The document discusses different types of intelligent agents, including table-driven agents, simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. It also covers agent environments, structures, and applications such as medical diagnosis systems, part-picking robots, and interactive tutors. The chapter provides an overview of intelligent agents, defining what agents are and describing their main components and how they can be designed for different tasks.

1. Chapter 2: Intelligent Agents
Intelligent Agents (IA) - Concept
IA Types
Environment types
IA Behaviour
IA Structure
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2. Mission
 To design/create computer programs that
have some intelligence, can do some
intelligent tasks or can do tasks that require
some intelligence.
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3. What is an (Intelligent) Agent?
 An over-used, over-loaded, and misused term.
 Anything that can be viewed as perceiving its environment
through sensors and acting upon that environment through
its effectors to maximize progress towards its goals.
 PAGE (Percepts, Actions, Goals, Environment)
 Task-specific & specialized: well-defined goals and environment
 Human agent: eyes, ears, and other organs for sensors; hands,
legs, mouth, and other body parts for actuators
 Robotic agent: cameras and infrared rangefinders for sensors;
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4. Intelligent Agents
 Agents interact with environments through
sensors and effectors
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5. Intelligent Agents and Artificial
Intelligence
 Human mind as network of thousands or millions of agents all
working in parallel. To produce real artificial intelligence, this
school holds, we should build computer systems that also
contain many agents and systems for arbitrating among the
agents' competing results.
 Distributed decision-making
and control
 Challenges:
 Action selection: What next action
to choose
 Conflict resolution
sensors
effectors
Agency
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6. Agent Research Areas
We can split agent research into two main strands:
 Distributed Artificial Intelligence (DAI) –
Multi-Agent Systems (MAS) (1980 – 1990)
 Much broader notion of "agent" (1990’s – present)
 interface, reactive, mobile, information
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7. A Windscreen Agent
How do we design a agent that can wipe the windscreens when
needed?
 Goals?
 Percepts ?
 Sensors?
 Effectors ?
 Actions ?
 Environment ?
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8. A Windshield Wiper Agent
(Cont’d)
 Goals: To keep windscreens clean and maintain
good visibility
 Percepts: Raining, Dirty, Clear
 Sensors: Camera (moist sensor)
 Effectors: Wipers (left, right, back)
 Actions: Off, Slow, Medium, Fast
 Environment: Nairobi city, pot-holed roads, highways,
etc
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9. Interacting Agents
Collision Avoidance Agent (CAA)
 Goals: Avoid running into obstacles
 Percepts ?
 Sensors?
 Effectors ?
 Actions ?
 Environment: Highway
Lane Keeping Agent (LKA)
• Goals: Stay in current lane
• Percepts ?
• Sensors?
• Effectors ?
• Actions ?
• Environment: Freeway
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10. Interacting Agents
Collision Avoidance Agent (CAA)
 Goals: Avoid running into obstacles
 Percepts: Obstacle distance, velocity, trajectory
 Sensors: Vision, proximity sensing
 Effectors: Steering Wheel, Accelerator, Brakes, Horn, Headlights
 Actions: Steer, speed up, brake, blow horn, signal (headlights)
 Environment: Highway
Lane Keeping Agent (LKA)
• Goals: Stay in current lane
• Percepts: Lane center, lane boundaries
• Sensors: Vision
• Effectors: Steering Wheel, Accelerator, Brakes
• Actions: Steer, speed up, brake
• Environment: Freeway
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11. The Right Thing = The
Rational Action
 Rational Action: The action that maximizes the
expected value of the performance measure given the
percept sequence to date
 Rational = Best Yes, to the best of its knowledge
 Rational = Optimal Yes, to the best of its abilities
(including its constraints)
 Rational # omniscience
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12. Rationality
 What is rational at any given time depends on
four things:
 The performance measure that defines the
criterion of success
 The agent’s prior knowledge of the env.
 The actions the agent can perform
 The agent’s percept sequence to date.
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13. 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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14. 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.
 Rationality is distinct from omniscience (all-knowing with
infinite knowledge)
 Agents can perform actions in order to modify future
percepts so as to obtain useful information (information
gathering, exploration)
 An agent is autonomous if its behavior is determined by
its own experience (with ability to learn and adapt)
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15. Behavior and performance of
IAs
 Perception (sequence) to Action Mapping: f : P*  A
 Ideal mapping: specifies which actions an agent ought
to take at any point in time
 Description: Look-Up-Table,..
 Performance measure: a subjective measure to
characterize how successful an agent is (e.g., speed, power
usage, accuracy, money, etc.)
 (degree of) Autonomy: to what extent is the agent able to
make decisions and actions on its own?
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16. How is an Agent different
from other software?
 Agents are autonomous, that is they act on behalf of the user
 Agents contain some level of intelligence, from fixed rules to
learning engines that allow them to adapt to changes in the
environment
 Agents don't only act reactively, but sometimes also
proactively
 Agents have social ability, that is they communicate with the
user, the system, and other agents as required
 Agents may also cooperate with other agents to carry out more
complex tasks than they themselves can handle
 Agents may migrate from one system to another to access
remote resources or even to meet other agents
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17. Specifying the task environment
 The acronym PEAS can be used, which
means:
 Performance measure
 Environment
 Actuators
 Sensors
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18. PEAS
 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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19. Agent: Medical diagnosis system
 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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20. Agent: Part-picking robot
 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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21. Agent: Interactive English tutor
 Performance measure: Maximize student's
score on test
 Environment: Set of students
 Actuators: Screen display (exercises,
suggestions, corrections)
 Sensors: Keyboard
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22. Environment Types
 Characteristics
 Accessible vs. inaccessible
 An environment is accessible if the sensors detect all aspects that are
relevant to the choice of action.
 Deterministic vs. non-deterministic
 If the next state of the environment is completely determined by the
current state and the actions selected by the agents, then we say the
environment is deterministic.
 Episodic vs. non-episodic
 In an episodic environment, the agent’s experience is divided into
“episodes.” Each episode consists of the agent perceiving and then
acting.
 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.
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23. Environment Types
 Characteristics
 Discrete vs. continuous
 If there are a limited number of distinct, clearly defined
percepts and actions we say that the environment is discrete.
 Hostile vs. friendly
 This depends on the agent perception.
 Single agent (vs. multiagent): An agent operating by itself
in an environment.
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24. Environment Types
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25. Structure of Intelligent Agents
 Agent = architecture + program
 Agent program: the implementation of f : P*  A, the
agent’s perception-action mapping
function: Skeleton-Agent(Percept) returns Action
memory  UpdateMemory(memory, Percept)
Action  ChooseBestAction(memory)
memory  UpdateMemory(memory, Action)
return Action
 Architecture: a device that can execute the agent program
(e.g., general-purpose computer, specialized device, specialized,
hardware/software, beobot, etc.)
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26. Agent types
 Four basic types in order of increasing generality:
 Table Driven agents
 Simple reflex agents
 Model-based reflex agents
 Goal-based agents
 Utility-based agents
 Other Agents:
 Learning Agents
 Mobile Agents
 Information Agents
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27. function TABLE-DRIVEN-AGENT (percept) returns action
static: percepts, a sequence, initially empty
table, a table, indexed by percept sequences, initially fully specified
append percept to the end of percepts
action  LOOKUP(percepts, table)
return action
An agent based on a prespecified lookup table. It keeps track of percept
sequence and just looks up the best action
Table-driven agent
• Problems
– Huge number of possible percepts (consider an automated taxi
with a camera as the sensor) => lookup table would be huge
– Takes long time to build the table
– Not adaptive to changes in the environment; requires entire
table to be updated if changes occur
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28. Simple reflex agents
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29. function SIMPLE-REFLEX-AGENT(percept) returns action
static: rules, a set of condition-action rules
state  INTERPRET-INPUT (percept)
rule  RULE-MATCH (state,rules)
action  RULE-ACTION [rule]
return action
A simple reflex agent works by finding a rule whose condition
matches the current situation (as defined by the percept) and
then doing the action associated with that rule.
First match.
No further matches sought.
Only one level of deduction.
Simple reflex agents
• Differs from the lookup table based agent is that the
condition (that determines the action) is
already higher-level interpretation of the
percepts
– Percepts could be e.g. the pixels on the camera of the automated taxi
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30. Model-based reflex agents (Reflex
agents w/ state)
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31. Reflex agent with internal state
…
function REFLEX-AGENT-WITH-STATE (percept) returns action
static: state, a description of the current world state
rules, a set of condition-action rules
state  UPDATE-STATE (state, percept)
rule  RULE-MATCH (state, rules)
action  RULE-ACTION [rule]
state  UPDATE-STATE (state, action)
return action
A reflex agent with internal state works by finding a rule
whose condition matches the current situation (as defined by
the percept and the stored internal state) and then doing
the action associated with that rule.
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32. Goal-based agents
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33. Agent with explicit goals …
• Choose actions so as to achieve a (given or computed) goal = a
description of desirable situations. e.g. where the taxi wants to go
• Keeping track of the current state is often not enough – need
to add goals to decide which situations are good
• Deliberative instead of reactive
• May have to consider long sequences of possible actions
before deciding if goal is achieved – involves considerations of
the future, “what will happen if I do…?” (search and planning)
• More flexible than reflex agent. (e.g. rain / new destination) In
the reflex agent, the entire database of rules would have to be
rewritten
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34. 34

