introduction to artificial intelligence 2nd semester 2015 ... · introduction to artificial...

Introduction to Artificial Intelligence2nd semester 2016/2017

Chapter 2: Intelligent AgentsMohamed B. Abubaker

Palestine Technical College – Deir El-Balah

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Agents and Environments

• An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators

• A human agent has:• eyes, ears, and other organs for sensors

• hands, legs, mouth, and other body parts for actuators

• Robotic agent: • cameras and infrared range finders for sensors

• various motors for actuators

• The term percept refers to the agent’s perceptual inputs at any given instance.

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Agents and Environments (cont..)

• Agent’s behavior is described by the agent function that maps any given percept sequence to an action.

• Agent function for an artificial agent will be implemented by an agent program

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Agents interact with environment through sensors and actuators

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Vacuum-cleaner world

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The Concept of Rationality

• A rational agent is one that does the right thing

• The right action is the one that will cause the agent to be most successful

• Performance measure: An objective criterion for success of an agent's behavior

• E.g., performance measure of a vacuum-cleaner agent could be:• amount of dirt cleaned up, amount of time taken, amount of electricity consumed,

amount of noise generated, etc.

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Rationality

• Rational Agent: For each possible percept sequence, a rational agent should selectan action that is expected to maximize its performance measure, given theevidence provided by the percept sequence and whatever built-in knowledge theagent has.

• The answer if a given agent is a rational agent, depends on four things:• The performance measure

• The agent’s prior knowledge of the environment

• The actions that the agent can perform

• The agent’s percepts sequence to date

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Omniscience, Learning, and Autonomy

• Rationality is distinct from omniscience (all-knowing with infinite knowledge)• Rationality ≠ Perfection

• Rationality maximizes the expected performance

• Information gathering

• Rational agent does not require to gather information only, but also to learn asmuch as possible from what it perceives.• Learning

• The agent’s initial configuration could reflect some prior knowledge of the environment,but as the agent gains experience this may be modified and augmented.

• A rational agent should be autonomous• if its behavior is determined by its own experience

• become effectively independent of its prior knowledge

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The Nature of Environments

• In designing an agent, the first step must always be to specify the task environment as fully as possible

• Task Environment (PEAS):• Performance Measure

• Environment

• Actuators

• Sensors

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PEAS description of the task environment for an automated taxi

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Properties of Task Environment

• Fully observable vs. Partially observable

• Single agent vs. multi-agent

• Deterministic vs. Stochastic

• Episodic vs. Sequential

• Static vs. Dynamic

• Discrete vs. Continuous

• Known vs. Unknown

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Properties of Task Environment

• Fully observable vs. Partially observable• Fully observable:

• An agent's sensors give it access to the complete state of the environment at each point in time.

• the sensors detect all aspects that are relevant to the choice of action.

• convenient because the agent need not maintain any internal state to keep track of the world

• Partially observable

• because of noisy and inaccurate sensors

• or because parts of the state are simply missing from the sensor data

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Properties of Task Environment

• Single agent vs. multi-agent• An agent solving a crossword puzzle by itself is a single agent environment

• Multi-agent environment

• Cooperative

• Competitive

• Communication

• Chess, taxi driving, soccer

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Properties of Task Environment

• Deterministic vs. Stochastic• Deterministic

• The next state of the environment is completely determined by the current state and the action executed by the agent

• Crossword puzzle, chess

• Otherwise, it is a stochastic environment.

• Taxi driving, dice

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Properties of Task Environment

• Episodic vs. Sequential• Episodic

• The agent's experience is divided into atomic "episodes"

• each episode consists of the agent perceiving and then performing a single action

• the choice of action in each episode depends only on the episode itself

• Assembly line

• Sequential

• The current decision could affect all future decisions

• Chess, taxi driving

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Properties of Task Environment

• Static vs. Dynamic• Dynamic

• The environment can changed while an agent is deliberating (deciding on an action)

• Otherwise, it is a static environment

• The environment is semidynamic if the environment itself does not change with the passage of time but the agent's performance score does

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Properties of Task Environment

• Discrete vs. Continuous• applies to the state of the environment, to the way time is handled, and to the percepts

and actions of the agent

• A limited number of distinct states, and discrete set of percepts and actions. Discrete

• Taxi driving is a continuous-state and continuous-time problem

• Known vs. Unknown• refers not to the environment itself but to the agent’s (or designer’s) state of knowledge

about the “laws of physics” of the environment

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The Structure of Agents

• So far, describe agent by behavior:• the action that is performed after any given sequence of percepts

• The job of AI is to design:• an agent program that implements the agent function

• this program will run on some sort of computing device with physical sensors and actuators

• Agent = architecture + program

• The difference between the agent program and the agent function:• Agent program takes the current percept as input

• Agent function takes the entire percept history

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Table lookup Agent

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Agent program for a vacuum-cleaner agent

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Basic kinds of Agent Programs

• Simple reflex agents

• Model-based reflex agents

• Goal-based agents

• Utility-based agents

• Learning agents

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Simple reflex agents

• The simplest kind of agent

• These agents select actions on the basis of the current percept, ignoring the rest of the percept history

• Simple reflex agents have the admirable property of being simple, but they turn out to be of limited intelligence

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Simple reflex agents

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Simple reflex agents

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Model-based reflex agents

• The most effective way to handle partial observability is for the agent to keep track of the part of the world it can’t see now.

• The agent should maintain some sort of internal state that depends on the percept history and thereby reflects at least some of the unobserved aspects of the current state

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Model-based reflex agents

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Model-based reflex agents

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Goal-based agents

• Knowing something about the current state of the environment is not always enough to decide what to do

• the agent needs some sort of goal information that describes situations that are desirable

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Goal-based agents

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Utility-based agents

• Goals alone are not enough to generate high-quality behavior in most environments

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Learning agents

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END

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