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![Simple Reflex Agents
It is the simplest type. These agents select actions on the
bases of current percept.
Has no memory.
EX: if car-in-front-is-breaking then initiate-braking
function REFLEX-VACUUM-AGENT( [location,status])
returns an action
if status = Dirty then return Suck
else if location = A then return Right
else if location = B then return Left](https://image.slidesharecdn.com/uniti-1-150703160731-lva1-app6892/75/Introduction-To-Artificial-Intelligence-10-2048.jpg)
Uploaded byNeHal VeRma
Introduction To Artificial Intelligence
Artificial intelligence (AI) aims to create intelligent machines that can perceive and act like humans. Alan Turing first proposed testing machine intelligence with the Turing Test in 1956. An intelligent agent perceives its environment through sensors and acts through effectors. Rational agents are expected to select actions that maximize their performance given perceived inputs and built-in knowledge. Different types of AI agents include simple reflex agents that react to current inputs, model-based reflex agents that track the world state, goal-based agents that consider goals, and utility-based agents that evaluate action outcomes.
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Introduction To Artificial Intelligence
- 1. Introduction To AI Systems By:Nehal Varma
- 2. What is ArtificialIntelligence? AI is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. some of the activities computer with artificial intelligence are designed for include: speech reconization Learning Planning Problem Solving
- 3.
- 4. Alan Turing The TuringTest was the first serious proposal in the philosophy of artificial intelligence in 1956.
- 5. Agent Definition: Anagent perceives its environment via sensors and acts in that environment with its effectors. agent = architecture + program
- 6. Intelligent Agent Anagent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators. Example Of Agents: A human agent has eyes, ears, and other organs for sensors and hands, legs, vocal tract, and so on for actuators. A robotic agent might have cameras and infrared range finders for sensors and various motors for actuators. A software agent receives keystrokes, file contents, and network packets as sensory inputs and acts on the environment by displaying on the screen, writing files, and sending network packets.
- 7. Rational Agents Foreach possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has. For example: Car Wiper, Vaccum Cleaner
- 8. Environment Types Fullyobserved: An agent’s sensors give it access to the complete state of the environment at each point in time. Deterministic: The next state of environment is completely determined by the current state and action executed by the agent. Episodic: The agent experience is divided into atomic episodes and the choice of actions & quality in each episodes depends only on the episode, because subsequent episodes do not depend on what actions occur in previous episodes. Do not need to think ahead.
- 9. Types Of Agents Simple Reflex Agents Model Based Reflex Agents Goal Based Agents Utility Based Agents
- 10. Simple Reflex Agents It is the simplest type. These agents select actions on the bases of current percept. Has no memory. EX: if car-in-front-is-breaking then initiate-braking function REFLEX-VACUUM-AGENT( [location,status]) returns an action if status = Dirty then return Suck else if location = A then return Right else if location = B then return Left
- 11.
- 12. Model Based ReflexAgents It keeps track of the current state of the world using an internal model(Environment). It then chooses an action in the same way as the reflex agent.
- 13. Goal Based Agents Knowing something about the current state of the environment is not always enough to decide what to do. For example. at a road junction, the taxi can turn left, turn right, or go straight on. The agent needs some sort of goal information. for example, passenger's destination. Sometimes goal-based action selection is straight forward. Sometimes it will be more tricky—for example, when the agent has to consider long sequences of twists and turns in order to find a way to achieve the goal.
- 14. Utility Based Agents Goals alone are not enough to generate high-quality behavior in most environments. For example, many action sequences will get the taxi to its destination (thereby achieving the goal) but some are quicker, safer, more reliable, or cheaper than others. How well can the goal be achieved (degree of happiness)