Intelligent Agent Informatics Engineering Study Program School of Electrical Engineering and Informatics Institute of Technology Bandung
Intelligent Agent
Informatics Engineering Study ProgramSchool of Electrical Engineering and Informatics
Institute of Technology Bandung
Outline
Review
Agents & Environment
Rationality
PEAS (Performance Measures, Environment, Actuators, Sensors)
Environment types
Agent types
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Review• Berdasarkan keempat pendekatan IB, tentukan pendekatan yang
digunakan pada aplikasi/ teknologi berikut ini, ataukah aplikasi
tersebut tidak menggunakan pendekatan inteligensi buatan.
Jelaskan dengan singkat jawaban anda.
– NuPIC, platform perangkat lunak yang berbasiskan pada model
struktur dan operasi pada neocortex (bagian pada otak
mamalia).
– PXDES, aplikasi yang melakukan diagnosis X-ray untuk
penentuan pneumoconiosis (penyakit paru-paru yang
disebabkan oleh penghisapan debu).
– Pc-Nqthm, aplikasi „proof-checker‟ yang berlandaskan pada teori
automated reasoning, berdasarkan aturan formal logika.
– AceMoney, aplikasi yang membantu mengorganisasikan dan
mengatur keuangan individu.
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Agent & Environment
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Environment
Agent
Percepts
Actions
sensors
actuators
?
Agents
[1]: Software that gathers information about an environment and takes actions based on that information.
A robot
A factory
A web shopping program
… [2]: computer system that is situated in some
environment, and that is capable of autonomous action in this environment in order to meet its design objectives
Computational agents that behave autonomously
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Agents• Anything that can be viewed as perceiving its
environment through sensors and acting upon that
environment through effectors.
– A robot
– A factory
– A web shopping program
– …
Computational agents that behave autonomously
• Human agent: 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
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The Agent & the Environment
• How do we begin to formalize the problem of building an
agent?
– Make a dichotomy between the agent and the environment
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Vacuum-cleaner world
• Percepts: location and contents, e.g.,
[A,Dirty]
• Actions: Left, Right, Suck, NoOp
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World Model
• A – the action space
• P – the percept space
• Define:
– S – internal state [may not be visible to agent]
– Perception function: S P
– World dynamics: S x A S
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Perception Function
World Dynamics
ap
s
Agent Design
• U – utility function: S real (or S* real)
• The agent design problem: Find P* A
– mapping of sequences of percepts to actions
– maximize the utility of the resulting sequences of
states (each action maps from one state to next state)
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Planning Agent Policy
• Planning is explicitly considering future consequences of actions in order to choose the best one.
• So, planning is the process of generating possible sequences of actions, simulating their consequences, picking which is the best and committing to one of these actions.
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ai that leadss
a1
aa2
a3
u
u
u
u
u
u to max U
s1
s2
s3
a4
a5
Rational agents• An agent should strive to "do the right thing", based on
what it can perceive and the actions it can perform. 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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Rational agents
• Rational Agent: For each 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.
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Rationality• A rational agent takes actions it believes will achieve its goals.
– Assume I don‟t like to get wet, so I bring an umbrella. Is that rational?
– Depends on the weather forecast and whether I‟ve heard it. If I‟ve heard the forecast for rain (and I believe it) then bringing the umbrella is rational.
• Rationality ≠ omniscience
– Assume the most recent forecast is for rain but I did not listen to it and I did not bring my umbrella. Is that rational?
– Yes, since I did not know about the recent forecast!
• Rationality ≠ success
– Suppose the forecast is for no rain but I bring my umbrella and I use it to defend myself against an attack. Is that rational?
– No, although successful, it was done for the wrong reason.
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Limited Rationality• There is a big problem with our definition
of rationality
• The agent might not be able to compute
the best action (subject to its beliefs and
goals).
• So, we want to use limited rationality:
“acting in the best way you can subject to
the computational constraints that you
have”.
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Limited Rationality (2)• The (limited rational) agent design problem:
Find P* A
– mapping of sequences of percepts to actions
– maximizes the utility of the resulting sequences of
states
– subject to our computational constraints
• To design an agent specify PEAS
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PEAS• PEAS: Performance measure, Environment,
Actuators, Sensors
• Must first specify the setting for intelligent agent design
• Consider, e.g., the task of designing an automated taxi driver:– Performance measure: Safe, fast, legal, comfortable
trip, maximize profits
– Environment: Roads, other traffic, pedestrians, customers
– Actuators: Steering wheel, accelerator, brake, signal, horn
– Sensors: Cameras, sonar, speedometer, GPS, odometer, engine sensors, keyboard
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Another Example of PEAS
Medical diagnosis system Agent
• Performance measure: Healthy patient, minimize
costs, lawsuits
• Environment: Patient, hospital, staff
• Actuators: Screen display (questions, tests,
diagnoses, treatments, referrals)
• Sensors: Keyboard (entry of symptoms, findings,
patient's answers)
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Another Example of PEAS
Part-picking robot Agent
• Performance measure: Percentage of parts in
correct bins
• Environment: Conveyor belt with parts, bins
• Actuators: Jointed arm and hand
• Sensors: Camera, joint angle sensors
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Environment types
• Fully observable (vs. partially observable): An agent's sensors give it access to the complete state of the environment at each point in time.
• Deterministic (vs. stochastic): The next state of the environment is completely determined by the current state and the action executed by the agent. (If the environment is deterministic except for the actions of other agents, then the environment is strategic)
• Episodic (vs. sequential): The agent's experience is divided into atomic "episodes" (each episode consists of the agent perceiving and then performing a single action), and the choice of action in each episode depends only on the episode itself.
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Environment types
• Static (vs. dynamic): The environment is unchanged
while an agent is deliberating. (The environment is
semidynamic if the environment itself does not change
with the passage of time but the agent's performance
score does)
• Discrete (vs. continuous): A limited number of distinct,
clearly defined percepts and actions.
• Single agent (vs. multiagent): An agent operating by
itself in an environment.
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Environment types
Chess with Chess without Taxi driving
a clock a clock
Fully observable Yes Yes No
Deterministic Strategic Strategic No
Episodic No No No
Static Semi Yes No
Discrete Yes Yes No
Single agent No No No
• The environment type largely determines the agent design
• The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent
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Agent functions and programs
• An agent is completely specified by the agent
function mapping percept sequences to actions
• One agent function (or a small equivalence
class) is rational
• Aim: find a way to implement the rational agent
function concisely
• We can use table lookup that maps sequence of
percepts to action
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Table-lookup agent
• Drawbacks:
– Huge table
– Take a long time to build the table
– No autonomy
– Even with learning, need a long time to learn the table
entries
• Alternatives reflex agent: agent respond very
flexible to a very broad range of stimuli
– Drawback: coulds not store the response
• Next: Types of agent based
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Agent types
Four basic types :
• Simple reflex agents
• Model-based reflex agents
• Goal-based agents
• Utility-based agents
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Simple reflex agents
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Model-based reflex agents
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Goal-based agents
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Utility-based agents
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Learning agents (later on…)
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THANK YOU