35. Utility-based agent …
• When there are multiple possible alternatives, how to decide
which one is best?
• A goal specifies a crude destination between a happy and
unhappy state, but often need a more general performance
measure that describes “degree of happiness”
• Utility function U: State  Real Number indicating a
measure of success or happiness when at a given state
• Allows decisions comparing choice between conflicting goals,
and choice between likelihood of success and importance of
goal (if achievement is uncertain)
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36. 36

37. Learning Agents
• After an agent is programmed, can it work
immediately?
– No, it still need teaching
• In AI,
– Once an agent is done
– We teach it by giving it a set of examples
– Test it by using another set of examples
• We then say the agent learns
– A learning agent
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38. Learning Agents
• Four conceptual components
– Learning element
• Making improvement
– Performance element
• Selecting external actions
– Critic
• Tells the Learning element how well the agent is doing
with respect to fixed performance standard.
(Feedback from user or examples, good or not?)
– Problem generator
• Suggest actions that will lead to new and informative
experiences.
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39. Mobile agents
 Programs that can migrate from one machine to another.
 Execute in a platform-independent execution environment.
 Require agent execution environment (places).
 Mobility not necessary or sufficient condition for agenthood.
 Practical but non-functional advantages:
 Reduced communication cost (e.g., from PDA)
 Asynchronous computing (when you are not connected)
 Two types:
 One-hop mobile agents (migrate to one other place)
 Multi-hop mobile agents (roam the network from place to place)
 Applications:
 Distributed information retrieval.
 Telecommunication network routing.
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40. Information agents
 Manage the explosive growth of information.
 Manipulate or collate information from many distributed
sources.
 Information agents can be mobile or static.
 Challenge: ontologies for annotating Web pages.
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41. Discussion Questions
1. For each of the following, develop a PEAS
description of the task environment:
1. Robot soccer player
2. Internet book-shopping agent
3. Autonomous Mars rover
4. Mathematician’s theorem-proving assistant
2. For the above Agents characterize their
environment.
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