Inteligent Robots and Knowledge Representation Intelligent Agents In which we discuss the nature of agents, the diversity of environments, and the resulting menagerie of agent types.
Dr. Igor Trajkovski
Dr. Igor Trajkovski
1
Outline ♦ Agents and environments ♦ Rationality ♦ PEAS (Performance measure, Environment, Actuators, Sensors) ♦ Environment types ♦ Agent types
Dr. Igor Trajkovski
2
Agents and environments sensors percepts ?
environment actions
agent
actuators
Agents include humans, robots, softbots, thermostats, etc. The agent function maps from percept histories to actions: f : P∗ → A The agent program runs on the physical architecture to produce f
Dr. Igor Trajkovski
3
Vacuum-cleaner world
A
B
Percepts: location and contents, e.g., [A, Dirty] Actions: Lef t, Right, Suck, N oOp
Dr. Igor Trajkovski
4
A vacuum-cleaner agent Percept sequence [A, Clean] [A, Dirty] [B, Clean] [B, Dirty] [A, Clean], [A, Clean] [A, Clean], [A, Dirty] ..
Action Right Suck Lef t Suck Right Suck ..
function Reflex-Vacuum-Agent( [location,status]) returns an action if status = Dirty then return Suck else if location = A then return Right else if location = B then return Left
What is the right function? Can it be implemented in a small agent program? Dr. Igor Trajkovski
5
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 > k dirty squares? A rational agent chooses whichever action maximizes the expected value of the performance measure given the percept sequence to date Rational 6= all-knowing – percepts may not supply all relevant information Rational 6= foreseeing the future – action outcomes may not be as expected Hence, rational 6= successful Rational ⇒ exploration, learning, autonomy
Dr. Igor Trajkovski
6
Rationality (cont.) 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 environment. • The actions that the agent can perform. • The agent’s percept sequence to date.
Dr. Igor Trajkovski
7
PEAS To design a rational agent, we must specify the task environment Consider, e.g., the task of designing an automated taxi: Performance measure?? Environment?? Actuators?? Sensors??
Dr. Igor Trajkovski
8
PEAS To design a rational agent, we must specify the task environment Consider, e.g., the task of designing an automated taxi: Performance measure?? safety, destination, profits, legality, comfort, . . . Environment?? US streets/freeways, traffic, pedestrians, weather, . . . Actuators?? steering, accelerator, brake, horn, speaker/display, . . . Sensors?? video, accelerometers, gauges, engine sensors, keyboard, GPS, . . .
Dr. Igor Trajkovski
9
Internet shopping agent Performance measure?? Environment?? Actuators?? Sensors??
Dr. Igor Trajkovski
10
Internet shopping agent Performance measure?? price, quality, appropriateness, efficiency Environment?? current and future WWW sites, vendors, shippers Actuators?? display to user, follow URL, fill in form Sensors?? HTML pages (text, graphics, scripts)
Dr. Igor Trajkovski
11
Environment types The range of task environments that might arise in AI is obviously vast. We can, however, identify a fairly small number of dimensions along which task environments can be categorized. • Fully observable vs. partially observable • Deterministic vs. stochastic • Episodic vs. sequential • Static vs. dynamic • Discrete vs. continuous. • Single agent vs. multiagent.
Dr. Igor Trajkovski
12
Environment types (cont.) Solitaire
Backgammon
Internet shopping
Taxi
Observable?? Deterministic?? Episodic?? Static?? Discrete?? Single-agent??
Dr. Igor Trajkovski
13
Environment types Observable?? Deterministic?? Episodic?? Static?? Discrete?? Single-agent??
Solitaire Yes
Backgammon Yes
Internet shopping No
Taxi No
Dr. Igor Trajkovski
14
Environment types Observable?? Deterministic?? Episodic?? Static?? Discrete?? Single-agent??
Solitaire Yes Yes
Backgammon Yes No
Internet shopping No Partly
Taxi No No
Dr. Igor Trajkovski
15
Environment types Observable?? Deterministic?? Episodic?? Static?? Discrete?? Single-agent??
