes broad general statements. It starts with a general statement and examines the possibilities to reach a specific, logical conclusion. Even if all of the premises are true in a statement, inductive reasoning allows for the conclusion to be false. For example, "Nita is a teacher. Nita is studious. Therefore, All teachers are studious."
• Deductive Reasoning: It starts with a general statement and examines the possibilities to reach a specific, logical conclusion. If something is true of a class of things in general, it is also true for all members of that class. For example, "All women of age above 60 years are grandmothers. Shalini is 65 years. Therefore, Shalini is a grandmother."

Learning

Learning is the process that enables systems to adapt over time by processing data. This involves acquiring new data or modifying existing knowledge, skills, or behavior. Machine Learning and Deep Learning algorithms play a significant role in analyzing patterns in datasets and learning from them. The three main ways to learn in AI are −

• Auditory Learning: It is learning by listening and hearing. For example, students listening to recorded audio lectures.
• Episodic Learning: To learn by remembering sequences of events that one has witnessed or experienced. This is linear and orderly.
• Motor Learning: It is learning by precise movement of muscles. For example, picking objects, Writing, etc.
• Observational Learning: To learn by watching and imitating others. For example, child tries to learn by mimicking her parent.
• Perceptual Learning: It is learning to recognize stimuli that one has seen before. For example, identifying and classifying objects and situations.
• Relational Learning: It involves learning to differentiate among various stimuli on the basis of relational properties, rather than absolute properties. For Example, Adding little less salt at the time of cooking potatoes that came up salty last time, when cooked with adding say a tablespoon of salt.
• Spatial Learning: It is learning through visual stimuli such as images, colors, maps, etc. For Example, A person can create roadmap in mind before actually following the road.
• Stimulus-Response Learning: It is learning to perform a particular behavior when a certain stimulus is present. For example, a dog raises its ear on hearing doorbell.

Decision-Making

Decision-Making depends on algorithms that determine sequences of actions to reach a specific goals. Techniques such as A* Search or Monte Carlo Tree Search are commonly used along with optimization methods like Linear Programming and Genetic Algorithms to identify the best sequence of actions from various alternatives.

Linguistic Intelligence

Linguistic Intelligence refers to capability of the system to understand and interpret natural language effectively, which includes both written and spoken. This allows systems to understand the order and meaning of words and to apply meta-linguistic skills to reflect on the use of language.

Problem-Solving

Problem-Solving is the ability to process information and find solutions to complex or challenging situations. It involves identifying the problem, generating potential solutions, and implementing the best solution effectively. The techniques used for these processes include −

• Search Algorithms: Explore techniques for example dept-first search, breadth-first search, and A* Algorithm, which are used to identify the possible solution in order to find the optimal solution.
• Heuristics: It includes strategies or methods that guide the search process in AI algorithms by providing estimates of the most effective solution. They are often used in situations where it is difficult to find an exact solution, and provides approximate solution.
• Optimization Techniques: Methods functioning as genetic algorithms and simulated annealing to optimize the search through the available possibilities.

Action Selection

Action Selection is the process by which an intelligent agent decides what action to perform at any given time. It is one of the significant component that directly influences the agent's effectiveness in interacting with the environment. This process involves evaluating the possible actions at a particular state and select the one that maximizes the agent's chances to achieve its goal.