This presentation discusses rational agents and rationality. It defines rationality as acting reasonably and with good judgment to achieve goals. A rational agent selects actions that are expected to maximize its performance given its percepts and prior knowledge. Rational agents use functions to map percept sequences to actions. Examples discussed include a delivery robot, diagnostic system, trading agent, and tutoring system. The presentation also covers the key components of agents, including performance measures, environments, actuators, sensors, and the PEAS framework. It concludes that building more autonomous rational agents with enhanced learning abilities can satisfy the growing demands of artificial intelligence.

1. SCHOOL OF COMPUTER SCINCE
PRESENTATION ON
RATIONALITY AND RATIONAL AGENTS
PRESENTED BY:-
GANGAM SHIVA SANDEEP
REDDY
(2021MCA38)

2. The Concept Of Rationality
• Arationalagentisonethatdoestheright thing.
• Everyentryinthetableisfilledoutcorrectly.
• Whatistherightthing?
• Approximation: themostsuccessfulagent
• Measureofsuccess?
• Performancemeasureshouldbeobjective
• E.g.theamountofdirtcleanedwithinacertaintime.
• E.g.howcleantheflooris.
• Performance measureaccordingtowhatiswantedinthe
environmentinsteadof howtheagentsshouldbehave.

3. RATIONALITY
 Status of being reasonable , sensible and having
good sense of judgement.
 Rationality is concern with expected action and
results depending upon what the agent has
perceived.
 Performing actions with the aim of obtaining useful
information .
 A rational agent always perform right action .
 Good at problem solving.
 The performance measure is determined by the
degree of success.

4. RATIONALITY FUNCTIONS
Rationality depends on four functions
• The performance Rationality
measure that defines the criterion of
success.
• The agent's prior knowledge of the
environment.
• The actions that the agent can
perform.
• The agent's percept sequence to
date.
 Rational Agents use for Game theory
and Decision theory for various real-
world scenarios.

5. Rationality Eg;

6. Decision Making Example:

7. Input to an Agent
• Abilities — the set of possible actions it can perform
Goals/Preferences — what it wants, its desires, its values,.
• Prior Knowledge — what it comes into being knowing,
what it doesn’t get from experience, . . . • History of stimuli
— what it receives from environment now (observations,
percepts)
• past experiences — what it has received in the past

8. Examples of Rational
Autonomous delivery robot roams around an office environment
and delivers coffee, parcels, . . .
• Diagnostic assistant helps a human troubleshoot problems and
suggests repairs or treatments. E. g. , electrical problems,
medical diagnosis.
• Intelligent tutoring system teaches students in some subject
area.
• Trading agent buys goods and services on your behalf.

9. Example Responses Delivery Robot •
• What does the Delivery Robot need to do? ?
• Abilities: movement, speech, pickup and place objects.
• Prior knowledge: its capabilities, objects it may encounter, maps.
• Past experience: which actions are useful and when, what objects are there, how its
actions affect its position.
• Goals: what it needs to deliver and when, tradeoffs between acting quickly and acting
safely.
• Observations: about its environment from cameras, sonar, sound, laser range finders, or
keyboards.

10. Abilities: recommends fixes, ask questions.
Prior knowledge: how switches and lights work, how malfunctions manifest themselves, what
information tests provide, the side effects of repairs.
• Past experience: the effects of repairs or treatments, the prevalence of faults or diseases.
• Goals: fixing the device and tradeoffs between fixing or replacing different components.
• Observations: symptoms of a device or patient.
Responses Diagnostic System

11. Trading Agent :
• Abilities: acquire information, make recommendations, purchase items.
• Prior knowledge: the ontology of what things are available, where to purchase items, how to
decompose a complex item.
• Past experience: how long special last, how long items take to sell out, who has good deals, what your
competitors do.
• Goals: what the person wants, their tradeoff.
• Observations: what items are available, prices, number in stock,

12. Intelligent Tutoring System
• Abilities: Present information, give tests
• Prior knowledge: subject material, primitive strategies
• Past experience: common errors, effects of teaching strategies
• Goals: the students should master subject material, gain social skills,
study skills, inquisitiveness, interest
Observations: test results, facial expressions, questions, what the student
is concentrating on

13. Rational Agent
⦿ AI is about building rational agents.
⦿ An agent is something that perceives and
acts.
⦿ A rational agent always does the right
thing as-
What are the Functionalities ?(Goals)
What are the components?
How do we build them?

14. Rational Agent:
For each possible percept sequence, a rational agent
should select an action (using an agent function) that
is expected to maximize its performance measure,
given the evidence provided by the percept sequence
and whatever built-in prior knowledge the agent has.
A percept sequence is the complete history of
anything the agent has ever perceived.
A performance measure is a means of calculating how
well the agent has performed based on the sequence
of percepts that it has received.
 An agent’s prior knowledge of the environment is the
knowledge that the agent designer has given to the
agent before its introduction to the environment.
 AI is creating rational agents to use for Game Theory and
Decision theory for various real world scenarios.

15. Vaccum Cleaner:

16. Rational Agent:
An agent function maps percept sequences to actions.
f : seq(P) A
Agent function for vacuum Cleaner example:

18. What is Ideal Rational Agent?
 “For Each possible Percept sequence a
rational agent should select an action that
expected maximize the performance
measures given evidence provided by
percept sequence and whatever built in
knowledge”

19. Task Environments
Performance Measures used to evaluate how well an agent solves the task at hand
eg: Safe, fast,legal, comfortable trip max profits
Environment surroundings beyond the control of the agent
eg: Roads , other trafic , pedestrians, customers
Actuators used by the agent to perform actions
Eg: Steering wheel, accelerator, brake,signal, horn
Sensors provide information about current state of Env
Eg: Cameras, sonar, speedometer, GPS , odometer,
keyboard , enginde sensor

20. Omniscience, Learning, Autonomy
• 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)
• A rational agent should not only gather information, but also learn as
much as possible from what it perceives
• An agent is autonomous if its behavior is determined by its own
experience (with ability to learn and adapt). Rational agents should be
autonomous.

21. PEAS

22. PEAS Describtion Of
Automated Taxi:
Performance
Safety, destination, profits, legality, comfort
Environment
Streets/motorways, other traffic, pedestrians, weather
Actuators
Steering, accelerator, brake, horn, speaker/display
Sensors
Video, sonar, speedometer, engine ,sensors, keyboard, GPS

23. Examples :

24. Conclusion
 We Concluding that Rationality agent
is built in with intense to satisfy the
world of AI immense computing
abilities and significant decision
making .
 With Enhanced learning and better
coordinated activities better and
moreintelligent agents would be
made.

25. THANK YOU!!
G.ShivaSandeep
2021MCA38