Click to see the opening montage.

1946: ENIAC heralds the dawn of Computing

I propose to consider the question:

“Can machines think?”

--Alan Turing, 1950

1950: Turing asks the question….

1956: A new field is born

We propose that a 2 month, 10

man study of artificial

intelligence be carried out

during the summer of 1956 at

Dartmouth College in Hanover,

New Hampshire.

- Dartmouth AI Project

Proposal; J. McCarthy et al.;

Aug. 31, 1955.

1996: EQP proves that

Robbin’s Algebras are all boolean

[An Argonne lab program] has come up with a major mathematical

proof that would have been called creative if a human had thought of it.

-New York Times, December, 1996

----- EQP 0.9, June 1996 -----

The job began on eyas09.mcs.anl.gov, Wed Oct 2 12:25:37 1996

UNIT CONFLICT from 17666 and 2 at 678232.20 seconds.

---------------- PROOF ----------------

2 (wt=7) [] -(n(x + y) = n(x)).

3 (wt=13) [] n(n(n(x) + y) + n(x + y)) = y.

5 (wt=18) [para(3,3)] n(n(n(x + y) + n(x) + y) + y) = n(x + y).

6 (wt=19) [para(3,3)] n(n(n(n(x) + y) + x + y) + y) = n(n(x) + y).

…….

17666 (wt=33) [para(24,16426),demod([17547])] n(n(n(x) + x) ….

1997: HAL 9000 becomes operational

in fictional Urbana, Illinois

…by now, every intelligent person knew that

H-A-L is derived from Heuristic ALgorithmic

-Dr. Chandra, 2010: Odyssey Two

1997: Deep Blue ends Human

Supremacy in Chess

I could feel human-level intelligence across the room

-Gary Kasparov, World Chess Champion (human)

vs.

In a few years, even a single victory

in a long series of games would be the triumph of human genius.

For two days in May, 1999, an AI Program called Remote Agent

autonomously ran Deep Space 1 (some 60,000,000 miles from earth)

Real-time Execution

Adaptive Control

HardwareS

cripted

Ex

ecutiv

e

Generative

Planner &

Scheduler

Generative

Mode Identification

& Recovery

Scripts

Mission-levelactions &resources

component models

ESL

Monitors

GoalsGoals

1999: Remote Agent takes

Deep Space 1 on a galactic ride

2002: Computers start passing

Advanced Placement Tests

… a project funded by

(Microsoft Co-founder) Paul

Allen attempts to design a

“Digital Aristotle”.

Its first results involve

programs that can pass High

School Advanced Placement

Exam in Chemistry…

2005: Cars Drive Themselves

Stanley and three other cars drive themselves over a 132 mile mountain road

2005: Robots play soccer

(without headbutting!)

2005 Robot Soccer:

Humanoid league

2006: AI Celebrates its Golden Jubilee…

1956: A new field is born

We propose that a 2 month, 10

man study of artificial

intelligence be carried out

during the summer of 1956 at

Dartmouth College in Hanover,

New Hampshire.

- Dartmouth AI Project

Proposal; J. McCarthy et al.;

Aug. 31, 1955.

2007: Robots Drive on Urban Roads

11 cars drove themselves on urban streets (for DARPA Urban Challenge)

2010: Watson defeats Puny Humans in

Jeopardy!

And Ken Jennings pledges obeisance to the new Computer Overlords..

2014: Robots (instead of them foreigners)

Threaten to Take all your jobs

Winding Our Way

Down To Wall-E:

Adventures in

Artificial Intelligence

Agenda

• What is AI

• AI’s Successes and Expectations

• What is involved in doing AI

• Some ongoing projects in my lab

• Your questions?

Clicking on the

Graphic takes you

To the right webpage

What if we are writing intelligent

agents that interact with humans?

The COG project

The Robotic care givers

Mechanical flight

became possible

only when people

decided to stop

emulating birds…

Open only for Humans; Droids and Robots should go for CSE 462 next door ;-)

Do we want a machine that beats humans in chess or a machine that thinks like humans

while beating humans in chess?

DeepBlue supposedly DOESN’T think like humans..

(But what if the machine is trying to “tutor” humans about how to do things?)

(Bi-directional flow between thinking humanly and thinking rationally)

Default Position Useful for teaming with humans

Useful for tutoring systems

(a form of teaming)

Agenda

• What is AI

• AI’s Successes and Expectations

• What is involved in doing AI

• Some ongoing projects in my lab

• Your questions?

What AI can do is as important as

what it can’t yet do..

• Captcha project

Agenda

• What is AI

• AI’s Successes and Expectations

• What is involved in doing AI

• Some ongoing projects in my lab

• Your questions?

What Makes Agent Design Hard?

A: A Unified Brand-name-Free Introduction to Planning Subbarao Kambhampati

Environment

What action next?

A: A Unified Brand-name-Free Introduction to Planning Subbarao Kambhampati

Environment

Goals

(Static vs. Dynamic)

(Observable vs. Partially Observable)

(perfect vs. Imperfect)

(Deterministic vs. Stochastic)

What action next?

(Instantaneous vs. Durative)

(Full vs. Partial satisfaction)

Architectures for Intelligent Agents

Wherein we discuss why do we need representation, reasoning and learning

(Model-based reflex agents)

How do we write agent programs for these?

This one already assumes that the “sensorsfeatures” mapping has been done!

