Course Overview

Agents acting in an environment

Future and Ethics of AI

Dimensions of complexity

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 1

What is Artificial Intelligence?

Artificial Intelligence is the synthesis and analysis ofcomputational agents that act intelligently.

An agent is something that acts in an environment.

An agent acts intelligently if:I its actions are appropriate for its goals and circumstancesI it is flexible to changing environments and goalsI it learns from experienceI it makes appropriate choices given perceptual and

computational limitations

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 2

Agents acting in an environment

Prior Knowledge

Environment

StimuliActions

Past Experiences

Goals/Preferences

Agent

Abilities

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 3

Inside Black Box

Learner Inference Engine

offline online

Prior Knowledge

Past Experiences/Data

ObservationsGoals/Preference

Actions

Abilities

KB

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 4

Controller

memories Controller

percepts commands

Body

memories

Environment

stimuli actionsAgent

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 5

Functions implemented in a controller

memories

percepts commands

memories

For discrete time, a controller implements:

belief state function returns next belief state / memory.What should it remember?

command function returns commands to body.What should it do?

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 6

Future and Ethics of AI

What will super-intelligent AI bring?

I Automation and unemployment? What if people are notlonger needed to make economy work?

I Smart weapons? Automated terrorists?

What will a super-intelligent AI be able to do better?I predict the futureI optimize (constrained optimization)

Whose values/goals will they use? (Why?)

Will we need a new ethics of AI?

Is super-human AI inevitable (wait till computers getfaster)? (Singularity)Is there fundamental research to be done?Is it easy because humans are not as intelligent as we liketo think?

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 7

Future and Ethics of AI

What will super-intelligent AI bring?I Automation and unemployment? What if people are not

longer needed to make economy work?

I Smart weapons? Automated terrorists?

What will a super-intelligent AI be able to do better?I predict the futureI optimize (constrained optimization)

Whose values/goals will they use? (Why?)

Will we need a new ethics of AI?

Is super-human AI inevitable (wait till computers getfaster)? (Singularity)Is there fundamental research to be done?Is it easy because humans are not as intelligent as we liketo think?

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 8

Future and Ethics of AI

What will super-intelligent AI bring?I Automation and unemployment? What if people are not

longer needed to make economy work?I Smart weapons? Automated terrorists?

What will a super-intelligent AI be able to do better?I predict the futureI optimize (constrained optimization)

Whose values/goals will they use? (Why?)

Will we need a new ethics of AI?

Is super-human AI inevitable (wait till computers getfaster)? (Singularity)Is there fundamental research to be done?Is it easy because humans are not as intelligent as we liketo think?

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 9

Future and Ethics of AI

What will super-intelligent AI bring?I Automation and unemployment? What if people are not

longer needed to make economy work?I Smart weapons? Automated terrorists?

What will a super-intelligent AI be able to do better?

I predict the futureI optimize (constrained optimization)

Whose values/goals will they use? (Why?)

Will we need a new ethics of AI?

Is super-human AI inevitable (wait till computers getfaster)? (Singularity)Is there fundamental research to be done?Is it easy because humans are not as intelligent as we liketo think?

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 10

Future and Ethics of AI

What will super-intelligent AI bring?I Automation and unemployment? What if people are not

longer needed to make economy work?I Smart weapons? Automated terrorists?

What will a super-intelligent AI be able to do better?I predict the futureI optimize (constrained optimization)

Whose values/goals will they use? (Why?)

Will we need a new ethics of AI?

Is super-human AI inevitable (wait till computers getfaster)? (Singularity)Is there fundamental research to be done?Is it easy because humans are not as intelligent as we liketo think?

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 11

Future and Ethics of AI

What will super-intelligent AI bring?I Automation and unemployment? What if people are not

longer needed to make economy work?I Smart weapons? Automated terrorists?

What will a super-intelligent AI be able to do better?I predict the futureI optimize (constrained optimization)

Whose values/goals will they use? (Why?)

Will we need a new ethics of AI?

Is super-human AI inevitable (wait till computers getfaster)? (Singularity)Is there fundamental research to be done?Is it easy because humans are not as intelligent as we liketo think?

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 12

Future and Ethics of AI

What will super-intelligent AI bring?I Automation and unemployment? What if people are not

longer needed to make economy work?I Smart weapons? Automated terrorists?

What will a super-intelligent AI be able to do better?I predict the futureI optimize (constrained optimization)

Whose values/goals will they use? (Why?)

Will we need a new ethics of AI?

Is super-human AI inevitable (wait till computers getfaster)? (Singularity)Is there fundamental research to be done?Is it easy because humans are not as intelligent as we liketo think?