Solitaire Yes Yes No
Backgammon Yes No No
Internet shopping No Partly No
Taxi No No No
Dr. Igor Trajkovski
16
Environment types Observable?? Deterministic?? Episodic?? Static?? Discrete?? Single-agent??
Solitaire Yes Yes No Yes
Backgammon Yes No No Semi
Internet shopping No Partly No Semi
Taxi No No No No
Dr. Igor Trajkovski
17
Environment types Observable?? Deterministic?? Episodic?? Static?? Discrete?? Single-agent??
Solitaire Yes Yes No Yes Yes
Backgammon Yes No No Semi Yes
Internet shopping No Partly No Semi Yes
Taxi No No No No No
Dr. Igor Trajkovski
18
Environment types Observable?? Deterministic?? Episodic?? Static?? Discrete?? Single-agent??
Solitaire Backgammon Internet shopping Taxi Yes Yes No No Yes No Partly No No No No No Yes Semi Semi No Yes Yes Yes No Yes No Yes (except auctions) No
The environment type largely determines the agent design The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent
Dr. Igor Trajkovski
19
Agents The job of AI is to design the agent program that implements the agent function mapping percepts to actions. We assume this program will run on some sort of computing device with physical sensors and actuators-we call this the architecture: agent = architecture + program
Dr. Igor Trajkovski
20
Table driven agents 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
• Consider the automated taxi: the visual input from a single camera comes in at the rate of roughly 27 megabytes per second (30 frames per second, 640 x 480 pixels with 24 bits of color information). This gives a lookup table with over 10250,000,000,000 entries for an hour’s driving. • Even the lookup table for chess - a tiny, well-behaved fragment of the real world-would have at least 10150 entries.
Dr. Igor Trajkovski
21
Table driven agents - problems The daunting size of these tables (the number of atoms in the observable universe is less than 1080) means that • no physical agent in this universe will have the space to store the table • the designer would not have time to create the table • no agent could ever learn all the right table entries from its experience • even if the environment is simple enough to yield a feasible table size, the designer still has no guidance about how to fill in the table entries
Dr. Igor Trajkovski
22
Agent types Four basic types in order of increasing generality: – simple reflex agents – reflex agents with state – goal-based agents – utility-based agents All these can be turned into learning agents
Dr. Igor Trajkovski
23
Simple reflex agents Agent
Sensors
Condition−action rules
What action I should do now
Environment
What the world is like now
Actuators
Dr. Igor Trajkovski
24
Example function Reflex-Vacuum-Agent( [location,status]) returns an action if status = Dirty then return Suck else if location = A then return Right else if location = B then return Left
or if car-in-front-is-braking then initiate-braking
Dr. Igor Trajkovski
25
Reflex agents with state Sensors State How the world evolves
What my actions do
Condition−action rules
Agent
What action I should do now
Environment
What the world is like now
Actuators
Dr. Igor Trajkovski
26
Example function Reflex-Vacuum-Agent( [location,status]) returns an action static: last A, last B, numbers, initially ∞ if status = Dirty then . . .
Dr. Igor Trajkovski
27
Goal-based agents Sensors State What the world is like now
What my actions do
What it will be like if I do action A
Goals
What action I should do now
Agent
Environment
How the world evolves
Actuators
Dr. Igor Trajkovski
28
Utility-based agents Sensors State What the world is like now
What my actions do
What it will be like if I do action A
Utility
How happy I will be in such a state What action I should do now
Agent
Environment
How the world evolves
Actuators
Dr. Igor Trajkovski
29
Learning agents Performance standard
Sensors
Critic
changes Learning element
knowledge
Performance element
learning goals
Environment
feedback
Problem generator
Agent
Actuators
Dr. Igor Trajkovski
30
Summary Agents interact with environments through actuators and sensors The agent function describes what the agent does in all circumstances The performance measure evaluates the environment sequence A perfectly rational agent maximizes expected performance Agent programs implement (some) agent functions PEAS descriptions define task environments Environments are categorized along several dimensions: observable? deterministic? episodic? static? discrete? single-agent? Several basic agent architectures exist: reflex, reflex with state, goal-based, utility-based
Dr. Igor Trajkovski
31