CS 380: ARTIFICIAL INTELLIGENCE

Transcription

1 CS 380: ARTIFICIAL INTELLIGENCE RATIONAL AGENTS 9/25/2013 Santiago Ontañón

2 Do you think a machine can be made that replicates human intelligence?

3 Outline What is an Agent? Rationality Agents and Environments Agent Types (these slides are adapted from Russel & Norvig s official slides)

4 Outline What is an Agent? Rationality Agents and Environments Agent Types (these slides are adapted from Russel & Norvig s official slides)

5 What is an Agent? Agent: autonomous entity which observes and acts upon an environment and directs its activity towards achieving goals. Example agents: Humans Robots Software agents ATMs etc.

6 What is an Agent? sensors environment percepts actions? agent actuators Theoretically: f : P! A In practice: the agent runs on a physical system (e.g. a computer) to produce f

7 What is an Agent? sensors environment percepts actions? agent Theoretically: actuators f : P! A Maps sequences of percepts to actions In practice: the agent runs on a physical system (e.g. a computer) to produce f

8 Example: Vacuum Cleaner Room A Room B Percepts: Location sensor and Dirt sensor Actions: Left, Right, Suck, Wait

9 Example: Vacuum Cleaner Room A Room B [A, Clean] Percepts: Location sensor and Dirt sensor Actions: Left, Right, Suck, Wait

10 Example: Vacuum Cleaner Percept sequence Action [A, Clean] Right [A, Dirty] Suck [B, Clean] Left [B, Dirty] Suck [A, Clean], [A, Clean] Right [A, Clean], [A, Dirty]. 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

11 Example: Vacuum Cleaner Percept sequence Action [A, Clean] Right [A, Dirty] Suck [B, Clean] Left [B, Dirty] Suck [A, Clean], [A, Clean] Right [A, Clean], [A, Dirty]. Suck. Two questions: 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 function? Can it be implemented as a computer program? (How?)

12 Outline What is an Agent? Rationality Agents and Environments Agent Types (these slides are adapted from Russel & Norvig s official slides)

13 Rationality As we discussed last class, intelligence is an ill-defined concept. Let us assume a performance measure, for example: $1 per room cleaned $10 per room cleaned minus $1 per each movement etc. Rational agent: Chooses actions that maximize the expected value of the performance measure given the current percept sequence.

14 Rationality As we discussed last class, intelligence is an ill-defined concept. Let us assume a performance measure, for example: $1 per room cleaned $10 per room cleaned minus $1 per each movement etc. Rational agent: Performance measure is given (external) to the agent. Chooses actions that maximize the expected value of the performance measure given the current percept sequence.

15 Rationality Imagine two agents A and B whose performance measure is defined by how many $ they make at the casino. A and B play one game of Black Jack A plays the move that theoretically has higher chances of winning B plays a different, more risky move B ends up winning Which agent is rational?

16 Rationality Imagine two agents A and B whose performance measure is defined by how many $ they make at the casino. A and B play one game of Black Jack A plays the move that theoretically has higher chances of winning B plays a different, more risky move B ends up winning Which agent is rational? A is the rational agent Rationality does not imply clairvoyance Rationality does not imply omniscient Thus, rational does not mean successful

17 Outline What is an Agent? Rationality Agents and Environments Agent Types (these slides are adapted from Russel & Norvig s official slides)

18 How do we Design a Rational Agent? To design a rational agent, we first need to define its environment, performance measure, etc. PEAS: Performance Environment Actions Sensors (percepts)

19 Example (informal): Automated Taxi Performance: Environment: Actions: Sensors:

20 Example (informal): Automated Taxi Performance: profit, safety, legality, Environment: streets, pedestrians, cars, weather, traffic lights/signs, Actions: steering, throttle, break, horn, display, Sensors: GPS, video, sonar, keyboard, accelerometers, microphones,

21 Example: Chess Agent Performance: Environment: Actions: Sensors:

22 Example: Chess Agent Performance: Environment: Chess board, and opponent. Actions: Sensors:

23 Example: Chess Agent Performance: Once reached a terminal state (draw, win): -1 for losing, 0 for draw, 1 for winning. Environment: Chess board, and opponent. Actions: Sensors:

24 Example: Chess Agent Performance: Once reached a terminal state (draw, win): -1 for losing, 0 for draw, 1 for winning. Environment: Chess board, and opponent. Actions: Pairs of coordinates: (x1,y1) à (x2, y2). Specify the source piece and the target position (for castling, the king s move is specified). Sensors:

25 Example: Chess Agent Performance: Once reached a terminal state (draw, win): -1 for losing, 0 for draw, 1 for winning. Environment: Chess board, and opponent. Actions: Pairs of coordinates: (x1,y1) à (x2, y2). Specify the source piece and the target position (for castling, the king s move is specified). Sensors: Board perceived as a 8x8 matrix. Each element in the matrix can take one of the following values: E, BP, WP, BB, WB, BK, WN, BR, WN, BQ, WK, BK, WK

26 Example: Chess Agent Performance: Once reached a terminal state (draw, win): -1 for losing, 0 for draw, 1 for winning. Environment: Chess board, and opponent. The environment might have lots of features (piece size and material, opponent s facial expression, etc.). When designing a rational agent, we only need to provide sensors for the Actions: Pairs of coordinates: (x1,y1) information à (x2, that y2). is necessary Specify to the source piece and the target position (for castling, the performance measure) king s move is specified). Sensors: Board perceived as a 8x8 matrix. Each element in the matrix can take one of the following values: E, BP, WP, BB, WB, BK, WN, BR, WN, BQ, WK, BK, WK achieve the agent s goals (maximize

27 Example: Chess Agent WR WN WB WK WQ WB WN WR WP WP WP WP WP WP WP WP BP BP BP BP BP BP BP BP BR BN BB BK BQ BB BN BR Environment Percept

28 Environment Types Observable Deterministic Sequential Static Discrete Single-Agent Solitaire Chess Backgammon Taxi StarCraft Real Life

29 Environment Types Observable Deterministic Sequential Static Discrete Single-Agent Solitaire No Yes Yes Yes Yes Yes Chess Backgammon Taxi StarCraft Real Life

30 Environment Types Observable Deterministic Sequential Static Discrete Single-Agent Solitaire No Yes Yes Yes Yes Yes Chess Yes Yes Yes Yes Yes No Backgammon Taxi StarCraft Real Life

31 Environment Types Observable Deterministic Sequential Static Discrete Single-Agent Solitaire No Yes Yes Yes Yes Yes Chess Yes Yes Yes Yes Yes No Backgammon Yes No Yes Yes Yes No Taxi StarCraft Real Life

32 Environment Types Observable Deterministic Sequential Static Discrete Single-Agent Solitaire No Yes Yes Yes Yes Yes Chess Yes Yes Yes Yes Yes No Backgammon Yes No Yes Yes Yes No Taxi No No Yes No No No StarCraft Real Life

33 Environment Types Observable Deterministic Sequential Static Discrete Single-Agent Solitaire No Yes Yes Yes Yes Yes Chess Yes Yes Yes Yes Yes No Backgammon Yes No Yes Yes Yes No Taxi No No Yes No No No StarCraft No Almost Yes No Yes No Real Life No No (?) Yes No No (?) No

34 Outline What is an Agent? Rationality Agents and Environments Agent Types (these slides are adapted from Russel & Norvig s official slides)

35 Agent Types We will cover 4 basic types: Reactive agents (reflex agents) State-based agents Goal-based agents Utility-based agents They can all incorporate learning

36 Reactive Agents Agent Sensors Condition action rules What the world is like now What action I should do now Environment Actuators

37 State-based Agents State How the world evolves What my actions do Condition action rules Sensors What the world is like now What action I should do now Environment Agent Actuators

38 Goal-based Agents State How the world evolves What my actions do Goals Sensors What the world is like now What it will be like if I do action A What action I should do now Environment Agent Actuators

39 Utility-based Agents State How the world evolves What my actions do Utility Sensors What the world is like now What it will be like if I do action A How happy I will be in such a state What action I should do now Environment Agent Actuators

40 Learning Agents Performance standard Critic Sensors feedback learning goals Learning element changes knowledge Performance element Environment Problem generator Agent Actuators

41 Summary Agents: Interact with environments via actuators (actions) and sensors (percepts) Rational agent: maximizes expected performance PEAS Environment types Basic agent architectures