c463 b551 artificial intelligence l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
C463 / B551 Artificial Intelligence PowerPoint Presentation
Download Presentation
C463 / B551 Artificial Intelligence

Loading in 2 Seconds...

play fullscreen
1 / 22

C463 / B551 Artificial Intelligence - PowerPoint PPT Presentation


  • 257 Views
  • Uploaded on

C463 / B551 Artificial Intelligence Dana Vrajitoru Intelligent Agents Intelligent Agent Agent : entity in a program or environment capable of generating action. An agent uses perception of the environment to make decisions about actions to take.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'C463 / B551 Artificial Intelligence' - albert


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
c463 b551 artificial intelligence

C463 / B551Artificial Intelligence

Dana Vrajitoru

Intelligent Agents

intelligent agent
Intelligent Agent
  • Agent: entity in a program or environment capable of generating action.
  • An agent uses perception of the environment to make decisions about actions to take.
  • The perception capability is usually called a sensor.
  • The actions can depend on the most recent perception or on the entire history (percept sequence).

Artificial Intelligence – D. Vrajitoru

agent function
Agent Function
  • The agent function is a mathematical function that maps a sequence of perceptions into action.
  • The function is implemented as the agent program.
  • The part of the agent taking an action is called an actuator.
  • environment  sensors  agent function  actuators  environment

Artificial Intelligence – D. Vrajitoru

slide4

Environment

Environment

Sensors

Percept (Observations)

Agent Function

Agent

Actuator

Environment

Action

Environment

Artificial Intelligence – D. Vrajitoru

rational agent
Rational Agent
  • A rational agent is one that can take the right decision in every situation.
  • Performance measure: a set of criteria/test bed for the success of the agent's behavior.
  • The performance measures should be based on the desired effect of the agent on the environment.

Artificial Intelligence – D. Vrajitoru

rationality
Rationality
  • The agent's rational behavior depends on:
    • the performance measure that defines success
    • the agent's knowledge of the environment
    • the action that it is capable of performing
    • the current sequence of perceptions.
  • Definition: for every possible percept sequence, the agent is expected to take an action that will maximize its performance measure.

Artificial Intelligence – D. Vrajitoru

agent autonomy
Agent Autonomy
  • An agent is omniscient if it knows the actual outcome of its actions. Not possible in practice.
  • An environment can sometimes be completely known in advance.
  • Exploration: sometimes an agent must perform an action to gather information (to increase perception).
  • Autonomy: the capacity to compensate for partial or incorrect prior knowledge (usually by learning).

Artificial Intelligence – D. Vrajitoru

environment
Environment
  • Task environment – the problem that the agent is a solution to.
  • Properties:
  • Observable - fully or partiallyA fully observable environment needs less representation.
  • Deterministic or stochasticStrategic –deterministic except for the actions of other agents.

Artificial Intelligence – D. Vrajitoru

environment9
Environment
  • Episodic or sequentialSequential – future actions depend on the previous ones. Episodic – individual unrelated tasks for the agent to solve.
  • Static – dynamic
  • Discrete – continuous
  • Single agent – multi agentMultiple agents can be competitive or cooperative.

Artificial Intelligence – D. Vrajitoru

more definitions of agents
More Definitions of Agents
  • "An agent is a persistent software entity dedicated to a specific purpose. " (Smith, Cypher, and Spohrer 94 )
  • "Intelligent agents are software entities that carry out some set of operations on behalf of a user or another program with some degree of independence or autonomy, and in so doing, employ some knowledge or representation of the user's goals or desires." (IBM)
  • "Intelligent agents continuously perform three functions: perception of dynamic conditions in the environment; action to affect conditions in the environment; and reasoning to interpret perceptions, solve problems, draw inferences, and determine actions. "(Hayes-Roth 94)

Artificial Intelligence – D. Vrajitoru

agent vs program
Agent vs. Program
  • Size – an agent is usually smaller than a program.
  • Purpose – an agent has a specific purpose while programs are multi-functional.
  • Persistence – an agent's life span is not entirely dependent on a user launching and quitting it.
  • Autonomy – an agent doesn't need the user's input to function.

Artificial Intelligence – D. Vrajitoru

simple agents
Table-driven agents: the function consists in a lookup table of actions to be taken for every possible state of the environment.

If the environment has n variables, each with t possible states, then the table size is tn.

Only works for a small number of possible states for the environment.

Simple reflex agents: deciding on the action to take based only on the current perception and not on the history of perceptions.

Based on the condition-action rule:

(if (condition) action)

Works if the environment is fully observable

Simple Agents

Artificial Intelligence – D. Vrajitoru

slide13
(defuntable_agent (percept)

(let ((action t))

(push percept percepts)

(setq action

(lookup percepts table))

action))

(defunreflex_agent (percept)

(let ((rule t) (state t) (action t))

(setq state (interpret percept))

(setq rule (match state))

(setq action (decision rule))

action))

Artificial Intelligence – D. Vrajitoru

slide14
percepts = []

table = {}

def table_agent (percept):

action = True

percepts.append(percept)

action = lookup(percepts, table)

return action

def reflex_agent (percept):

state = interpret(percept)

rule = match(state)

action = decision(rule)

return action

Artificial Intelligence – D. Vrajitoru

model based reflex agents
Model-Based Reflex Agents
  • If the world is not fully observable, the agent must remember observations about the parts of the environment it cannot currently observe.
  • This usually requires an internal representation of the world (or internal state).
  • Since this representation is a model of the world, we call this model-based agent.

Artificial Intelligence – D. Vrajitoru

slide16
(setq state t) ; the world model

(setq action nil) ; latest action

(defunmodel_reflex_agent (percept)

(let ((rule t))

(setq state

(update_state

state action percept))

(setq rule (match state))

(setq action (decision rule))

action))

Artificial Intelligence – D. Vrajitoru

slide17
state = True # the world model

action = False # latest action

def model_reflex_agent (percept)

state = update_state(state,

action,

percept)

rule = match(state)

action = decision(rule)

return action

Artificial Intelligence – D. Vrajitoru

goal driven agents
Goal-Driven Agents
  • The agent has a purpose and the action to be taken depends on the current state and on what it tries to accomplish (the goal).
  • In some cases the goal is easy to achieve. In others it involves planning, sifting through a search space for possible solutions, developing a strategy.
  • Utility-based agents: the agent is aware of a utility function that estimates how close the current state is to the agent's goal.

Artificial Intelligence – D. Vrajitoru

learning agents
Learning Agents
  • Agents capable of acquiring new competence through observations and actions.
  • Components:
    • learning element (modifies the performance element)
    • performance element (selects actions)
    • feedback element (critic)
    • exploration element (problem generator).

Artificial Intelligence – D. Vrajitoru

other types of agents
Other Types of Agents
  • Temporarily continuous – a continuously running process,
  • Communicative agent – exchanging information with other agents to complete its task.
  • Mobile agent – capable of moving from one machine to another one (or from one environment to another).
  • Flexible agent – whose actions are not scripted.
  • Character – an agent with conversation skills, personality, and even emotional state.

Artificial Intelligence – D. Vrajitoru

agent classification
Agent Classification

Artificial Intelligence – D. Vrajitoru

agent example
Agent Example
  • A file manager agent.
  • Sensors: commands like ls, du, pwd.
  • Actuators: commands like tar, gzip, cd, rm, cp, etc.
  • Purpose: compress and archive files that have not been used in a while.
  • Environment: fully observable (but partially observed), deterministic (strategic), episodic, dynamic, discrete.

Artificial Intelligence – D. Vrajitoru