Intelligent (Knowledge Based) agent in AI PEAS coordinates Types of AI agents State based model Logic based model Agent architecture

1. Intelligent (Knowledge Based) Agents

2. Agent: Definition
• An agent is anything that can be viewed as perceiving its environment
through sensors and acting upon that environment through actuators
• Human agent:
– Sensors: eyes, ears, and other organs
– Actuators: hands, legs, and some body parts
• Robotic agent:
– Sensors: cameras, range finders, etc.
– Actuators: levers, motors, etc.
• Softbots (s/w agents)

3. Agent Architecture
Real World Agent
Sensors
Effectors
Reasoning &
Decisions Making
Model of World
(being updated)
List of
Possible Actions
Prior Knowledge
about the World
Goals/Utility

4. Intelligent Agents
• Fundamental abilities of intelligence
– Sensing
– Understanding and reasoning
– Acting
• In order to act, it must first sense. Blind action is not intelligent action
• To sense wisely, AI system has to understand
• Agent must be autonomous
• Agent must be rational

5. Sensors and Effectors
• An agent perceives its environment through sensors
– Percept: the complete set of inputs to an agent at a given time
– The current percept or a sequence of percepts determines the action
of an agent
• An agent can change its environment through effectors or actuators
– Operation involving an actuator is an action

6. Agents and Environments
• The agent function maps from percept histories to actions:
[f: P  A]
• The agent program runs on the physical architecture to
produce f
• f = agent = architecture + program

7. Agent Functions
• An agent is completely specified by the agent function that maps percept
sequences to actions
• Find a way to implement the agent function concisely
• An agent program implements the above mapping (ie, from percept sequences
to actions)
Agent’s Performance
• Behavior of Agent: In terms of agent function:
– Mapping: “Perception history into Action”
– Ideal Mapping: the sequence of actions the agent ought to take at any
point in time
• Performance of Agent: a subjective measure to characterize how successful
an agent is performing in terms of
– Power consumption, accuracy, profit, etc.

8. Setting of Agent: PEAS
• Specification of the setting for intelligent agent design
has 4 coordinates: PEAS
–Performance measure
–Environment
–Actuators
–Sensors

9. PEAS: Example 1
• 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

10. PEAS: Example 2
• 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)

11. PEAS: Example 3
• 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

12. PEAS: Example 4
• Interactive English tutor:
– Performance measure: Maximize student's score on test
– Environment: Set of students
– Actuators: Screen display (exercises, suggestions,
corrections)
– Sensors: Keyboard

13. task
environm
ent
observable deterministic/
stochastic
episodic/
sequential
static/
dynamic
discrete/
continuous
agents
crossword
puzzle
fully determ. sequential static discrete single
chess with
clock
fully strategic sequential semi discrete multi
taxi
driving
partial stochastic sequential dynamic continuous multi
image
analysis
fully determ. episodic semi continuous single
partpicking
robot
partial stochastic episodic dynamic continuous single
refinery
controller
partial stochastic sequential dynamic continuous single
interact.
tutor
partial stochastic sequential dynamic discrete multi

14. Realistic Environments
• The simplest environment is
– Fully observable, deterministic, episodic, static, discrete
and single-agent.
• Most real situations are:
– Partially observable, stochastic, sequential, dynamic,
continuous and multi-agent.

15. Types of Agents
Concept
• Autonomous agent
• Rational agent
• Perfect rationality
• Bounded
rationality
Type of Agent
– Table-driven agent
– Simple reflex agent
– Model-based reflex agent
– Goal-based agent
• Problem-solving agent
– Utility-based agent
• Can distinguish between
different goals
– Learning agent

16. Autonomous Agent
• Autonomous: free, independent, sovereign, not subject to the
rule or control of another.
• An agent is autonomous if its behavior is determined by its
own experience (with ability to learn and adapt)
– An autonomous agent decides autonomously which action
to take in the current situation to maximize progress
toward a goal

17. Rational Agents
• An agent should strive to "do the right thing", based on what it
can perceive and perform the actions.
• For each possible percept sequence, a rational agent should
select an action that is expected to maximize its performance
measure using the agent has whatever built-in knowledge.
• Rationality is distinct from omniscience (all-knowing with
infinite knowledge)

18. Perfect Vs Bounded Rationality
• Perfect Rationality: Assumes that the rational agent knows all
information and takes action that maximizes its utility.
– Humans do not satisfy this definition
• Bounded Rationality: Because of the limitations of the human
mind, humans use approximate methods to handle many tasks.

19. Table-driven Agents
A table is a simple way to specify the mapping, [f: P  A]
• Information comes from sensors: percepts
• Look it up in a table
• Triggers actions through effectors
• No notion of history. Action determined by current state

20. Drawbacks of Table-driven Agents
• Huge tables for mapping
– Chess needs a table with 35100 entries
• Take a long time to build the table by the designer
• No autonomy – all actions are pre-determined
• Even with learning, need a long time to learn the table entries
• Types of tables
– Rule based, Neural networks, etc

21. Simple Reflex Agents

22. Model-based Reflex Agents
34

23. Goal-based Agents
36

24. State-based Models (Search, Planning)
– Solutions are defined as a sequenceof steps
– Model the task as a graph of statesand solution as a
path
– A state captures all the relevant information about the past
in order to act (optimally) in the future
Applications: Navigation, Games
– State-space graphs

25. Logic-based Models (Logic)
– Implicit representation of classes of objects
– Deductive reasoning
Applications: Question answering systems, natural
language understanding
– Propositional logic, First-order logic

26. References
• Tom Markiewicz & Josh Zheng, Getting started with Artificial Intelligence,
Published by O’Reilly Media,2017
• Stuart J. Russell and Peter Norvig, Artificial Intelligence A Modern Approach