EXPLICIT MODELS OF THE ENVIRONMENT

--Blackbox models

--Factored models

Logical models

Probabilistic models

(aka Model-based Reflex Agents)

It is not always obvious what action to do now given a set of goals

You woke up in the morning. You want to attend a class. What should your action be?

Search (Find a path from the current state to goal state; execute the first op)

Planning (does the same for structured—non-blackbox state models)

State Estimation

Planning

Representation Mechanisms:

Logic (propositional; first order)

Probabilistic logic

Learning

the models

Search

Blind, Informed

Planning

Inference

Logical resolution

Bayesian inference

How the course topics stack up…

Learning

Dimensions:

What can be learned?

--Any of the boxes representing

the agent’s knowledge

--action description, effect probabilities,

causal relations in the world (and the

probabilities of causation), utility models

(sort of through credit assignment), sensor

data interpretation models

What feedback is available?

--Supervised, unsupervised,

“reinforcement” learning

--Credit assignment problem

What prior knowledge is available?

-- “Tabularasa” (agent’s head is a blank

slate) or pre-existing knowledge

Agenda

• What is AI

• AI’s Successes and Expectations

• What is involved in doing AI

• Some ongoing projects in my lab

• Your questions?

Planning for Human-Robot Teaming

Crowd-sourced planning

Event-analytics

Human-Robot Teaming

59

Search and report (rescue)

Goals incoming on the go

World is evolving

Model is changing

Infer instructions from

Natural Language

Determine goal formulation

through clarifications and

questions

Click for youtube version

Click for youtube version

Teach Me How To Work:

Natural Language Model Updates

Undergraduate

Student Summer

Project

Crowd-Sourced Planning

Yochan lab, Arizona State University

manhattan_gettingto

62

AI-MIX: Crowd Sourced Planning

AI-MIX (Automated Improvement of Mixed Initiative eXperiences)

Commanders

Goal & event generation

A sub-system of RADAR

63

AI-MIX: Crowd Sourced Planning

Force Structure (PDDL) • Reduces flexibility

Extract Structure • Plans from textual descriptions rather than actions

Interpretation

Steering (Model-lite)

Constraint Checking • Quantitative constraints

Constructive Critiques • Actively help creation and refinement of a plan:

suggesting new plan fragments, new ways of decomposing the current plan or set of goals

Winner of the "People's Choice Award" for the best demo at ICAPS 2014!

A sub-system of RADAR

Since the dawn of civilization, people congregated

in town squares to discuss events

The emergence of social media has now created a sprawling virtual town square,

whose scope is vast, and whose chatter can be captured!

opening exciting possibilities for analyzing what people are actually saying..

Which part of the event did a

tweet refer to?

What’s the relation between

event and tweets?

ET-LDA [AAAI’12, ICWSM’12, MMW’12]

Specific

Specific

Specific

General

Specific

General

Specific

General

General

Event Tweets

Determine tweet type

C(t)~Bernoulli(λ)

Determine which

segment a tweet (word)

refers to

S(t) ~ Categorical(γ)

Determine word’s

topic in event

Zs~multinomial(θ)

Tweets word’s topic

Zt~multinomial(ψ) or

Zt~multinomial(θ)

ET-LDA [AAAI’12, ICWSM’12, MMW’12]

Frequency of specific tweets

Event-tweets alignment

Evolution of specific tweets

Specific

Specific

Specific

General

Specific

General

Specific

General

General

SocSent [IJCAI’13]

ET-LDA & SocSent for Event sensemaking

DeMA for Event recognition

Alice for Event engagement prediction

Eventics, automated toolbox to conduct

in-depth analysis of 3 core tasks in

event analytics

How people respond to events on Twitter

What factors affect crowd’s engagement in events

Our toolbox enables a richer

perspective about

Summary & Additional

Resources

• Talked about

– What AI is

– AI’s Successes and Expectations

– What is involved in doing AI

– Some ongoing projects in my lab

– Your questions?

Agenda

• What is AI

• AI’s Successes and Expectations

• What is involved in doing AI

• Some ongoing projects in my lab

• Your questions?

Questions Submitted

• What policy, if any, has been created surrounding this new and developing

technology? Is there any work being done to use AI to improve human

cognition and performance?

• What are ways of combating the existential risks that are put forth by the

development of AI?

• Do you believe that there will ever be functioning domestic humanoid robots

for retail for the general population? (Not just Roombas, bur actual human

looking and functioning bots, or would there too much of an ethical debate on

if it is human?)

• Could we give AI the feeling of curiosity, leading them to have desires for

physical things?

• Yes!

– But this is not going to

be just a question of

hardware

– The robots need to

track the

beliefs/desires/intentio

ns of the humans

• ..and thus our work on

Human-Robot teaming..

• Feeling of Curiosity—

– Yes

– Exploration/Exploitatio

n tradeoff in

Reinforcement

Learning

• Desires for physical

things..

– Hmm..

Questions Submitted

• What policy, if any, has been created surrounding this new and developing

technology? Is there any work being done to use AI to improve human

cognition and performance?

• What are ways of combating the existential risks that are put forth by the

development of AI?

• Do you believe that there will ever be functioning domestic humanoid robots

for retail for the general population? (Not just Roombas, bur actual human

looking and functioning bots, or would there too much of an ethical debate on

if it is human?)

• Could we give AI the feeling of curiosity, leading them to have desires for

physical things?

Summary & Additional

Resources

• Talked about

– What AI is

– AI’s Successes and Expectations

– What is involved in doing AI

– Some ongoing projects in my lab

– Your questions?