Artificial intelligence what is agent and all about agent
- 1. AI CH:02 Intelligent Agents Vidya Vikas Education Trust’s Universal College of Engineering, Vasai(E)
- 2. Content • Introduction of agents, • Structure of Intelligent Agent, • Characteristics of Intelligent Agents • Types of Agents: Simple Reflex, Model Based, Goal Based, Utility Based Agents. • Environment Types: Deterministic, Stochastic, Static, Dynamic,Observable, Semi-observable, Single Agent, Multi Agent
- 3. Agent • An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators
- 5. Vacuum-cleaner world • This world has just two locations: squares A and B. • The vacuum agent perceives which square it is in and whether there is dirt in the square. • It can choose to move left, move right, suck up the dirt, or do nothing. • One very simple agent function is the following: if the current square is dirty, then suck; otherwise, move to the other square. • A partial tabulation of this agent function is shown
- 9. Structure of an AI Agent • The task of AI is to design an agent program which implements the agent function. • The structure of an intelligent agent is a combination of architecture and agent program. It can be viewed as: • Agent = Architecture + Agent program • Architecture: Architecture is machinery that an AI agent executes on. • Agent Function: Agent function is used to map a percept to an action. • f:P* → A • Agent program: Agent program is an implementation of agent function. An agent program executes on the physical architecture to produce function f.
- 10. Characteristics of Agent • Situatedness The agent receives some form of sensory input from its environment, and it performs some action that changes its environment in some way. • Examples of environments: Vacuum cleaner • Autonomy The agent can act without direct intervention by humans or other agents and that it has control over its own actions and internal state.
- 11. • Adaptivity The agent is capable of (1) reacting flexibly to changes in its environment; (2) taking goal-directed initiative (i.e., is pro-active), when appropriate; and (3) learning from its own experience, its environment, and interactions with others. • Sociability The agent is capable of interacting in a peer-to-peer manner with other agents or humans.
- 12. Types of Environment • Fully observable vs. partially observable • Single agent vs. multiagent • Deterministic vs. stochastic • Episodic vs. sequential • Static vs. dynamic • Discrete vs. continuous • Known vs. unknown
- 13. 1.Fully observable vs. partially observable: • If an agent’s sensors give it access to the complete state of the environment at each point in time, then we say that the task environment is fully observable. • Fully observable environments are convenient because agent need not maintain any internal state to keep track of the world. • An environment is partially observable because of noisy and inaccurate sensors or parts of state are missing from sensor data. • For example, a vacuum agent with only a local dirt sensor cannot tell whether there is dirt in other squares, and an automated taxi cannot see what other drivers are thinking.
- 15. 2.Single agent vs. multiagent: • For example, an agent solving a crossword puzzle by itself is clearly in a single-agent environment, • whereas an agent playing chess is in a two agent environment. • Chess is a competitive multiagent environment. • Carrom is multiagent.
- 16. 3.Deterministic vs. stochastic. • Deterministic vs. stochastic. • If the next state of the environment is completely determined by the current state and the action executed by the agent, then we say the environment is deterministic; • otherwise, it is stochastic. • Playing chess is deterministic. • Taxi driving is clearly stochastic because one can never predict the behaviour of traffic. .
- 17. 4.Episodic vs. sequential: • In an episodic task environment, the agent’s experience is divided into atomic episodes. • In each episode the agent receives a percept and then performs a single action.The next episode does not depend on the actions taken in previous episodes. Many classification tasks are episodic. • For example, an agent that has to spot defective parts on an assembly line bases each decision on the current part, regardless of previous decisions; • In sequential environment current decision could affect all future decisions. • Chess and taxi driving are sequential: in both cases, short-term actions can have long-term consequences.
- 19. 5.Static vs. dynamic: • If the environment can change while an agent is deliberating, then we say the environment is dynamic for that agent; otherwise, it is static. • Eg. Taxi driving is clearly dynamic: the other cars and the taxi itself keep moving • Crossword puzzles are static
- 20. 6.Discrete vs. continuous • If the environment has limited number of distinct state clearly defined percepts and actions then the environment is discrete. • For example, the chess environment has a finite number of distinct states. • Taxi driving is a continuous-state and continuous-time problem: the speed and location of the taxi and of the other vehicles sweep through a range of continuous values.
- 21. 7.Known vs. unknown: • In a known environment, the outcomes for all actions are given. • Obviously, if the environment is unknown, the agent will have to learn how it works in order to make good decisions. • for example, in solitaire card games, I know the rules but am still unable to see the cards that have not yet been turned over.
- 22. ● Observable (Fully/Partially) ● Single/Multi agent ● Deterministic ● Sequential ● Static ● Discrete
- 24. Type of Agents Agents can be grouped into four classes based on their degree of perceived intelligence and capability : • Simple reflex agents; • Model-based reflex agents; • Goal-based agents; and • Utility-based agents.
- 26. 1.Simple reflex agent. • These agents select actions based on the current percept, ignoring the rest of the percept history. • For example, the vacuum agent because its decision is based only on the current location and on whether that location contains dirt • condition–action rule, written as • if status = Dirty then Clean • else if location = A then Right EG: Medical diagnosis system If the patient has cold,fever,cough, breathing problem then start the treatment for covid.
- 27. The agent function is based on the condition-action rule. A condition-action rule is a rule that maps a state i.e, condition to an action. If the condition is true, then the action is taken, else not.
- 28. 2.Model-based reflex agent. • It uses internal model to keep track of the current state of the world. • The Model-based agent can work in a partially observable environment, and track the situation • For other driving tasks such as changing lanes, the agent needs to keep track of where the other cars are if it can’t see them all at once.
- 29. ● Model: It is knowledge about "how things happen in the world," so it is called a Model-based agent. ● Internal State: It is a representation of the current state based on percept history.
- 31. 3.Goal Based Agent ● The knowledge of the current state environment is not always sufficient to decide for an agent to what to do. ● An agent knows the description of current state and also needs some sort of goal information that describes situation that are desirable. • The action matched with the current state selected depends on the goal state. • The goal based agent is more flexible for more than one destination. • After identifying one destination the new destination is specified goal based agent is activated to come up with a behavior.
- 33. 4.Utility based Agent • These agents are similar to the goal-based agent but provide an extra component of utility measurement which makes them different by providing a measure of success at a given state. • Utility-based agent act based not only goals but also the best way to achieve the goal. • The Utility-based agent is useful when there are multiple possible alternatives, and an agent has to choose in order to perform the best action. • The utility function maps each state to a real number to check how efficiently each action achieves the goals.
- 34. Utility based Agent • 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. • when there are conflicting goals,only some of which can be achieved (for example, speed and safety), • The utility function specifies the appropriate tradeoff or the most important goal • they choose the action based on a preference (utility)for each state.
- 36. Difference between goal-based agents and utility-based agents • Goal based agents decides its actions based on goal whereas Utility based agents decides its actions based on utilities. • Goal based agents are more flexible whereas Utility based agents are less flexible. • Goal based agents are less faster whereas Utility based agents are more faster. • Goal based agents are not enough to generate high-quality behavior in most environment whereas Utility based agents are enough to generate high-quality behavior in most environment. • Goal based agents can not specify the appropriate tradeoff whereas Utility based agents can specify the appropriate tradeoff . • Goal based agents are less safer whereas Utility based agents are more safer
- 37. Rational • Rational dictionary meaning is something logical, sensible and not emotional. • A rational agent is one that does the right thing. • The rationality of an agent is measured by its performance measure.
- 39. Thank you ! 39