CSE 471/598, CBS 598
Intelligent Agents
TIP
Spring 2005
We’re intelligent agents,
aren’t we?
Introduction
An agent is anything that can be viewed
as perceiving its environment through
sensors and acting upon that
environment through actuators.
Let’s look at Figure 2.1
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Is that me?
An agent function maps percepts to actions
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All about Agents
We will learn
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How agents should act
Environments of agents
Types of agents
 human, robot, software agents
A vacuum-cleaner world with 2 locations (Fig
2.2)
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A simple agent (Fig 2.3)
What makes an agent good or bad?
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We need to specify how agents should act in order
to measure
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How Agents should act
A rational agent is one that does the right
thing.
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What is “right”? The issue of performance
measure, not a simple one
 You often get what you ask for.
 Be as objective as possible
 Measure what one wants, not how the agent
should behave
 How to be a rational instructor/student?
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A related issue is when to measure it.
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A rational agent is not
omniscient
Rationality is concerned with expected
success given what has been perceived
A percept sequence contains everything
that the agent has perceived so far
An ideal rational agent should do
whatever action that maximize its
expected performance
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Rationality does not mean perfection which
maximizes actual performance
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Four key components
What is rational depends on PEAS:
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Performance measure
Environment
Actuators – generating actions
Sensors – receiving percepts
Another example? Taxi driver (Fig 2.4)
 Let’s look at its performance measure
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Definition of a 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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From percept sequences to actions
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A mapping with possibly infinite entries
An ideal mapping describes an ideal agent
It’s not always necessary to have an explicit
mapping in order to be ideal (e.g., sqrt (x))
An agent should have some autonomy.
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i.e., its behavior is determined by its own
experience.
Autonomy can evolve with an agent’s experience
and percept sequence - learning.
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External environments
Without exception, actions are done by
the agent on the environment, which in
turn provides percepts to the agents.
Environments affect the design of
agents
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Types of environments
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Types of Environments
Fully vs. partially observable
Deterministic vs. stochastic
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E is deterministic but actions of other agents are not
=>strategic
Episodic vs. sequential
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An example of episodic environment?
Static vs. dynamic
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E does not change, performance score does => semi-
dynamic
Discrete vs. continuous
Single vs. multiple agents
What is the most difficult environment?
Let’s look at some examples in Fig 2.6
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Design and Implementation of
Agents
Design an agent function that maps the
agent’s percepts to actions.
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Or to realize how actions are
selected/determined
Implement the agent function in an agent
program which is realized in an agent
architecture
Agent = Architecture+ Program
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Percepts and actuators + function mappings
From Robots to Softbots (Amazon’s A9)
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Architectures can be very different
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Some examples of agents
All agents have four elements (PEAS):
1. Performance
3. Actuators
2. Environment
4. Sensors
Fig 2.5 demos some agent types
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We can see that there are many ways to
define these components and it’s difficult
to enumerate all possibilities
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Starting from the simplest
A look-up agent (Fig 2.7)
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Generates actions based on percept sequences
 Your decision today is determined by many things
happened in the past
Why not just look up?
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How far back should we look up
Scaling up
An equivalent question is about the table size
What else should we try?
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Types of agents
Simple reflex agents - respond based on
the current percept, ignore the percept
history. It cuts down a lot of possibilities.
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An example (Fig 2.8)
A simple reflex agent (Figs 2.9,2.10)
 Condition-action Rules
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Innate reflexes vs. learned responses
What if the environment is not fully
observable?
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Model-based reflex agents
They can handle partial observability
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Knowledge about how the world works is called a
model of the world
Maintain internal state to keep information
of the changing environment and involve
consideration of the future
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Respond to a percept accordingly (Figs 2.11,2.12)
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Goal-based agents
They aim to achieve goals (F2.13)
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Goal: desirable states,
Search for a sequence of actions,
Plan for solving sub-problems with
special purposes
Goals alone are often not enough to
generate high-quality behavior. Why?
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Utility-based agents
They aim to maximize their utilities (F2.14)
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Utility: the quality of being useful, a single value
function
Happy or not (a goal or not) vs. How happy when
the goal is achieved
resolve conflicting goals (speed vs. safety)
evaluate with multiple uncertain qualities
search for trade-off facing multiple goals
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Learning agents
They can learn to improve (Fig 2.15)
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Operate in initially unknown environments and
become more competent
Four components: (1) problem generator (to
create exploratory actions), (2) performance
element (the earlier entire agent), (3) learner, (4)
critic (to provide feedback)
The above types of agents can be found in
the later chapters we will discuss.
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Summary
There are various types of agents who cannot
live without external environment.
Efficiency and flexibility of different agents.
Using ourselves as a model and our world as
environment (Are we too ambitious?), you may:
Describe options for future consideration
Recommend a new type of agents
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