1. How R&N define AI humanly vs. rationally thinking vs. acting
humanly vs. rationally
Systems that think like humans
Systems that think rationally
Systems that act like humans
Systems that act rationally
thinking
vs.
acting
Rational Agents

2. Acting Rationally
“Doing the right thing" … "that which is expected to maximize goal achievement, given the available information." 
Unlike the previous approach (thinking rationally) the process of "acting" rationally doesn't necessarily require "thinking."  (blinking fits in here).
In AI we define things that act rationally as “agents.”

3. Agents “An agent is simply something that acts.”
An agent is an entity that is capable of perceiving its environment (through sensors) and responding appropriately to it (through actuators).

4. Agents
If the agent is intelligent, it should be able to weigh alternatives.
“A rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome.”

5. Agents
An agent should be able to derive new information from data by applying sound logical rules.
It should possess extensive knowledge in the domain where it is expected to solve problems.

6. Agents
We will consider true intelligent, rational, agents as entities which display:
Perception
Persistence
Adaptability
Autonomous control

7. Agents and Environments
Agents include humans, robots, softbots, thermostats, etc.
The agent function maps from percept histories to actions:
The agent program runs on the physical architecture to produce

8. Agents and Environments
Vacuum-Cleaner World (Figure 2.2)

9. Agents and Environments
Vacuum-Cleaner World (Figure 2.3)

10. Agents and Environments
Vacuum-Cleaner World (Figure 2.3)

11. Agents and Environments
Vacuum-Cleaner World

12. Rationality
A rational agent does the right thing.
What is the right thing?
One possibility:
The action that will maximize success.
But what is success?
The action that maximizes the agent’s goals.
Use a performance measure to evaluate agent’s success.
So what would be a good performance measure for the vacuum agent?

13. Rationality
Fixed performance measure evaluates the environment sequence
One point per square cleaned up in time T
One point per clean square per time step, minus one per move?
Penalize for more than k dirty squares?
A rational agent chooses whichever action maximizes the expected value of the performance measure given the percept sequence to date.

14. Rationality
Rational agent definition: “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.”

15. Rationality Rationality is not Rationality implies Omniscience
Clairvoyance
Success
Rationality implies
Exploration
Learning
Autonomy

16. PEAS
To design a rational agent, we must specify the task environment (the “problems” to which rational agents are the “solutions”).
Performance measure
Environment
Actuators
Sensors
Example: the task of designing an automated taxi.

17. PEAS Performance measure?
Environment?
Actuators?
Sensors?
Safety, destination, profits, legality, comfort…
US streets/freeways, traffic, pedestrians, weather…
Steering, accelerator, brake, horn, speaker/display…
Video, accelerometers, gauges, engine sensors, keyboard, GPS, …

18. PEAS - Internet news gathering agent
Scans Internet news sources to pick interesting items for its customers
Performance measure?
Environment?
Actuators?
Sensors?

19. PEAS - Internet news gathering agent
Scans Internet news sources to pick interesting items for its customers
Performance measure?
Environment?
Actuators?
Sensors?

20. Environment Types
We often describe the environment based on six attributes.
Fully/partially observable
Deterministic/stochastic
Episodic/sequential
Static/dynamic
Discrete/continuous
Single agent/multiagent

21. Environment Types Categorization of environment tasks:
Fully/partially observable extent to which an agent’s sensors give it access to the complete state of the environment
Deterministic/stochastic extent to which the next state of the environment is determined by the current state and the current action

22. Environment Types Categorization of environment tasks:
Episodic/sequential extent to which the agent’s experience is divided into atomic episodes
Static/dynamic extent to which the environment can change while the agent is deliberating

23. Environment Types Categorization of environment tasks:
Discrete/continuous extent to which state of the environment, time, percepts and actions of the agent are expressed as a set of discrete values
Single agent/multiagent

24. This is as far as we got

25. Environment Types

26. Environment Types

27. Environment Types

28. Environment Types

29. Environment Types

30. Environment Types

31. Environment Types
The environment type largely determines the agent design
The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent

32. RoboCup
“By the year 2050, develop a team of fully autonomous humanoid robots that can win against the human world soccer champion team. “ (
Develop a PEAS description of the task environment for a RoboCup participant.
Include a thorough classification of the environment using R&N’s six properties of task environments.

33. Agent Types Agent = architecture + program Simple reflex agent
Reflex agent with state
Goal-based agent
Utility-based agent
Learning agent (arguably not a 5th agent but a different model of any of the previous agents).

34. Agent Types
Simple reflex agent

35. Agent Types
Reflex agent with state

36. Agent Types
Goal-based agent

37. Agent Types
Utility-based agent

38. Agent Types
Learning agent