How R&N define AI 

Systems that think like humans

Systems that think rationally

Systems that act like humans

Systems that act rationally

humanly vs. rationally

thinkingvs.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

One more idea before we “start”

• AI goes beyond “normal” CS…

• AI often is working on problems that we know are intractable…– Consider chess

4

One more idea before we “start”

• In the early 1970s someone wrote the following bit of trivia: – If every man, woman, and child on earth were

to spend every waking moment playing chess (16 hours per day) at the rate of one game per minute, it would take 146 billion years to use every variation of the first 10 moves.

5

One more idea before we “start”

• Given this complexity, how the heck can humans EVER hope to play chess well???

• We don't often arrive at the best solutions, but we usually do arrive at solutions that are good enough.

• This idea of satisficing rather than optimizing when confronted with an intractable problem is central to what AI is about.

6

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).

7

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.”

8

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.

9

Agents

• We will consider true intelligent, rational, agents as entities which display:– Perception

– Persistence

– Adaptability

– Autonomous control

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

Agents and Environments

• Vacuum-Cleaner World (Figure 2.2)

Agents and Environments

• Vacuum-Cleaner World (Figure 2.3)

Agents and Environments

• Vacuum-Cleaner World (Figure 2.3)

Agents and Environments

• Vacuum-Cleaner World

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?

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.

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.”

Rationality• Rationality is not

– Omniscience

– Clairvoyance

– Success

• Rationality implies– Exploration

– Learning

– Autonomy

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.

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, …

PEAS - Internet news gathering agent

Scans Internet news sources to pick interesting items for its customers

• Performance measure?

• Environment?

• Actuators?

• Sensors?

PEAS - Internet news gathering agent

Scans Internet news sources to pick interesting items for its customers

• Performance measure?

• Environment?

• Actuators?

• Sensors?

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

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/stochasticextent to which the next state of the environment is determined by the current state and the current action

Environment Types

• Categorization of environment tasks:– Episodic/sequential

extent to which the agent’s experience is divided into atomic episodes

– Static/dynamicextent to which the environment can change while the agent is deliberating

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

Environment Types

Environment Types

Environment Types

Environment Types

Environment Types

Environment Types

Environment Types

Environment Types

• The environment type largely determines the agent design

• The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent

RoboCup

• “By the year 2050, develop a team of fully autonomous humanoid robots that can win against the human world soccer champion team. “ (www.robocup.org).

• 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.

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).

Agent Types

• Simple reflex agent

Agent Types

• Reflex agent with state

Agent Types

• Goal-based agent

Agent Types

• Utility-based agent

Agent Types

• Learning agent