Introduction

The field itself is an emerging area of computer sciences and a lot of work is underway in order mature the concepts of this field.

Intelligence

The characteristic of intelligence comes in when we try to solve something, we check various ways to solve it, we check different combinations, and many other things to solve different problems. All this thinking, this memory manipulation capability, this numerical processing ability and a lot of other things add to ones intelligence.

However, the doctor is actually faced with solving a problem of diagnosis having looked at some specific measurements. It is important to consider that a doctor who would have a better memory to store all this precious knowledge, better ability of retrieving the correct portion of the knowledge for the correct patient will be better able to classify a patient. Hence, telling us that memory and correct and efficient memory and information manipulation also counts towards ones.

Our intelligence actually helps us do this. Hence the ability to tackle ambiguous and fuzzy problems demonstrates intelligence

Intelligent Machines

• A machine searches through a mesh and finds a path? 
•  machine solves problems like the next number in the sequence? 
• A machine develops plans?  A machine diagnoses and prescribes? 
• A machine answers ambiguous questions? 
• A machine recognizes fingerprints? 
• A machine understands? 
• A machine perceives?
•  A machine does MANY MORE SUCH THINGS!
•  A machine behaves as HUMANS do? HUMANOID

Formal Definitions for Artificial Intelligence

Systems that think like humans Systems that act like humans “The exciting new effort to make computers think … machines with minds, in the full and literal sense” (Haugeland, 1985) “The art of creating machines that perform functions that require intelligence when performed by people” (Kurzweil 1990) “[The automation of] activities that we associate with human thinking, activities such “The study of how to make computers do things at which, at the moment, people are as decision making, problem solving, learning …” (Bellman, 1978).

Components of Intelligent Agents:

1. 1.Sensors: These are the perceptual mechanisms that allow the agent to gather information from its environment. Sensors can include cameras, microphones, temperature sensors, and more.
2. Actuators: Actuators are responsible for executing the actions or commands generated by the agent. Examples include motors, speakers, and other output devices
3. Agent Program: This is the software or logic that processes the sensor inputs and determines the actions to take. It's the "intelligence" of the agent.

Types of Environments

1.Fully Observable vs. Partially Observable: An environment is fully observable if the agent's sensors provide complete information about its state. If the agent has limited or incomplete information, the environment is partially observable.

2.Deterministic vs. Stochastic: In a deterministic environment, the outcomes of actions are predictable. In a stochastic environment, there is an element of randomness or uncertainty.

3.Static vs. Dynamic: A static environment remains unchanged while the agent operates, while a dynamic environment can change over time.

4.Discrete vs. Continuous: An environment may have discrete states and actions, or it may involve continuous variables.