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 13

Future and Ethics of AI

What will super-intelligent AI bring?I Automation and unemployment? What if people are not

longer needed to make economy work?I Smart weapons? Automated terrorists?

What will a super-intelligent AI be able to do better?I predict the futureI optimize (constrained optimization)

Whose values/goals will they use? (Why?)

Will we need a new ethics of AI?

Is super-human AI inevitable (wait till computers getfaster)? (Singularity)Is there fundamental research to be done?Is it easy because humans are not as intelligent as we liketo think?

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 14

Dimensions of Complexity

Flat or modular or hierarchical

Explicit states or features or individuals and relations

Static or finite stage or indefinite stage or infinite stage

Fully observable or partially observable

Deterministic or stochastic dynamics

Goals or complex preferences

Single-agent or multiple agents

Knowledge is given or knowledge is learned fromexperience

Reason offline or reason while interacting withenvironment

Perfect rationality or bounded rationality

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 15

State-space Search

flat or modular or hierarchical

explicit states or features or individuals and relations

static or finite stage or indefinite stage or infinite stage

fully observable or partially observable

deterministic or stochastic dynamics

goals or complex preferences

single agent or multiple agents

knowledge is given or knowledge is learned

reason offline or reason while interacting with environment

perfect rationality or bounded rationality

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 16

Classical Planning

flat or modular or hierarchical

explicit states or features or individuals and relations

static or finite stage or indefinite stage or infinite stage

fully observable or partially observable

deterministic or stochastic dynamics

goals or complex preferences

single agent or multiple agents

knowledge is given or knowledge is learned

reason offline or reason while interacting with environment

perfect rationality or bounded rationality

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 17

Decision Networks

flat or modular or hierarchical

explicit states or features or individuals and relations

static or finite stage or indefinite stage or infinite stage

fully observable or partially observable

deterministic or stochastic dynamics

goals or complex preferences

single agent or multiple agents

knowledge is given or knowledge is learned

reason offline or reason while interacting with environment

perfect rationality or bounded rationality

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 18

Markov Decision Processes (MDPs)

flat or modular or hierarchical

explicit states or features or individuals and relations

static or finite stage or indefinite stage or infinite stage

fully observable or partially observable

deterministic or stochastic dynamics

goals or complex preferences

single agent or multiple agents

knowledge is given or knowledge is learned

reason offline or reason while interacting with environment

perfect rationality or bounded rationality

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 19

Decision-theoretic Planning

flat or modular or hierarchical

explicit states or features or individuals and relations

static or finite stage or indefinite stage or infinite stage

fully observable or partially observable

deterministic or stochastic dynamics

goals or complex preferences

single agent or multiple agents

knowledge is given or knowledge is learned

reason offline or reason while interacting with environment

perfect rationality or bounded rationality

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 20

Reinforcement Learning

flat or modular or hierarchical

explicit states or features or individuals and relations

static or finite stage or indefinite stage or infinite stage

fully observable or partially observable

deterministic or stochastic dynamics

goals or complex preferences

single agent or multiple agents

knowledge is given or knowledge is learned

reason offline or reason while interacting with environment

perfect rationality or bounded rationality

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 21

Relational Reinforcement Learning

flat or modular or hierarchical

explicit states or features or individuals and relations

static or finite stage or indefinite stage or infinite stage

fully observable or partially observable

deterministic or stochastic dynamics

goals or complex preferences

single agent or multiple agents

knowledge is given or knowledge is learned

reason offline or reason while interacting with environment

perfect rationality or bounded rationality

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 22

Classical Game Theory

flat or modular or hierarchical

explicit states or features or individuals and relations

static or finite stage or indefinite stage or infinite stage

fully observable or partially observable

deterministic or stochastic dynamics

goals or complex preferences

single agent or multiple agents

knowledge is given or knowledge is learned

reason offline or reason while interacting with environment

perfect rationality or bounded rationality

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 23

Humans

flat or modular or hierarchical

explicit states or features or individuals and relations

static or finite stage or indefinite stage or infinite stage

fully observable or partially observable

deterministic or stochastic dynamics

goals or complex preferences

single agent or multiple agents

knowledge is given or knowledge is learned

reason offline or reason while interacting with environment

perfect rationality or bounded rationality

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 24

Comparison of Some Representations

CP MDPs IDs RL POMDPs GThierarchical 4

properties 4 4 4

relational 4

indefinite stage 4 4 4 4

stochastic dynamics 4 4 4 4 4

partially observable 4 4 4

values 4 4 4 4 4

dynamics not given 4

multiple agents 4

bounded rationality

c©D. Poole and A. Mackworth 2016 Artificial Intelligence, Lecture 16.1, Page 25